diff --git a/Claude/10 Claude features that 90% of users ignore.md b/Claude/10 Claude features that 90% of users ignore.md
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+---
+title: "10 Claude features that 90% of users ignore"
+source: "https://x.com/0xbobaaa/status/2044475727030984900"
+author:
+ - "[[@0xbobaaa]]"
+published: 2026-04-16
+created: 2026-04-26
+description: "that’s “how to not waste gas” level advice. i want to talk about something different - how to actually drive faster.if you already use Clau..."
+tags:
+ - "clippings"
+---
+
+
+that’s “how to not waste gas” level advice. i want to talk about something different - how to actually drive faster. if you already use Claude daily for content, research or code here are 10 things that turn it from a chatbot into a full-blown work tool. most of them are free.
+
+## 1\. projects - a separate workspace for every task
+
+
+
+most people open a new chat, dump context, explain who they are and what they need. every single time from scratch. projects fix this. it’s a folder with persistent memory: you upload files, write custom instructions, and every new chat inside the project already knows the full context.
+
+here’s what it looks like in practice. you create a project called “X content”. in the instructions you write: what post style, what tone of voice, what data to use, what words are banned. you upload examples of your best posts. done every new chat in this project starts not from zero, but with full understanding of the task.
+
+i keep separate projects for different workflows: one for posts, another for research, a third for technical work. Claude behaves differently in each because the instructions are different.
+
+projects used to be paid-only. since march 2026 they’re free for everyone.
+
+## 2\. memory - set it once, use it forever
+
+
+
+memory is Claude’s long-term recall that works across chats. Claude automatically remembers key things: who you are, what you do, how you prefer to receive answers. but beyond the automatic stuff, you can directly tell Claude what to remember. for example: “remember that i write content for crypto audiences, prefer short paragraphs and hate phrases like ‘in the world of cryptocurrency’ and ‘let’s dive in’”. set it up once and Claude never again opens with “of course! let’s dive into this topic!” there’s also user preferences in settings static instructions that apply to all chats. perfect place for baseline settings: language, format, response style. the token savings are small, but the sanity savings are massive. you stop burning the first 3-5 messages of every chat on calibration.
+
+## 3\. styles - switch formats in one click
+
+
+
+few people know this, but Claude has a custom styles feature. these are writing style presets you can switch between. why this matters. you have at least three different formats: a telegram post (short, punchy), an X thread (hooks, structure, CTA), a blog longread (depth, examples, data). typing “write in the style of…” every time is wasteful. instead you create a style, describe the rules, and then just select it before your prompt. Claude instantly switches between “aggressive crypto thread” and “analytical longread” with zero extra prompting. you can create styles manually, or ask Claude to analyze a text sample and generate a style from it.
+
+## 4\. artifacts - not text in chat, but a finished product
+
+
+
+when Claude answers in chat that’s text you then copy somewhere. when Claude creates an artifact it’s a separate object: a document, table, interactive widget, HTML page, even a mini app.
+
+for a content creator this means: instead of “write me a comparison table of traders” you get an actual interactive table you can download or share. instead of “sketch me a landing page” a working HTML with design.
+
+artifacts can render React components, SVG graphics, markdown documents, mermaid diagrams. if you need a yield calculator, portfolio tracker or data visualization Claude can build it right in the chat.
+
+most users treat Claude as a text generator. artifacts turn it into a finished product generator.
+
+## 5\. deep research - research on autopilot
+
+
+
+standard web search in Claude is a quick lookup across 1-3 queries. deep research is a completely different beast.
+
+you give it a task, Claude formulates dozens of search queries on its own, reads sources, cross-references data and delivers a structured report with links. this isn’t “google it for me” it’s full-blown research that would take you a couple hours.
+
+crypto context examples: “analyze the last 3 months of prediction market activity on Polymarket which categories are growing, which are declining, what trends are emerging”. or: “compile info on the top 10 copy-trading platforms in crypto, compare fees and mechanics”.
+
+the result is a ready-made report you can use as the foundation for an article, thread or internal doc.
+
+the feature is available on paid plans. if you make data-driven content this is one of the most useful tools out there.
+
+## 6\. structured prompts - prompt engineering for people tired of redoing things
+
+
+
+the advice “write specific prompts” is correct but way too vague. here’s what actually works: structured prompts with markup. Claude understands XML tags perfectly. instead of one long paragraph you break your prompt into blocks:
+
+```xml
+you're a copywriter for a crypto telegram channel
+
+write a post about trader {name} on Polymarket
+
+
+win rate: 73%
+profit: $47,000
+best trade: "Trump wins" @ 0.62
+
+
+
+- hook in the first line (question or hard statement)
+- 3-4 short paragraphs
+- concrete numbers in every paragraph
+- CTA at the end
+
+
+
+- no phrases like "in the world of crypto", "let's dive in"
+- no emojis at the start of lines
+- 150 words max
+
+
+
+[paste an example of a good post]
+
+```
+
+the difference is massive. with a prompt like this Claude nails the result on the first try 8 out of 10 times. and every saved iteration is both tokens and time. spend 20 minutes building a template once then use it for months, only swapping out the data.
+
+## 7\. extra usage - don’t overpay for Max if you don’t need it
+
+
+
+the Pro plan costs $20/mo. Max 5x - $100/mo. 5x the price, and most people think the choice is binary: either suffer with limits or pay a hundred. but there’s a third option: extra usage. you turn it on in settings, set a monthly cap (how much you’re willing to pay on top of $20), and when your regular limit runs out Claude keeps working at API prices. for most users this is cheaper than Max. you only pay for actual overuse, not a permanent subscription. if you have one heavy week per month and Pro covers the rest extra usage saves you $60-70 monthly compared to Max. important: set a spending cap. without one you can accidentally rack up a solid bill, especially if you use extended thinking.
+
+## 8\. connectors - Claude inside your ecosystem
+
+
+
+Claude can connect to external services: Google Drive, Gmail and others. this isn’t just “upload a file”. it’s direct access to your documents from inside the chat. connect Google Drive and you can say “find my content plan doc for april and suggest posts for next week”. Claude will find the file, read it and deliver the result. for those working with a team of content creators, this removes an entire layer of manual work: no need to download a file, upload it to the chat, explain context. Claude works with your data directly. setup takes a minute. settings -> connectors -> pick a service -> authorize.
+
+## 9\. peak hours and prompt editing - two tactical hacks
+
+
+
+two quick tips worth knowing.
+
+first: since march 2026 Claude burns through your limit faster during peak hours. peak is 5:00-11:00 AM pacific time, weekdays. for Europe that’s roughly 2:00-8:00 PM. the same request during peak hours costs more.
+
+practical takeaway: heavy tasks (longreads, research, generating large volumes of content) are better done in the morning or late evening European time. light tasks whenever.
+
+second: if Claude gave you something you didn’t want don’t write a new message saying “no, i meant something else”. hit edit on your original prompt and rephrase.
+
+every new message in the chat exponentially increases the cost: message number 30 costs 31 times more than the first one, because Claude re-reads the entire conversation history. fix the prompt, don’t feed the history.
+
+## 10\. multimodality - drop screenshots, stop describing things in words
+
+
+
+Claude can see images. this sounds obvious, but 90% of people keep describing in words what they could just show. instead of “on the BTC/USDT chart on the 4H timeframe there’s a double bottom forming with support at 62,400” just drop a screenshot of the chart and write “what do you see on this chart?”. Claude will break down patterns, levels, volumes.
+
+this works for: Polymarket screenshots (position analysis, order book), TradingView charts, on-chain dashboard screens, PDF reports (drop the whole thing, Claude reads it), even thread screenshots if you need a quick translation or summary.
+
+Claude processes multiple images in one message. you can drop 5 screenshots of different traders and ask for a comparative analysis all in one prompt. stop describing in words what you can show with an image. it’s faster, more accurate and saves tokens.
+
+## bottom line
+
+Claude is not a chatbot where you throw prompts and hope for the best. it’s a work tool with a ton of settings that most people simply never opened.
+
+open your settings. seriously.
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diff --git a/Claude/40 Claude Cowork Commands, Workflows & Automations Most Users Don't Know — The Complete List.md b/Claude/40 Claude Cowork Commands, Workflows & Automations Most Users Don't Know — The Complete List.md
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+++ b/Claude/40 Claude Cowork Commands, Workflows & Automations Most Users Don't Know — The Complete List.md
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+---
+title: "40 Claude Cowork Commands, Workflows & Automations Most Users Don't Know — The Complete List"
+source: "https://x.com/eng_khairallah1/status/2047609433489035739"
+author:
+ - "[[@eng_khairallah1]]"
+published: 2026-04-24
+created: 2026-04-26
+description: "I've been using Claude Cowork every single day since it launched.Save this :)These 40 commands and workflows are the ones that changed how I..."
+tags:
+ - "clippings"
+---
+
+
+I've been using Claude Cowork every single day since it launched.
+
+Save this :)
+
+These 40 commands and workflows are the ones that changed how I work.
+
+Most people open Cowork, type "organize my files," and think that is all it does.
+
+That is like buying a Swiss Army knife and only using the bottle opener.
+
+Cowork has an entire layer of functionality that almost nobody talks about - slash commands, scheduled tasks, sub-agent patterns, connector chains, and workflow templates that turn it from a helpful assistant into an autonomous operating system.
+
+Here are the 40 commands, tricks, and workflows that most users have no idea exist. Zero fluff. Every single one tested.
+
+# Slash Commands That Change Everything (1-10)
+
+**01\. /schedule**: Set up recurring tasks that run automatically. "Every Monday at 8am, check my Gmail for anything urgent, summarize my calendar for the week, and save a briefing to /Weekly." Your computer needs to be on and Claude Desktop open, but it runs unattended.
+
+**02\. /compact**: When your conversation gets long and Claude starts losing context, this compresses the conversation history while keeping the important details. Fresh context equals better output. Use this before Claude starts repeating mistakes.
+
+**03\. /clear**: Nuclear reset. Wipes the entire conversation and starts fresh. Use when context is too polluted to save. Better to start clean than fight a confused agent.
+
+**04\. /strategy**: From the Product Management plugin. Walks you through a full strategic canvas: vision, goals, target audience, competitive positioning. Chain it with /business-model → /pricing → /plan-launch for a complete product strategy session in 20 minutes.
+
+**05\. /review**: Custom slash command you can build. Create a review checklist for any type of work. content, code, proposals, reports. Put it in .claude/commands/ and it is available in every session.
+
+**06\. /memory**: Shows you which memory files and context Claude currently has loaded. This is your debugging tool when Claude is behaving inconsistently. If the right context is not loaded, you have found your problem.
+
+**07\. /doctor**: Diagnostic command. When something is not working right, this shows you the state of your Cowork environment. connected apps, loaded skills, available commands, and current permissions.
+
+**08\. /plan**: Forces Claude into planning mode before execution. Instead of diving straight into a task, Claude first creates a step-by-step plan, shows it to you for approval, and only then executes. Essential for any task touching multiple files or systems.
+
+**09\. /cost**: Shows estimated token usage for a task before you run it. On the Max plan this matters less, but on Pro ($20/month) where limits are tighter, knowing a task will cost 3x normal usage before running it saves you from burning through your allocation on something that was not worth it.
+
+**10\. /undo**: Rolls back the last file operation. Made a mistake? Claude moved the wrong files? Hit /undo before panicking. Only works for the most recent operation.
+
+# File System Power Moves (11-18)
+
+**11\. Batch rename with intelligence**: "Rename all files in /Downloads using this pattern: YYYY-MM-DD\_description\_type. Use the file creation date for the date and generate the description from the file content." Claude reads each file, understands what it is, and renames intelligently.
+
+**12\. Smart deduplication**: "Find all duplicate files across /Documents and /Desktop. Show me what you found before deleting anything. For near-duplicates (same content, different names), keep the one with the most recent modification date." Claude finds true duplicates AND near-duplicates.
+
+**13\. Folder structure from chaos**: "Look at every file in /Downloads. Create a logical folder structure based on what you find. group by project, then by file type within each project. Move everything into the new structure and give me a summary of what went where." Turns a dumping ground into an organized workspace.
+
+**14\. Archive stale files**: "Find all files in /Projects that haven't been modified in 90 days. Move them to /Archive/\[year\]/\[month\]. Don't touch anything in /Projects/Active." Automatic housekeeping without accidentally archiving active work.
+
+**15\. Template generator**: "Read all the proposals in /Proposals/Completed. Identify the common structure, sections, and formatting. Create a blank template in /Templates/proposal-template.docx that follows the same pattern." Claude reverse-engineers your best work into reusable templates.
+
+**16\. Recursive search and extract**: "Search through every PDF in /Research for mentions of \[topic\]. Extract the relevant paragraphs, note which document and page each one came from, and compile them into a single research summary file." Cross-document research in seconds.
+
+**17\. Format converter pipeline**: "Convert all .docx files in /Content to .md format. Preserve formatting, headers, and bullet points. Save the markdown versions to /Content/markdown with the same filenames." Batch conversion with formatting intelligence.
+
+**18\. Size audit**: "Analyze my /Documents folder. Show me the 20 largest files, any folders over 1GB, and estimate how much space I could free up by removing files I haven't opened in 6 months." Storage management with context.
+
+# Connector Workflows (19-26)
+
+**19\. Gmail → Summary → Drive**: "Check my Gmail for all unread emails. Categorize them: urgent, needs response, FYI, can delete. Draft responses for the routine ones. Save the entire summary to a file in my Google Drive /Daily folder with today's date."
+
+**20\. Calendar → Prep Brief**: "Check my Google Calendar for tomorrow. For each meeting, research the attendees and their companies. Create a one-page prep brief for each meeting and save them to /Meeting-Prep/\[date\]."
+
+**21\. Slack → Action Items**: "Read my Slack messages from the last 24 hours across all channels. Extract every action item directed at me. Compile them into a task list sorted by channel and urgency. Save to /Tasks/slack-actions-\[date\].md."
+
+**22\. Drive → Analysis → Slides**: "Pull the Q3 data from the spreadsheet in my Google Drive. Analyze trends, identify the top 3 insights, and create a 5-slide PowerPoint summary with charts. Save to /Presentations."
+
+**23\. Email chain resolver**: "Find the email thread about \[project\] in my Gmail. Read the entire thread. Summarize what was decided, what is still open, and who needs to do what next. Draft a follow-up email that moves the conversation forward."
+
+**24\. Multi-source report builder**: "Pull this week's sales data from the Google Sheet in Drive. Check Slack [#sales](https://x.com/search?q=%23sales&src=hashtag_click) channel for any deal updates mentioned this week. Combine everything into a formatted weekly sales report and save as PDF."
+
+**25\. Meeting notes distributor**: "Read the meeting notes I just saved to /Meetings. Extract the action items. For each person mentioned, draft a brief email with just their action items and deadlines. Show me the emails before sending."
+
+**26\. Cross-platform search**: "I am looking for information about \[project name\]. Search my Google Drive, Slack messages, and Gmail for anything related. Compile everything you find into a single document organized by source."
+
+# Document and Content Workflows (27-34)
+
+**27\. Voice note to polished draft**: "Read the transcript in /Voice-Notes/\[filename\]. This is a rough voice recording of my ideas about \[topic\]. Turn it into a polished 1,500-word article. Keep my natural voice but add structure, clean up the rambling, and make it publication-ready."
+
+**28\. Meeting recording to structured notes**: "Process the meeting transcript at /Meetings/\[filename\]. Create structured notes with: decisions made, action items (who owns each one + deadline), open questions, and key discussion points. Format as markdown."
+
+**29\. Research to executive brief**: "I have 12 articles saved in /Research/\[project\]. Read all of them. Create a 2-page executive brief that synthesizes the key findings, identifies conflicting viewpoints, and recommends 3 action items based on the research. Include source references."
+
+**30\. Proposal customizer**: "Read the proposal template at /Templates/proposal.docx. Read the client brief at /Clients/\[name\]/brief.md. Generate a customized proposal for this specific client. Match the template structure but tailor every section to their situation."
+
+**31\. Contract to plain English**: "Read the contract at /Legal/\[filename\].pdf. Create a plain-English summary with: key terms, important deadlines, auto-renewal clauses, liability caps, and anything unusual or potentially problematic. This is not legal advice. it is a time-saving first-pass review."
+
+**32\. Spreadsheet narrative writer**: "Read the spreadsheet at /Data/\[filename\].xlsx. Write a narrative analysis for a non-technical audience. Cover the top 3 trends, any anomalies worth investigating, and 2 recommended actions. Include the specific numbers but explain what they mean."
+
+**33\. Content repurposing pipeline**: "Read the article at /Content/\[filename\].md. Create: 8 standalone tweets, 2 LinkedIn posts, 3 Instagram captions, and 1 newsletter teaser email with a subject line. Save each format in its own file in /Content/repurposed/\[date\]."
+
+**34\. Weekly newsletter assembler**: "Read all files in /Content/drafts created this week. Select the 3 strongest pieces. Write a newsletter that links to each one with a 2-sentence teaser. Add an intro paragraph and a sign-off. Save as newsletter-\[date\].md."
+
+# Scheduled Automations (35-40)
+
+**35\. Daily inbox zero processor**: Schedule daily at 7am: "Check Gmail. Categorize all unread emails. Draft responses for routine ones. Flag urgent ones. Save the summary to /Daily/inbox-\[date\].md."
+
+**36\. Weekly file cleanup**: Schedule every Friday at 5pm: "Sort /Downloads by type. Move documents to /Documents, images to /Images, code to /Projects. Delete anything older than 60 days that is not in /Important."
+
+**37\. Monday planning brief**: Schedule every Monday at 7:30am: "Check my calendar for the week. Pull any relevant prep materials from Drive. Scan Gmail for outstanding action items. Create a weekly planning document with priorities, meetings, and deadlines. Save to /Weekly/plan-\[date\].md."
+
+**38\. Monthly financial organizer**: Schedule the 1st of every month: "Process all receipt images in /Receipts. Extract vendor, amount, date, and category from each. Create a categorized expense spreadsheet. Calculate totals by category. Save to /Finance/expenses-\[month\].xlsx."
+
+**39\. Bi-weekly competitive scan**: Schedule every other Monday: "Search the web for the latest news about \[competitor 1\], \[competitor 2\], and \[competitor 3\]. Check for pricing changes, product launches, and hiring. Compare to the previous scan. Save to /Intelligence/comp-scan-\[date\].md."
+
+**40\. End of day log**: Schedule daily at 6pm: "Read any files I created or modified today in /Projects. Write a brief work log noting what was accomplished, what is in progress, and what needs attention tomorrow. Save to /Daily/log-\[date\].md."
+
+# Pro Tips for Getting the Most Out of Cowork
+
+**Batch your tasks.** Instead of running 5 small tasks separately, combine related work into one session. One session processing 10 files uses fewer tokens than 10 separate sessions processing 1 file each.
+
+**Be specific in your instructions.** "Organize my files" burns tokens on back-and-forth clarification. "Move all PDFs from Downloads to Documents/PDFs, rename them with YYYY-MM-DD prefix, and delete anything older than 90 days" runs cleanly in one pass.
+
+**Schedule heavy tasks for off-peak hours.** Evenings and weekends reportedly give higher throughput. Schedule your most compute-intensive automations for these windows.
+
+**Always use /plan for complex tasks.** If a task touches more than 3 files or involves multiple steps, force plan mode first. Review the plan. Adjust. Then execute. You will save tokens and avoid mistakes.
+
+**Keep your folder structure clean.** Cowork works best with logical, predictable folder structures. The cleaner your file system, the better Claude navigates it.
+
+# TL;DR
+
+**40 commands and workflows. All tested daily. Most users know maybe 5 of these.**
+
+Cowork is not a chatbot with file access. It is an autonomous operating system that runs your workflows while you focus on the work that actually matters.
+
+This list took months of daily usage to compile. If it saved you time, you know what to do.
+
+I post stuff like this regularly - AI tools, workflows, commands, and things I actually use. No fluff.
+
+**Follow me** [@eng\_khairallah1](https://x.com/@eng_khairallah1) **for more developer tools, workflows, and techniques. No fluff. Just what works.**
+
+**hope this was useful for you, Khairallah** **❤️**
\ No newline at end of file
diff --git a/Claude/40 Powerful AI Prompts for Claude That Produce Expert-Level Results.md b/Claude/40 Powerful AI Prompts for Claude That Produce Expert-Level Results.md
new file mode 100644
index 0000000..1098706
--- /dev/null
+++ b/Claude/40 Powerful AI Prompts for Claude That Produce Expert-Level Results.md
@@ -0,0 +1,224 @@
+---
+title: "40 Powerful AI Prompts for Claude That Produce Expert-Level Results"
+source: "https://x.com/eng_khairallah1/status/2048334883198738761"
+author:
+ - "[[@eng_khairallah1]]"
+published: 2026-04-26
+created: 2026-04-26
+description: "I tested 500+ prompts, and these are the 40 that produce expert-level output every single time.Save this :)Most prompt collections are gener..."
+tags:
+ - "clippings"
+---
+
+
+I tested 500+ prompts, and these are the 40 that produce expert-level output every single time.
+
+Save this :)
+
+Most prompt collections are generic. "Write me a blog post." "Summarize this text." "Explain this concept."
+
+Those are not prompts. Those are wishes.
+
+A real prompt is an engineered instruction that produces consistent, high-quality, production-ready output regardless of which model you use. It includes the role, the context, the constraints, the format, the quality standard, and specific examples.
+
+These 40 prompts work on Claude, ChatGPT, AND Gemini. Tested on all three. Verified on all three. Copy-paste ready.
+
+**Organized by use case. Difficulty-rated. Save this.**
+
+# Writing and Content (01 to 10)
+
+**01\. The Expert Article Writer**
+
+You are a senior content strategist who has written for top-tier publications. Write a \[WORD COUNT\]-word article about \[TOPIC\]. Audience: \[WHO THEY ARE and WHAT THEY KNOW\] Angle: \[YOUR UNIQUE TAKE — what makes this different from every other article on this topic\] Structure: - Hook: Open with a bold claim or surprising fact. No generic introductions. - Problem: Why the current way of thinking about this topic is wrong or incomplete. - Framework: Present your argument in 3-5 named sections with clear headers. - Evidence: Each section must include one specific example, case study, or data point. - Action: End with 3 specific things the reader can do THIS WEEK. Rules: - Paragraphs: 3 sentences maximum - No filler phrases ("it is important to note", "in today's world") - No hedge words ("might", "could potentially", "it seems like") - Every claim must be specific, not vague - Bold the most important sentence in each section This article should be good enough to publish without editing.
+
+**02\. The Thread Architect**
+
+Write a Twitter/X thread about \[TOPIC\]. Thread structure: - Tweet 1: The hook. Bold claim, surprising stat, or contrarian take. Must stop the scroll in 2 seconds. - Tweets 2-3: The problem. Why most people get this wrong. - Tweets 4-10: The framework. Numbered steps, techniques, or insights. One per tweet. Each tweet must stand alone AND flow in sequence. - Tweet 11-12: Real example or case study proving the framework works. - Final tweet: One actionable takeaway + call to action. Rules: - Each tweet under 280 characters - No hashtags - No emojis unless they add meaning - No "let me explain" or "here's the thing" — start each tweet with substance - Thread should feel like learning from a sharp friend, not reading a textbook Total: 12-15 tweets.
+
+**03\. The Email Drafter**
+
+Draft an email for this situation: \[DESCRIBE THE SITUATION, THE RECIPIENT, AND YOUR GOAL\] Tone: \[professional/casual/direct/diplomatic\] Rules: - Subject line: specific and action-oriented (not "Quick question" or "Following up") - Opening: Get to the point in the first sentence. No "I hope this finds you well." - Body: Maximum 3 short paragraphs. Each paragraph serves one purpose. - Close: Clear next step or ask. The recipient should know exactly what you want them to do. - Total length: Under 150 words Generate 2 versions: Version A: \[TONE 1 — e.g., direct and assertive\] Version B: \[TONE 2 — e.g., warm and collaborative\]
+
+**04\. The Content Repurposer**
+
+Take this content and repurpose it into 5 formats: \[PASTE YOUR ARTICLE/POST/TRANSCRIPT\] Create: 1. Twitter/X thread (12 tweets, each under 280 characters) 2. LinkedIn post (200-300 words, professional but not corporate) 3. Newsletter intro (100 words, teases the full content) 4. 3 standalone social media posts (each self-contained, each highlighting a different insight) 5. Short-form video script (60 seconds, conversational, designed to be spoken to camera) Rules: - Each format should feel native to its platform, not like a copy-paste - Maintain the core argument and key insights across all formats - Adjust tone per platform: X = punchy and direct, LinkedIn = professional and thoughtful, Newsletter = personal and exclusive
+
+**05\. The Copywriting Converter**
+
+Rewrite this text to be more persuasive: \[PASTE TEXT\] Apply these copywriting principles: - Lead with the benefit, not the feature - Use specific numbers instead of vague claims - Address objections before they form - Create urgency without being manipulative - End with a clear, low-friction call to action Show the rewritten version, then explain the 3 most impactful changes you made and why they work psychologically.
+
+**06\. The Blog Post Outliner**
+
+Create a detailed outline for a blog post about \[TOPIC\]. Target audience: \[WHO\] Goal: \[WHAT THE READER SHOULD DO/FEEL/KNOW AFTER READING\] Target length: \[WORD COUNT\] For each section, provide: - Header (compelling, specific, not generic) - 2-3 sentence summary of what this section covers - The key data point, example, or argument this section needs - Transition to the next section Also include: - 3 alternative headline options (ranked by predicted click-through) - Suggested meta description (under 160 characters) - 5 internal/external linking opportunities
+
+**07\. The Storytelling Transformer**
+
+Take this dry, factual information and transform it into a compelling narrative: \[PASTE DRY FACTS, DATA, OR TECHNICAL CONTENT\] Rules: - Open with a specific scene, person, or moment — not a definition - Weave the facts into a story arc: setup → tension → resolution - Use analogies to make complex ideas feel intuitive - Include one "aha moment" where the reader's understanding shifts - End with a takeaway that connects the story back to the reader's life Keep the facts accurate. Change the delivery, not the truth.
+
+**08\. The Headline Generator**
+
+Generate 20 headline options for this content: \[BRIEF DESCRIPTION OF THE CONTENT\] Categories: - 5 curiosity-driven headlines (make them NEED to click) - 5 benefit-driven headlines (make them see the value immediately) - 5 contrarian headlines (challenge what they currently believe) - 5 specific-number headlines (use exact quantities or timeframes) For each headline, rate its predicted click-through on a scale of 1-10 and explain why. Rank your top 3 overall with reasoning.
+
+**09\. The Case Study Builder**
+
+Turn these raw facts into a compelling case study: Client: \[NAME/TYPE\] Problem: \[WHAT THEY STRUGGLED WITH\] Solution: \[WHAT WAS IMPLEMENTED\] Results: \[MEASURABLE OUTCOMES\] Structure: 1. The Challenge (2 paragraphs — make the reader feel the pain) 2. The Approach (3-4 paragraphs — specific steps taken, not generic descriptions) 3. The Results (1-2 paragraphs — specific numbers, before/after comparisons) 4. Key Takeaway (1 paragraph — the lesson that applies to the reader) 5. Pull Quote (one sentence the client would actually say, based on the results) Rules: - Use present tense for immediacy - Include at least 3 specific numbers - The pull quote should feel real, not like marketing copy
+
+**10\. The Style Mimic**
+
+Analyze these 3 writing samples from the same author: \[PASTE\]\[PASTE\]\[PASTE\] Identify: - Sentence length patterns (short, mixed, long) - Vocabulary level (simple, technical, mixed) - Tone (formal, casual, authoritative, conversational) - Structural habits (paragraph length, use of headers, bullet patterns) - Signature phrases or patterns they repeat - Things they consistently avoid Then write a 300-word piece about \[NEW TOPIC\] that matches this author's exact style. The reader should believe the original author wrote it.
+
+# Analysis and Strategy (11 to 20)
+
+**11\. The SWOT Analyzer**
+
+Perform a comprehensive SWOT analysis of \[COMPANY/PRODUCT/STRATEGY\]. For each quadrant (Strengths, Weaknesses, Opportunities, Threats): - List 5 specific items (not generic — tied to THIS specific situation) - For each item: one sentence explaining WHY it belongs in this quadrant - Rate each item's impact: High / Medium / Low Then provide: - The #1 strategic priority based on this analysis (one sentence) - The biggest risk if this priority is ignored (one sentence) - The first action to take this week (one specific, actionable step)
+
+**12\. The Decision Matrix**
+
+I need to decide between these options: \[LIST 2-4 OPTIONS\] Context: \[RELEVANT BACKGROUND — budget, timeline, team, goals\] Build a decision matrix: 1. Identify the 5 most important criteria for this decision (ask me if unsure) 2. Weight each criterion by importance (must total 100%) 3. Score each option against each criterion (1-10) 4. Calculate weighted scores 5. Present as a formatted table Then write a 2-paragraph recommendation that: - Clearly states which option to choose and why - Acknowledges the strongest argument for the runner-up - Identifies the one condition that would change the recommendation
+
+**13\. The Root Cause Analyzer**
+
+Here is a problem I am facing: \[DESCRIBE THE PROBLEM AND ITS SYMPTOMS\] Perform a root cause analysis: 1. Ask "Why?" 5 times in sequence (the 5 Whys technique), going deeper each time 2. For each level, identify whether this is a symptom or a root cause 3. At the deepest level, identify the TRUE root cause 4. Propose 3 solutions — one for the surface symptom, one for the mid-level cause, and one for the root cause 5. Recommend which solution to implement and why Do not accept my initial framing of the problem at face value. The real problem is often not the one I described.
+
+**14\. The Market Opportunity Scanner**
+
+Analyze the market opportunity for \[PRODUCT/SERVICE IDEA\]. Evaluate: 1. DEMAND: Who wants this? How many of them are there? How do you know they want it? 2. COMPETITION: Who else does this? What do they charge? Where are they weak? 3. TIMING: Why now? What changed that makes this viable today when it was not before? 4. MOAT: What would prevent competitors from copying this in 6 months? 5. UNIT ECONOMICS: What would this cost to deliver per customer vs. what could you charge? For each section: be specific, use numbers where possible, and flag anything you are uncertain about. End with a GO / CAUTIOUS GO / NO GO recommendation with your confidence level (high/medium/low).
+
+**15\. The Meeting Strategist**
+
+I have a meeting about \[TOPIC\] with \[WHO — their role, relationship, what they care about\]. My goal for this meeting: \[WHAT YOU WANT TO ACHIEVE\] Prepare: 1. Opening statement (2 sentences — sets the right frame for the conversation) 2. 3 key points I must communicate (in priority order) 3. 3 questions I should ask (ordered from most to least important) 4. 3 likely objections and how to respond to each 5. My ideal closing statement (summarizes agreement and next steps) 6. Walk-away point: what is the minimum acceptable outcome?
+
+**16\. The Pricing Strategist**
+
+Help me price \[PRODUCT/SERVICE\]. Context: - What it does: \[DESCRIPTION\] - Who it is for: \[TARGET CUSTOMER\] - What it costs me to deliver: \[YOUR COSTS\] - Competitor pricing: \[WHAT ALTERNATIVES CHARGE, if known\] - Value delivered: \[WHAT THE CUSTOMER GETS — time saved, money earned, problems solved\] Design a pricing structure: 1. Three tiers (entry, core, premium) with names that reflect value, not size 2. What is included in each tier and WHY each boundary exists 3. The psychology behind each price point 4. Which tier most customers should land on and how to guide them there 5. One-time vs. recurring analysis — which model fits better and why Show the pricing in a formatted comparison table.
+
+**17\. The Competitive Teardown**
+
+Perform a competitive teardown of \[COMPETITOR NAME/URL\]. Analyze: 1. POSITIONING: What is their core message? Who are they targeting? What emotion are they selling? 2. PRODUCT: What do they actually offer? What is the core feature vs. nice-to-haves? 3. PRICING: How do they charge? What does each tier include? Where is the profit margin? 4. CONTENT: What topics do they publish about? How often? What format performs best? 5. WEAKNESS: Where are they vulnerable? What do their customers complain about? What are they not doing? End with: "If I were competing directly with \[COMPETITOR\], the 3 things I would do differently are..."
+
+**18\. The OKR Builder**
+
+Help me create OKRs for \[TEAM/INDIVIDUAL/COMPANY\] for \[TIME PERIOD\]. Context: \[CURRENT SITUATION — where we are, where we want to go, what resources we have\] For each Objective (suggest 3): - The Objective: ambitious but achievable, qualitative, inspiring - 3-4 Key Results: specific, measurable, time-bound - For each Key Result: the current baseline, the target, and how you would measure it - A confidence level (1-10) that this Key Result is achievable in the timeframe Flag any Key Results that might conflict with each other.
+
+**19\. The Risk Assessor**
+
+I am about to \[DESCRIBE THE INITIATIVE/DECISION/PROJECT\]. Perform a risk assessment: 1. List the 7 most likely risks (things that could go wrong) 2. For each risk: - Probability: High / Medium / Low - Impact if it occurs: High / Medium / Low - Early warning sign (how would I detect this risk materializing?) - Mitigation strategy (what would I do to prevent it?) - Contingency plan (what would I do if it happens anyway?) 3. Plot all risks on a 2x2 matrix (probability vs. impact) 4. Identify the top 3 risks I should actively monitor Be pessimistic. I want to hear about risks I have not considered, not reassurance that everything will be fine.
+
+**20\. The Retrospective Facilitator**
+
+Facilitate a retrospective for this project/period: \[DESCRIBE WHAT HAPPENED\] Structure: 1. WHAT WENT WELL (identify 5 specific things that worked, with evidence) 2. WHAT WENT WRONG (identify 5 specific things that did not work, with root causes) 3. WHAT WE LEARNED (3 lessons that will change how we work going forward) 4. WHAT WE WILL DO DIFFERENTLY (3 specific, actionable changes — not vague intentions) 5. WHAT WE WILL STOP DOING (2 things we should deliberately stop) Rules: - Be specific. "Communication was bad" is useless. "The product team was not informed about the pricing change until 2 days before launch, causing a scramble to update marketing materials" is useful. - For every problem identified, include a specific preventive action.
+
+# Technical and Development (21 to 28)
+
+**21\. The Architecture Advisor**
+
+I want to build \[DESCRIBE THE SYSTEM\]. Requirements: - \[REQUIREMENT 1\] - \[REQUIREMENT 2\] - \[REQUIREMENT 3\] - Expected scale: \[USERS/DATA VOLUME\] - Budget constraints: \[ANY LIMITATIONS\] Propose 2 architectural approaches. For each: 1. Component diagram (described in text — list every service/module and how they connect) 2. Technology choices with reasoning for each 3. Pros and cons (be honest about tradeoffs) 4. Estimated complexity: Simple / Moderate / Complex 5. The #1 thing that could go wrong with this approach Recommend one approach. Explain why. Then give me the first 5 implementation steps in order.
+
+**22\. The Code Reviewer**
+
+Review this code: \[PASTE CODE\] Check for: 1. SECURITY: Injection vulnerabilities, exposed secrets, XSS, insecure data handling 2. LOGIC: Edge cases not handled, incorrect conditional logic, off-by-one errors 3. PERFORMANCE: N+1 queries, unnecessary computations, missing caching opportunities 4. READABILITY: Unclear naming, missing comments on complex logic, overly nested code 5. BEST PRACTICES: Violations of language conventions, missing error handling, unused imports For each issue found: - Severity: Critical / High / Medium / Low - Exact location (line or function name) - Why it is a problem (not just what is wrong, but what could happen) - The fix (show corrected code) If the code is clean, say so. Do not invent issues to seem thorough.
+
+**23\. The Debug Diagnostician**
+
+I am getting this error: \[PASTE COMPLETE ERROR MESSAGE AND STACK TRACE\] Context: \[WHAT THE CODE IS SUPPOSED TO DO\] 1. Do NOT jump to a fix immediately 2. First: explain what this error message means in plain English 3. Second: list the 3 most likely root causes in order of probability 4. Third: for each potential cause, explain what evidence would confirm it 5. Fourth: once the root cause is identified, show the fix 6. Fifth: explain what would prevent this type of bug in the future \[PASTE RELEVANT CODE\]
+
+**24\. The API Designer**
+
+Design a REST API for \[DESCRIBE THE SYSTEM/FEATURE\]. For each endpoint: - Method and path (following REST conventions) - Request body schema (with required/optional fields) - Response schema (success and error cases) - Authentication requirements - Rate limiting recommendation Also include: - Error response format (consistent across all endpoints) - Pagination approach for list endpoints - Versioning strategy - 3 potential security concerns and how to address each Present as a formatted API reference document.
+
+**25\. The Database Schema Designer**
+
+Design a database schema for \[DESCRIBE THE APPLICATION\]. Requirements: - \[LIST KEY ENTITIES AND RELATIONSHIPS\] - Expected scale: \[DATA VOLUME AND GROWTH RATE\] For each table: - Columns with types, constraints, and descriptions - Primary key and indexes - Foreign key relationships Also provide: - An entity relationship description (how tables connect) - 3 common queries this schema must support efficiently - Index recommendations for those queries - One potential scaling concern and how to address it - Migration strategy if requirements change later
+
+**26\. The Test Case Generator**
+
+Generate comprehensive test cases for this function/feature: \[PASTE CODE OR DESCRIBE THE FEATURE\] Test categories: 1. HAPPY PATH: 3 tests for normal, expected usage 2. EDGE CASES: 5 tests for boundary conditions, empty inputs, maximum values 3. ERROR CASES: 3 tests for invalid inputs, missing data, system failures 4. SECURITY: 2 tests for injection attempts, unauthorized access 5. PERFORMANCE: 1 test for behavior under load or with large datasets For each test: - Test name (descriptive) - Input - Expected output - Why this test matters
+
+**27\. The Documentation Writer**
+
+Write developer documentation for this code/API/system: \[PASTE CODE OR SPECIFICATION\] Include: 1. Overview (what this does and why it exists — 2-3 sentences) 2. Quick Start (get up and running in under 5 minutes) 3. Core Concepts (explain the mental model, not just the API surface) 4. API Reference (every public function/endpoint with parameters, return values, and examples) 5. Common Patterns (3 typical usage patterns with code examples) 6. Troubleshooting (5 common issues and their solutions) Write for a developer who is smart but has never seen this codebase before.
+
+**28\. The Refactoring Planner**
+
+This code needs refactoring: \[PASTE CODE\] Analyze: 1. What are the top 3 code quality issues? (duplicated logic, mixed responsibilities, unnecessary complexity) 2. For each issue: explain WHY it is a problem, not just that it exists 3. Propose a refactoring plan with specific steps in order 4. For each step: show the before and after code 5. Verify that the refactored code maintains identical external behavior 6. Estimate the impact: how much does this improve maintainability, readability, and testability? Do NOT change external behavior. Internal improvement only.
+
+# Productivity and Personal (29 to 35)
+
+**29\. The Weekly Planner**
+
+Here are my goals for this quarter: \[LIST THEM\] Here is what I accomplished last week: \[BRIEF SUMMARY\] Here are my commitments this week: \[MEETINGS, DEADLINES, OBLIGATIONS\] Create my weekly plan: 1. TOP 3 PRIORITIES (the things that matter most for my quarterly goals) 2. SCHEDULED (meetings and deadlines by day) 3. BUFFER TASKS (important but flexible) 4. DELIBERATELY SKIPPING (things I am choosing NOT to do this week and why) The DELIBERATELY SKIPPING section is the most important. Saying no is how priorities stay priorities.
+
+**30\. The Learning Accelerator**
+
+I want to learn \[TOPIC/SKILL\]. My current level: \[BEGINNER/INTERMEDIATE/ADVANCED\] Time available: \[HOURS PER WEEK\] Learning style: \[PRACTICAL/THEORETICAL/MIXED\] Goal: \[WHAT I WANT TO BE ABLE TO DO AFTER LEARNING THIS\] Create a learning plan: 1. Prerequisites: what do I need to know first? (be honest if I am missing foundations) 2. Core concepts: the 5-7 key ideas I must understand, in the order I should learn them 3. Projects: for each concept, one hands-on project that teaches it through building 4. Resources: for each concept, the single best resource (not 10 options — ONE) 5. Milestones: how do I know I actually understand each concept? (specific test, not "feel comfortable with") 6. Timeline: realistic week-by-week plan given my available time Do not pad the plan. If I can learn this in 3 weeks, do not stretch it to 8.
+
+**31\. The Negotiation Prep**
+
+I am about to negotiate \[WHAT\] with \[WHO\]. Context: \[RELEVANT BACKGROUND — relationship, history, power dynamics\] My ideal outcome: \[BEST CASE\] My acceptable outcome: \[MINIMUM I WILL ACCEPT\] Their likely position: \[WHAT THEY PROBABLY WANT\] Prepare: 1. My opening position and the reasoning behind it 2. 3 concessions I can offer (ordered from smallest to largest) 3. 3 things I should ask for in return for each concession 4. Their 3 most likely objections and my response to each 5. 2 creative options that give both sides more value (expand the pie) 6. My walk-away line and the exact words I would use to exit gracefully
+
+**32 — The Habit Designer**
+
+I want to build this habit: \[DESCRIBE THE HABIT\] My current routine: \[WHAT MY TYPICAL DAY LOOKS LIKE\] Past attempts: \[WHAT I HAVE TRIED BEFORE AND WHY IT FAILED\] Design a habit implementation plan: 1. The smallest possible version of this habit (2-minute starter version) 2. The trigger: what existing habit or event will I attach this to? 3. The environment design: what physical change makes the habit easier? 4. The reward: what immediate positive feedback reinforces the habit? 5. The tracking method: how do I measure consistency? 6. The failure protocol: what do I do when I miss a day? (because I will) 7. The progression: how does the 2-minute version grow over 4 weeks? Be realistic. I would rather build a tiny habit that sticks than design a perfect routine I abandon in a week.
+
+# Data and Research (33 to 37)
+
+**33\. The Data Interpreter**
+
+Analyze this data: \[PASTE DATA — CSV, table, numbers, survey results, etc.\] Provide: 1. Summary statistics (key numbers at a glance) 2. The 3 most important patterns or trends 3. 2 surprising findings that are not immediately obvious 4. 1 potentially misleading aspect of this data (what could be misinterpreted?) 5. 3 questions this data raises that would require additional data to answer Audience: \[WHO WILL READ THIS ANALYSIS AND THEIR TECHNICAL LEVEL\] Present findings in plain English first, then include the supporting numbers.
+
+**34\. The Survey Analyzer**
+
+Analyze these survey results: \[PASTE SURVEY RESPONSES\] Provide: 1. Key demographics/segments in the respondent pool 2. Top 5 findings ranked by significance 3. Areas of consensus (where respondents strongly agree) 4. Areas of division (where opinions are split) 5. Surprising findings that contradict common assumptions 6. Actionable recommendations based on the data (3 specific things to do) 7. Limitations of this data (sample size, bias, what we cannot conclude)
+
+**35\. The Research Synthesizer**
+
+I have gathered research from multiple sources on \[TOPIC\]: \[PASTE OR SUMMARIZE\]\[PASTE OR SUMMARIZE\]\[PASTE OR SUMMARIZE\] Synthesize: 1. Key themes across all sources (what do they agree on?) 2. Contradictions (where do sources disagree? who is more credible and why?) 3. Gaps (what questions remain unanswered?) 4. The 3 most important conclusions I can confidently draw 5. What I should research next to strengthen my understanding Do not just summarize each source separately. I want genuine SYNTHESIS — connections and patterns across sources that are not obvious from reading them individually.
+
+# Communication (36 to 40)
+
+**36\. The Difficult Conversation Prep**
+
+I need to have a difficult conversation about \[TOPIC\] with \[WHO — role, relationship\]. Situation: \[WHAT HAPPENED AND WHY THIS CONVERSATION IS NECESSARY\] My goal: \[WHAT I WANT TO ACHIEVE — not just what I want to say\] Their likely reaction: \[HOW I EXPECT THEM TO RESPOND\] Prepare: 1. Opening statement (direct but empathetic — does not avoid the issue but does not attack the person) 2. The core message in one sentence (what MUST be communicated regardless of how the conversation goes) 3. How to handle defensiveness (specific responses to "that's not fair" / "you're wrong" / emotional reactions) 4. How to listen actively (what questions to ask to understand their perspective) 5. How to reach resolution (3 possible outcomes ranked from best to acceptable) 6. Closing statement (summarizes agreement, preserves relationship, defines next steps)
+
+**37\. The Feedback Giver**
+
+I need to give feedback to \[WHO — role, relationship\] about \[WHAT — specific behavior or work\]. Context: \[POSITIVE RELATIONSHIP? RECURRING ISSUE? FIRST TIME?\] Draft the feedback using this framework: 1. Observation (what specifically I observed — behavior, not character) 2. Impact (how it affected the team, the project, or the outcome — specific, not vague) 3. Expectation (what I would like to see going forward — specific and achievable) 4. Support (what I will do to help them succeed — not just demand change) Tone: \[SUPPORTIVE / DIRECT / SERIOUS\] Write it as spoken words, not an email. This is a conversation, not a memo.
+
+**38\. The Presentation Outliner**
+
+Create a presentation outline for \[TOPIC\]. Audience: \[WHO and WHAT THEY CARE ABOUT\] Duration: \[LENGTH\] Goal: \[WHAT SHOULD THE AUDIENCE DO/THINK/FEEL AFTER THIS PRESENTATION\] Structure: 1. Opening (30 seconds — hook that makes them put down their phones) 2. Problem (1-2 minutes — make them feel the pain of the current situation) 3. Solution (3-5 minutes — your framework/proposal/insight, broken into 3 clear points) 4. Evidence (2-3 minutes — specific examples, data, or case studies for each point) 5. Objection handling (1-2 minutes — address the biggest concern before they raise it) 6. Call to action (30 seconds — exactly what you want them to do next) For each section: the key slide content, speaker notes, and one transition sentence to the next section.
+
+**39\. The Apology Crafter**
+
+I need to apologize to \[WHO\] for \[WHAT HAPPENED\]. Context: \[THE SITUATION — what I did, the impact, the relationship\] Draft an apology that: 1. Acknowledges specifically what I did wrong (not vague — the exact action) 2. Demonstrates I understand the impact on them (from THEIR perspective, not mine) 3. Takes full responsibility (no "but", no "if", no deflection) 4. Explains what I will do differently going forward (specific, actionable, verifiable) 5. Does NOT ask for forgiveness (that is their choice, not my request) Tone: sincere and direct. Under 200 words. No over-explaining. No making it about my feelings. Generate 2 versions: Version A: written (email/message) Version B: spoken (what I would say in person)
+
+**40\. The Elevator Pitch Builder**
+
+Build an elevator pitch for \[PRODUCT/SERVICE/IDEA\]. Audience: \[WHO I AM PITCHING TO\] Context: \[WHERE THIS PITCH WOULD HAPPEN — investor meeting, networking event, cold outreach\] Time: \[30 seconds / 60 seconds / 2 minutes\] Structure: 1. Hook (one sentence that makes them want to hear more) 2. Problem (one sentence describing the pain — use their language, not yours) 3. Solution (one sentence describing what I do — not how it works, what it DOES for them) 4. Proof (one sentence of credibility — traction, results, notable client) 5. Ask (one sentence — what I want them to do next) Generate 3 versions at different energy levels: Version A: confident and bold Version B: conversational and warm Version C: data-driven and precise Each version should feel natural when spoken aloud, not written.
+
+# How to Use This Collection
+
+**Step 1:** Save this list. You will not memorize 40 prompts and you should not try.
+
+**Step 2:** Find the 5 prompts most relevant to your work this week. Copy them. Customize the variables. Use them.
+
+**Step 3:** When a prompt produces excellent results, save the customized version to a personal template file. Over time you build a library tailored to your exact work.
+
+**Step 4:** Every week, try 2-3 prompts from this list that you have not used before. Expand your capability gradually.
+
+**The compounding effect:** After one month of consistent use, your AI workflow operates at a level most people will not reach for years.
+
+# TL;DR
+
+40 prompts. Tested on Claude, ChatGPT, and Gemini. Each one engineered for expert-level output.
+
+Writing and content. Analysis and strategy. Technical development. Productivity. Data and research. Communication.
+
+Stop typing vague prompts. Start using engineered ones.
+
+This collection took months to test and refine
+
+**Follow me** [@eng\_khairallah1](https://x.com/@eng_khairallah1) **for more tools, workflows, and systems. No fluff. Just what works.**
+
+**hope this was useful for you, Khairallah** **❤️**
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diff --git a/Claude/Agreement is a bug. I forced 11 Claude Code agents to disagree.md b/Claude/Agreement is a bug. I forced 11 Claude Code agents to disagree.md
new file mode 100644
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--- /dev/null
+++ b/Claude/Agreement is a bug. I forced 11 Claude Code agents to disagree.md
@@ -0,0 +1,354 @@
+---
+title: "Agreement is a bug. I forced 11 Claude Code agents to disagree."
+source: "https://x.com/nyk_builderz/status/2034492549180625316"
+author:
+ - "[[@nyk_builderz]]"
+published: 2026-03-19
+created: 2026-04-26
+description: "I tested 40+ architecture and strategy decisions with Claude Code. The biggest failures weren’t “wrong answers.” They were blind spots from ..."
+tags:
+ - "clippings"
+---
+
+
+I tested 40+ architecture and strategy decisions with Claude Code. The biggest failures weren’t “wrong answers.” They were **blind spots from a single perspective**.
+
+So I built a system that forces 11 agents to disagree before they agree. The breakthrough wasn’t a better prompt.
+
+It was a **structured disagreement**:
+
+• 11 perspectives modeled on historical thinkers
+
+• 6 deliberate polarity pairs
+
+• a 3-round protocol with cross-examination before consensus
+
+If you skip deliberation, you’re trusting a single perspective on a multi-dimensional decision.
+
+In this article, I’ll show you the full architecture, anti-recursion safeguards, and 11 pre-built triads for common domains.
+
+## The Problem With “Balanced” Single-Agent Answers
+
+Ask one model: “Monorepo or polyrepo?”
+
+You’ll get a polished, nuanced answer. It sounds balanced.
+
+It isn’t.
+
+The output comes from one reasoning tradition at a time.
+
+If the model leans system-first, you get monorepo logic.
+
+If it leans modularity-first, you get polyrepo logic.
+
+You’re still getting one perspective—just well-written.
+
+Even structured single-agent skills (“find the crux,” etc.) improve organization, but not perspective diversity.
+
+You get better singular reasoning.
+
+You do not get adversarial deliberation.
+
+## Council of High Intelligence: System Design
+
+LLMs don’t truly think in parallel.
+
+They simulate one coherent viewpoint per generation.
+
+So I externalized the disagreement layer:
+
+• **11 independent Claude Code subagents**
+
+• each with a unique analytical method
+
+• each with explicitly declared blind spots
+
+• coordinated by a central protocol enforcer
+
+Each one spawns as an independent Claude Code subagent with its own system prompt, analytical method, and blind spots declared upfront.
+
+```text
+┌─────────────────────────────────────────────┐
+│ Council of High Intelligence │
+│ │
+│ ┌─────────┐ ┌─────────┐ ┌─────────┐ │
+│ │Socrates │ │Aristotle│ │Aurelius │ │
+│ │ (opus) │ │ (opus) │ │ (opus) │ │
+│ └────┬────┘ └────┬────┘ └────┬────┘ │
+│ │ │ │ │
+│ ┌────┴────┐ ┌────┴────┐ ┌────┴────┐ │
+│ │Feynman │ │Lao Tzu │ │Sun Tzu │ │
+│ │(sonnet) │ │ (opus) │ │(sonnet) │ │
+│ └────┬────┘ └────┬────┘ └────┬────┘ │
+│ │ │ │ │
+│ ┌────┴────┐ ┌────┴────┐ ┌────┴────┐ │
+│ │ Ada │ │Machiav. │ │Torvalds │ │
+│ │(sonnet) │ │(sonnet) │ │(sonnet) │ │
+│ └────┬────┘ └────┬────┘ └────┬────┘ │
+│ │ │ │
+│ ┌────┴────┐ ┌────┴────┐ │
+│ │Musashi │ │ Watts │ │
+│ │(sonnet) │ │ (opus) │ │
+│ └─────────┘ └─────────┘ │
+│ │
+│ Coordinator: routes, enforces protocol, │
+│ applies anti-recursion rules │
+└─────────────────────────────────────────────┘
+```
+
+Model split:
+
+> **OPUS (depth-heavy):** Socrates, Aristotle, Aurelius, Lao Tzu, Watts
+
+> **SONNET (speed-critical):** Feynman, Sun Tzu, Ada, Machiavelli, Torvalds, Musashi
+
+This is not “11 experts.”
+
+It’s a deliberate system of counterweights.
+
+```text
+OPUS (depth-heavy)
+─────────────────────────────────────────────
+Socrates assumption destruction
+ → hidden premises everyone accepts
+Aristotle categorization and structure
+ → what category something belongs to
+Marcus Aurelius resilience and moral clarity
+ → what you control vs what you do not
+Lao Tzu non-action and emergence
+ → when the solution is to stop trying
+Alan Watts perspective dissolution
+ → when the problem is the framing
+
+SONNET (speed-critical)
+─────────────────────────────────────────────
+Feynman first-principles debugging
+ → unexplained complexity
+Sun Tzu adversarial strategy
+ → terrain and competitive dynamics
+Ada Lovelace formal systems
+ → what can and cannot be mechanized
+Machiavelli power dynamics
+ → how actors actually behave
+Linus Torvalds pragmatic engineering
+ → what ships vs what sounds good
+Miyamoto Musashi strategic timing
+ → the decisive moment
+```
+
+Each agent declares its analytical method, what it sees that others miss, and — critically — what it tends to miss.
+
+Socrates knows he spirals into infinite questioning.
+
+Torvalds knows he dismisses theoretical elegance. These declared blind spots are why the polarity pairs matter.
+
+## The 6 polarity pairs
+
+The council is not 11 random thinkers. It is 6 deliberate counterweights:
+
+1. **Socrates vs Feynman** — both question everything, but Socrates destroys top-down, while Feynman rebuilds bottom-up
+2. **Aristotle vs Lao Tzu** — Aristotle classifies everything into categories. Lao Tzu says the categories are the problem
+3. **Sun Tzu vs Aurelius** — Sun Tzu wins the external game. Aurelius governs the internal one
+4. **Ada vs Machiavelli** — Ada abstracts toward formal purity. Machiavelli anchors in messy human incentives
+5. **Torvalds vs Watts** — Torvalds ships concrete solutions. Watts questions whether the problem even exists
+6. **Musashi vs Torvalds** — Musashi waits for the perfect moment. Torvalds says ship it now
+
+These pairs prevent groupthink. When all 11 convene, every position has a structural opponent. When you pick a triad of 3, you are selecting specific tensions — not just expertise areas.
+
+# The 3-round deliberation protocol
+
+The council does not just poll 11 opinions. It runs a structured 3-round protocol that forces engagement.
+
+## Round 1: Independent analysis (parallel)
+
+All selected members receive the problem and produce a standalone analysis.
+
+400-word maximum per member.
+
+Each follows their agent-specific output template:
+
+Essential question, domain analysis, verdict, confidence level, and where they might be wrong.
+
+Every member runs as a parallel subagent. A 3-member triad completes round 1 in one parallel batch.
+
+## Round 2: Cross-examination (sequential)
+
+Each member receives an all-around 1 output and must answer 4 prompts:
+
+1. Which position do you most disagree with, and why?
+2. Which insight from another member strengthens your own position?
+3. What, if anything, changed your view?
+4. Restate your position
+
+300-word maximum. Must engage at least 2 other members by name.
+
+Sequential execution so later members can reference earlier cross-examinations.
+
+This is where the real value emerges. Feynman does not just state his view — he has to explain why he disagrees with Socrates.
+
+## Round 3: Synthesis
+
+Each member states their final position in 100 words or fewer. No new arguments. Crystallization only.
+
+Socrates gets exactly one question, then must state his position. This is the convergence gate that prevents the dialectic from spiraling.
+
+## Anti-recursion enforcement
+
+Socrates is the most dangerous member. His method is questioning, which means he can loop forever without committing to a position.
+
+Three enforcement mechanisms prevent this:
+
+The **hemlock rule**: if Socrates re-asks a question that another member has already addressed with evidence, the coordinator forces a 50-word position statement.
+
+No more questions.
+
+The **3-level depth limit**: question a premise, question the response, question once more. After 3 levels, Socrates must state his own position.
+
+The **2-message cutoff**: if any pair of members exchanges more than 2 messages, the coordinator cuts them off and forces round 3.
+
+These rules exist because the first version had no recursion limits. Socrates and Feynman would enter a questioning loop that consumed the entire context window.
+
+no conclusion. just questions.
+
+## Tie-breaking and consensus rules
+
+The council uses 3 decision rules:
+
+- **2/3 majority** = consensus. The dissenting position is recorded in a Minority Report section
+- No **majority** = the dilemma is presented to the user with each position clearly stated. The council does not force artificial consensus
+- **Domain expert weighting**: the member whose domain most directly matches the problem gets 1.5x weight
+
+The minority report matters. Sometimes the dissenting view is the most valuable output.
+
+It surfaces the risk that the majority position misses.
+
+## Pre-defined triads for common domains
+
+You do not need all 11 members for every question. The council ships 11 pre-built triads optimized for specific problem domains:
+
+```text
+DOMAIN TRIAD WHY
+─────────────────────────────────────────────────────────
+architecture Aristotle + Ada + Feynman classify → formalize → simplicity-test
+strategy Sun Tzu + Machiavelli + Aurelius terrain → incentives → moral grounding
+ethics Aurelius + Socrates + Lao Tzu duty → questioning → natural order
+debugging Feynman + Socrates + Ada bottom-up → assumptions → formal verify
+innovation Ada + Lao Tzu + Aristotle abstraction → emergence → classification
+conflict Socrates + Machiavelli + Aurelius expose → predict → ground
+complexity Lao Tzu + Aristotle + Ada emergence → categories → formalism
+risk Sun Tzu + Aurelius + Feynman threats → resilience → empirical verify
+shipping Torvalds + Musashi + Feynman pragmatism → timing → first-principles
+product Torvalds + Machiavelli + Watts ship it → incentives → reframing
+founder Musashi + Sun Tzu + Torvalds timing → terrain → engineering reality
+```
+
+Invocation:
+
+```text
+/council --triad architecture "should we split the monolith now or after Series B?"
+/council --full "is this acquisition worth pursuing at 8x revenue?"
+/council --members socrates,feynman,ada "why does our cache invalidation keep failing?"
+```
+
+\--triad selects a pre-built group.
+
+\--full convenes all 11.
+
+\--members lets you pick any 2-11 members manually.
+
+No flag auto-detects the domain from your question and selects the matching triad \[E7\].
+
+## What a council session produces
+
+The output is not 11 separate opinions. It is a structured verdict:
+
+```text
+## Council Verdict
+
+### Problem
+should we migrate from REST to GraphQL for the mobile API?
+
+### Council Composition
+Architecture triad: Aristotle, Ada, Feynman
+
+### Consensus Position
+migrate the mobile-facing read endpoints to GraphQL.
+keep REST for write operations and internal services.
+
+### Key Insights by Member
+- Aristotle: the problem is a category error — "migrate to GraphQL"
+ treats it as binary when the real question is which access
+ patterns benefit from graph traversal
+- Ada: GraphQL's type system gives you compile-time guarantees
+ on the client that REST cannot match — but only for reads
+- Feynman: the performance cost of GraphQL resolvers on write-heavy
+ paths is not theoretical. measure it. if N+1 queries appear
+ in staging, the abstraction is hiding real cost
+
+### Points of Agreement
+- mobile clients benefit from flexible queries (reduce over-fetching)
+- write operations do not benefit from GraphQL's query model
+- incremental migration is safer than full rewrite
+
+### Points of Disagreement
+- Aristotle wants a formal boundary definition before any migration
+- Feynman wants a prototype with real latency measurements first
+
+### Minority Report
+none — consensus reached on hybrid approach
+
+### Recommended Next Steps
+1. prototype GraphQL gateway for 2 highest-traffic read endpoints
+2. measure latency delta against current REST implementation
+3. if delta is under 15ms p99, proceed with mobile read migration
+```
+
+The key difference from asking a single model: You get the disagreements explicitly.
+
+Aristotle's "category error" reframe and Feynman's "measure it first" constraint are both surfaced. A single model averages these into one confident recommendation. The council keeps them separate so you can decide.
+
+## Installation
+
+The council is a Claude Code skill. Installation takes 30 seconds:
+
+```text
+git clone https://github.com/0xNyk/council-of-high-intelligence.git
+cd council-of-high-intelligence
+./install.sh
+```
+
+this copies 11 agent definitions to ~/.claude/agents/ and the coordinator skill to~/.claude/skills/council/SKILL.md.
+
+Manual installation if you prefer:
+
+```text
+cp agents/council-*.md ~/.claude/agents/
+mkdir -p ~/.claude/skills/council
+cp SKILL.md ~/.claude/skills/council/SKILL.md
+```
+
+Requires Claude Code CLI with agent subagent support. CC0 licensed — use it however you want.
+
+## When to use the council vs when not to
+
+Use the council for complex decisions where trade-offs are real.
+
+Architecture choices. Strategic pivots. Build-vs-buy. Pricing models.
+
+Do **not** use it for questions with clear, correct answers.
+
+Do **not** convene 11 thinkers to debate TypeScript vs JavaScript.
+
+Do not use **\--full** when a triad covers the domain.
+
+11 members consume significant context and API cost.
+
+**The sweet spot**: Decisions where you already have an opinion but suspect you are missing something. The council surfaces what you are not seeing — structured, with the disagreements visible.
+
+What is the last decision you made where a single confident answer hid the real trade-offs?
+
+## Repository licensed under CC0 (public domain), github.com/0xNyk/council-of-high-intelligence
+
+I started a private Telegram channel where I’ll be sharing insights and updates regularly:
+
+[https://t.me/+GJ-FEpzcZrtmMTky](https://t.me/+GJ-FEpzcZrtmMTky)
\ No newline at end of file
diff --git a/Claude/Anatomy of the .claude folder.md b/Claude/Anatomy of the .claude folder.md
new file mode 100644
index 0000000..bf74336
--- /dev/null
+++ b/Claude/Anatomy of the .claude folder.md
@@ -0,0 +1,419 @@
+---
+title: "Anatomy of the .claude/ folder"
+source: "https://x.com/akshay_pachaar/status/2035341800739877091"
+author:
+ - "[[Unknown]]"
+published: 2026-03-21
+created: 2026-03-22
+description:
+tags:
+ - "clippings"
+---
+**A complete guide to CLAUDE.md, custom commands, skills, agents, and permissions, and how to set them up properly.**
+
+Most Claude Code users treat the **.claude folder** like a black box. They know it exists. They've seen it appear in their project root. But they've never opened it, let alone understood what every file inside it does.
+
+That's a missed opportunity.
+
+**The .claude folder is the control center for how Claude behaves in your project.** It holds your instructions, your custom commands, your permission rules, and even Claude's memory across sessions. Once you understand what lives where and why, you can configure Claude Code to behave exactly the way your team needs it to.
+
+This guide walks through the entire anatomy of the folder, from the files you'll use daily to the ones you'll set once and forget.
+
+# Two folders, not one
+
+Before diving in, one thing worth knowing upfront: there are actually two .claude directories, not one.
+
+The first lives inside your project and the second lives in your home directory:
+
+
+
+**The project-level folder holds team configuration.** You commit it to git. Everyone on the team gets the same rules, the same custom commands, the same permission policies.
+
+The global **~/.claude/** folder holds your personal preferences and machine-local state like session history and auto-memory.
+
+# CLAUDE.md: Claude's instruction manual
+
+This is the most important file in the entire system. When you start a Claude Code session, the first thing it reads is **CLAUDE.md**. It loads it straight into the system prompt and keeps it in mind for the entire conversation.
+
+Simply put: **whatever you write in CLAUDE.md, Claude will follow.**
+
+If you tell Claude to always write tests before implementation, it will. If you say "never use console.log for error handling, always use the custom logger module," it will respect that every time.
+
+A **CLAUDE.md** at your project root is the most common setup. But you can also have one in **~/.claude/CLAUDE.md** for global preferences that apply across all projects, and even one inside subdirectories for folder-specific rules. Claude reads all of them and combines them.
+
+What actually belongs in CLAUDE.md
+
+Most people either write too much or too little. Here's what works.
+
+## Write:
+
+- Build, test, and lint commands (npm run test, make build, etc.)
+- Key architectural decisions ("we use a monorepo with Turborepo")
+- Non-obvious gotchas ("TypeScript strict mode is on, unused variables are errors")
+- Import conventions, naming patterns, error handling styles
+- File and folder structure for the main modules
+
+## Don't write:
+
+- Anything that belongs in a linter or formatter config
+- Full documentation you can already link to
+- Long paragraphs explaining theory
+
+Keep CLAUDE.md under 200 lines. Files longer than that start eating too much context, and Claude's instruction adherence actually drops.
+
+Here's a minimal but effective example:
+
+```plaintext
+# Project: Acme API
+
+## Commands
+npm run dev # Start dev server
+npm run test # Run tests (Jest)
+npm run lint # ESLint + Prettier check
+npm run build # Production build
+
+## Architecture
+- Express REST API, Node 20
+- PostgreSQL via Prisma ORM
+- All handlers live in src/handlers/
+- Shared types in src/types/
+
+## Conventions
+- Use zod for request validation in every handler
+- Return shape is always { data, error }
+- Never expose stack traces to the client
+- Use the logger module, not console.log
+
+## Watch out for
+- Tests use a real local DB, not mocks. Run \`npm run db:test:reset\` first
+- Strict TypeScript: no unused imports, ever
+```
+
+That's ~20 lines. It gives Claude everything it needs to work productively in this codebase without constant clarification.
+
+# CLAUDE.local.md for personal overrides
+
+Sometimes you have a preference that's specific to you, not the whole team. Maybe you prefer a different test runner, or you want Claude to always open files using a specific pattern.
+
+Create CLAUDE.local.md in your project root. Claude reads it alongside the main CLAUDE.md, and it's automatically gitignored so your personal tweaks never land in the repo.
+
+
+
+# The rules/ folder: modular instructions that scale
+
+CLAUDE.md works great for a single project. But once your team grows, you end up with a 300-line CLAUDE.md that nobody maintains and everyone ignores.
+
+The rules/ folder solves that.
+
+**Every markdown file inside .claude/rules/ gets loaded alongside your CLAUDE.md automatically.** Instead of one giant file, you split instructions by concern:
+
+```plaintext
+.claude/rules/
+├── code-style.md
+├── testing.md
+├── api-conventions.md
+└── security.md
+```
+
+Each file stays focused and easy to update. The team member who owns API conventions edits api-conventions.md. The person who owns testing standards edits testing.md. Nobody stomps on each other.
+
+The real power comes from **path-scoped rules**. Add a YAML frontmatter block to a rule file and it only activates when Claude is working with matching files:
+
+```markdown
+---
+paths:
+ - "src/api/**/*.ts"
+ - "src/handlers/**/*.ts"
+---
+# API Design Rules
+
+- All handlers return { data, error } shape
+- Use zod for request body validation
+- Never expose internal error details to clients
+```
+
+Claude won't load this file when it's editing a React component. It only loads when it's working inside src/api/ or src/handlers/. Rules without a paths field load unconditionally, every session.
+
+This is the right pattern once your CLAUDE.md starts feeling crowded.
+
+# The commands/ folder: your custom slash commands
+
+Out of the box, Claude Code has built-in slash commands like **/help** and **/compact**. The **commands/** folder lets you add your own.
+
+**Every markdown file you drop into .claude/commands/ becomes a slash command.**
+
+A file named **review.md** creates **/project:review**. A file named fix-issue.md creates **/project:fix-issue**. The filename is the command name.
+
+
+
+Here's a simple example. Create **.claude/commands/review.md**:
+
+```markdown
+---
+description: Review the current branch diff for issues before merging
+---
+## Changes to Review
+
+!\`git diff --name-only main...HEAD\`
+
+## Detailed Diff
+
+!\`git diff main...HEAD\`
+
+Review the above changes for:
+1. Code quality issues
+2. Security vulnerabilities
+3. Missing test coverage
+4. Performance concerns
+
+Give specific, actionable feedback per file.
+```
+
+Now run **/project:review** in Claude Code and it automatically injects the real git diff into the prompt before Claude sees it. The **!** backtick syntax runs shell commands and embeds the output. That's what makes these commands genuinely useful instead of just saved text.
+
+## Passing arguments to commands
+
+Use [$ARGUMENTS](https://x.com/search?q=%24ARGUMENTS&src=cashtag_click) to pass text after the command name:
+
+```markdown
+---
+description: Investigate and fix a GitHub issue
+argument-hint: [issue-number]
+---
+Look at issue #$ARGUMENTS in this repo.
+
+!\`gh issue view $ARGUMENTS\`
+
+Understand the bug, trace it to the root cause, fix it, and write a
+test that would have caught it.
+```
+
+Running **/project:fix-issue 234** feeds issue 234's content directly into the prompt.
+
+## Personal vs. project commands
+
+Project commands in **.claude/commands/** are committed and shared with your team. For commands you want everywhere regardless of project, put them in **~/.claude/commands/**. Those show up as **/user:command-name** instead.
+
+A useful personal command: a daily standup helper, a command for generating commit messages following your convention, or a quick security scan.
+
+# The skills/ folder: reusable workflows on demand
+
+You now know how commands work. Skills look similar on the surface, but the trigger is fundamentally different. Here's the distinction before we go any further:
+
+
+
+**Skills are workflows that Claude can invoke on its own**, without you typing a slash command, when the task matches the skill's description. Commands wait for you. Skills watch the conversation and act when the moment is right.
+
+Each skill lives in its own subdirectory with a SKILL.md file:
+
+```markdown
+.claude/skills/
+├── security-review/
+│ ├── SKILL.md
+│ └── DETAILED_GUIDE.md
+└── deploy/
+ ├── SKILL.md
+ └── templates/
+ └── release-notes.md
+```
+
+The SKILL.md uses YAML frontmatter to describe when to use it:
+
+```markdown
+---
+name: security-review
+description: Comprehensive security audit. Use when reviewing code for
+ vulnerabilities, before deployments, or when the user mentions security.
+allowed-tools: Read, Grep, Glob
+---
+Analyze the codebase for security vulnerabilities:
+
+1. SQL injection and XSS risks
+2. Exposed credentials or secrets
+3. Insecure configurations
+4. Authentication and authorization gaps
+
+Report findings with severity ratings and specific remediation steps.
+Reference @DETAILED_GUIDE.md for our security standards.
+```
+
+When you say "review this PR for security issues," Claude reads the description, recognizes it matches, and invokes the skill automatically. You can also call it explicitly with **/security-review**.
+
+**The key difference from commands:** skills can bundle supporting files alongside them. The [@DETAILED\_GUIDE](https://x.com/@DETAILED_GUIDE).md reference above pulls in a detailed document that lives right next to SKILL.md. Commands are single files. Skills are packages.
+
+Personal skills go in **~/.claude/skills/** and are available across all your projects.
+
+# The agents/ folder: specialized subagent personas
+
+When a task is complex enough to benefit from a dedicated specialist, you can define a subagent persona in .claude/agents/. Each agent is a markdown file with its own system prompt, tool access, and model preference:
+
+```plaintext
+.claude/agents/
+├── code-reviewer.md
+└── security-auditor.md
+```
+
+Here's what a code-reviewer.md looks like:
+
+```markdown
+---
+name: code-reviewer
+description: Expert code reviewer. Use PROACTIVELY when reviewing PRs,
+ checking for bugs, or validating implementations before merging.
+model: sonnet
+tools: Read, Grep, Glob
+---
+You are a senior code reviewer with a focus on correctness and maintainability.
+
+When reviewing code:
+- Flag bugs, not just style issues
+- Suggest specific fixes, not vague improvements
+- Check for edge cases and error handling gaps
+- Note performance concerns only when they matter at scale
+```
+
+When Claude needs a code review done, it spawns this agent in its own isolated context window. The agent does its work, compresses the findings, and reports back. Your main session doesn't get cluttered with thousands of tokens of intermediate exploration.
+
+The tools field restricts what the agent can do. A security auditor only needs Read, Grep, and Glob. It has no business writing files. That restriction is intentional and worth being explicit about.
+
+The model field lets you use a cheaper, faster model for focused tasks. Haiku handles most read-only exploration well. Save Sonnet and Opus for the work that actually needs them.
+
+Personal agents go in **~/.claude/agents/** and are available across all projects.
+
+
+
+# settings.json: permissions and project config
+
+The **settings.json** file inside **.claude/** controls what Claude is and isn't allowed to do. It's where you define which tools Claude can run, which files it can read, and whether it needs to ask before running certain commands.
+
+The complete file looks like this:
+
+```json
+{
+ "$schema": "https://json.schemastore.org/claude-code-settings.json",
+ "permissions": {
+ "allow": [
+ "Bash(npm run *)",
+ "Bash(git status)",
+ "Bash(git diff *)",
+ "Read",
+ "Write",
+ "Edit"
+ ],
+ "deny": [
+ "Bash(rm -rf *)",
+ "Bash(curl *)",
+ "Read(./.env)",
+ "Read(./.env.*)"
+ ]
+ }
+}
+```
+
+## Here's what each part does.
+
+**The** [$schema](https://x.com/search?q=%24schema&src=cashtag_click) **line** enables autocomplete and inline validation in VS Code or Cursor. Always include it.
+
+**The allow list** contains commands that run without Claude asking for confirmation. For most projects, a good allow list covers:
+
+- Bash(npm run \*) or Bash(make \*) so Claude can run your scripts freely
+- Bash(git \*) for read-only git commands
+- Read, Write, Edit, Glob, Grep for file operations
+
+**The deny list** contains commands that are blocked entirely, no matter what. A sensible deny list blocks:
+
+- Destructive shell commands like rm -rf
+- Direct network commands like curl
+- Sensitive files like .env and anything in secrets/
+
+**If something isn't in either list, Claude asks before proceeding.** That middle ground is intentional. It gives you a safety net without having to anticipate every possible command upfront.
+
+## settings.local.json for personal overrides
+
+Same idea as **CLAUDE.local.md**. Create **.claude/settings.local.json** for permission changes you don't want committed. It's auto-gitignored.
+
+# The global ~/.claude/ folder
+
+You don't interact with this folder often, but it's useful to know what's in it.
+
+**~/.claude/CLAUDE.md** loads into every Claude Code session, across all your projects. Good place for your personal coding principles, preferred style, or anything you want Claude to remember regardless of which repo you're in.
+
+**~/.claude/projects/** stores session transcripts and auto-memory per project. Claude Code automatically saves notes to itself as it works: commands it discovers, patterns it observes, architecture insights. These persist across sessions. You can browse and edit them with /memory.
+
+**~/.claude/commands/** and **~/.claude/skills/** hold personal commands and skills available across all projects.
+
+You generally don't need to manually manage these. But knowing they exist is handy when Claude seems to "remember" something you never told it, or when you want to wipe a project's auto-memory and start fresh.
+
+## The full picture
+
+Here's how everything comes together:
+
+```plaintext
+your-project/
+├── CLAUDE.md # Team instructions (committed)
+├── CLAUDE.local.md # Your personal overrides (gitignored)
+│
+└── .claude/
+ ├── settings.json # Permissions + config (committed)
+ ├── settings.local.json # Personal permission overrides (gitignored)
+ │
+ ├── commands/ # Custom slash commands
+ │ ├── review.md # → /project:review
+ │ ├── fix-issue.md # → /project:fix-issue
+ │ └── deploy.md # → /project:deploy
+ │
+ ├── rules/ # Modular instruction files
+ │ ├── code-style.md
+ │ ├── testing.md
+ │ └── api-conventions.md
+ │
+ ├── skills/ # Auto-invoked workflows
+ │ ├── security-review/
+ │ │ └── SKILL.md
+ │ └── deploy/
+ │ └── SKILL.md
+ │
+ └── agents/ # Specialized subagent personas
+ ├── code-reviewer.md
+ └── security-auditor.md
+
+~/.claude/
+├── CLAUDE.md # Your global instructions
+├── settings.json # Your global settings
+├── commands/ # Your personal commands (all projects)
+├── skills/ # Your personal skills (all projects)
+├── agents/ # Your personal agents (all projects)
+└── projects/ # Session history + auto-memory
+```
+
+# A practical setup to get started
+
+If you're starting from scratch, here's a progression that works well.
+
+**Step 1.** Run /init inside Claude Code. It generates a starter CLAUDE.md by reading your project. Edit it down to the essentials.
+
+**Step 2.** Add .claude/settings.json with allow/deny rules appropriate for your stack. At minimum, allow your run commands and deny .env reads.
+
+**Step 3.** Create one or two commands for the workflows you do most. Code review and issue fixing are good starting points.
+
+**Step 4.** As your project grows and your CLAUDE.md gets crowded, start splitting instructions into .claude/rules/ files. Scope them by path where it makes sense.
+
+**Step 5.** Add a ~/.claude/CLAUDE.md with your personal preferences. This might be something like "always write types before implementations" or "prefer functional patterns over class-based."
+
+That's genuinely all you need for 95% of projects. Skills and agents come in when you have recurring complex workflows worth packaging up.
+
+## The key insight
+
+The **.claude folder** is really a protocol for telling Claude who you are, what your project does, and what rules it should follow. The more clearly you define that, the less time you spend correcting Claude and the more time it spends doing useful work.
+
+**CLAUDE.md is your highest-leverage file.** Get that right first. Everything else is optimization.
+
+Start small, refine as you go, and treat it like any other piece of infrastructure in your project: something that pays dividends every day once it's set up properly.
+
+That's a wrap!
+
+If you enjoyed reading this.
+
+Find me → [@akshay\_pachaar](https://x.com/@akshay_pachaar) ✔️
+
+Every day, I share tutorials and insights on AI, Machine Learning and vibe coding best practices.
\ No newline at end of file
diff --git a/Claude/CLI 参考.md b/Claude/CLI 参考.md
new file mode 100644
index 0000000..00de2c5
--- /dev/null
+++ b/Claude/CLI 参考.md
@@ -0,0 +1,151 @@
+---
+title: "CLI 参考"
+source: "https://code.claude.com/docs/zh-CN/cli-reference"
+author:
+published:
+created: 2026-06-30
+description: "Claude Code 命令行界面的完整参考，包括命令和标志。"
+tags:
+ - "clippings"
+---
+## CLI 命令
+
+您可以使用这些命令启动会话、管道内容、恢复对话和管理更新：
+
+| 命令 | 描述 | 示例 |
+| --- | --- | --- |
+| `claude` | 启动交互式会话 | `claude` |
+| `claude "query"` | 使用初始提示启动交互式会话 | `claude "explain this project"` |
+| `claude -p "query"` | 通过 SDK 查询，然后退出 | `claude -p "explain this function"` |
+| `cat file \| claude -p "query"` | 处理管道内容 | `cat logs.txt \| claude -p "explain"` |
+| `claude -c` | 在当前目录中继续最近的对话 | `claude -c` |
+| `claude -c -p "query"` | 通过 SDK 继续 | `claude -c -p "Check for type errors"` |
+| `claude -r "" "query"` | 按 ID 或名称恢复会话 | `claude -r "auth-refactor" "Finish this PR"` |
+| `claude update` | 更新到最新版本 | `claude update` |
+| `claude install [version]` | 安装或重新安装本机二进制文件。接受版本号如 `2.1.118` 、 `stable` 或 `latest` 。请参阅 [安装特定版本](https://code.claude.com/docs/zh-CN/setup#install-a-specific-version) | `claude install stable` |
+| `claude auth login` | 登录您的 Anthropic 账户。使用 `--email` 预填充您的电子邮件地址，使用 `--sso` 强制 SSO 身份验证，使用 `--console` 使用 Anthropic Console 登录以进行 API 使用计费而不是 Claude 订阅 | `claude auth login --console` |
+| `claude auth logout` | 从您的 Anthropic 账户登出 | `claude auth logout` |
+| `claude auth status` | 以 JSON 格式显示身份验证状态。使用 `--text` 获取人类可读的输出。如果已登录，则以代码 0 退出，如果未登录，则以代码 1 退出 | `claude auth status` |
+| `claude agents` | 打开 [agent view](https://code.claude.com/docs/zh-CN/agent-view) 以监控和分派并行后台会话。使用 `--cwd ` 仅显示在该目录下启动的会话，或使用 `--json` 将实时会话打印为 JSON 数组以供脚本使用（ `--json --all` 也包括已完成的后台会话）。传递 `--permission-mode` 、 `--model` 、 `--effort` 或 `--agent` 以设置 [分派会话的默认值](https://code.claude.com/docs/zh-CN/agent-view#permission-mode-model-and-effort) 。接受 `--settings` 、 `--add-dir` 、 `--plugin-dir` 和 `--mcp-config` ，如顶级 `claude` 命令。打开 agent view 需要交互式终端 | `claude agents --json` |
+| `claude attach ` | 在此终端中附加到 [后台会话](https://code.claude.com/docs/zh-CN/agent-view#manage-sessions-from-the-shell) | `claude attach 7c5dcf5d` |
+| `claude auto-mode defaults` | 以 JSON 格式打印内置 [auto mode](https://code.claude.com/docs/zh-CN/permission-modes#eliminate-prompts-with-auto-mode) 分类器规则。使用 `claude auto-mode config` 查看应用了设置的有效配置 | `claude auto-mode defaults > rules.json` |
+| `claude daemon status` | 打印后台会话 [supervisor](https://code.claude.com/docs/zh-CN/agent-view#the-supervisor-process) 的状态、版本、套接字目录和工作进程数以进行诊断。如果 supervisor 未运行，则退出代码 1 | `claude daemon status` |
+| `claude daemon stop --any` | 停止后台会话 [supervisor](https://code.claude.com/docs/zh-CN/agent-view#the-supervisor-process) 及其托管的会话。传递 `--keep-workers` 以保持后台会话运行，以便下一个 supervisor 重新连接到它们。 `--any` 确认停止按需 supervisor，这是默认值。使用此命令从 [无响应的 supervisor](https://code.claude.com/docs/zh-CN/agent-view#agent-view-says-the-background-service-did-not-respond) 恢复 | `claude daemon stop --any --keep-workers` |
+| `claude logs ` | 从 [后台会话](https://code.claude.com/docs/zh-CN/agent-view#manage-sessions-from-the-shell) 打印最近的输出 | `claude logs 7c5dcf5d` |
+| `claude mcp` | 配置 Model Context Protocol (MCP) 服务器 | 请参阅 [Claude Code MCP 文档](https://code.claude.com/docs/zh-CN/mcp) 。 |
+| `claude mcp login ` | 运行配置的 MCP 服务器的 OAuth 流程而不打开交互式 `/mcp` 面板。适用于 HTTP、SSE 和 claude.ai 连接器服务器。在 SSH 上添加 `--no-browser` 以打印授权 URL 而不是打开浏览器，然后在提示处粘贴重定向 URL。需要 Claude Code v2.1.186 或更高版本。请参阅 [从命令行进行身份验证](https://code.claude.com/docs/zh-CN/mcp#authenticate-from-the-command-line) | `claude mcp login sentry` |
+| `claude mcp logout ` | 清除 MCP 服务器的存储 OAuth 凭据。需要 Claude Code v2.1.186 或更高版本 | `claude mcp logout sentry` |
+| `claude plugin` | 管理 Claude Code [plugins](https://code.claude.com/docs/zh-CN/plugins) 。别名： `claude plugins` 。请参阅 [plugin 参考](https://code.claude.com/docs/zh-CN/plugins-reference#cli-commands-reference) 了解子命令 | `claude plugin install code-review@claude-plugins-official` |
+| `claude project purge [path]` | 删除项目的所有本地 Claude Code 状态：记录、任务列表、调试日志、文件编辑历史、提示历史行和项目在 `~/.claude.json` 中的条目。省略 `[path]` 以从交互式列表中选择。标志： `--dry-run` 预览， `-y` / `--yes` 跳过确认， `-i` / `--interactive` 确认每一项， `--all` 用于每个项目。请参阅 [清除本地数据](https://code.claude.com/docs/zh-CN/claude-directory#clear-local-data) | `claude project purge ~/work/repo --dry-run` |
+| `claude remote-control` | 启动 [Remote Control](https://code.claude.com/docs/zh-CN/remote-control) 服务器以从 Claude.ai 或 Claude 应用控制 Claude Code。在服务器模式下运行（无本地交互式会话）。请参阅 [服务器模式标志](https://code.claude.com/docs/zh-CN/remote-control#start-a-remote-control-session) | `claude remote-control --name "My Project"` |
+| `claude respawn ` | 重启 [后台会话](https://code.claude.com/docs/zh-CN/agent-view#manage-sessions-from-the-shell) ，运行或已停止，保持其对话完整。使用 `--all` 重启每个运行中的会话，例如以获取更新的 Claude Code 二进制文件 | `claude respawn 7c5dcf5d` |
+| `claude rm ` | 从列表中删除 [后台会话](https://code.claude.com/docs/zh-CN/agent-view#manage-sessions-from-the-shell) 。对话记录保留在您的本地计算机上，可通过 `claude --resume` 访问 | `claude rm 7c5dcf5d` |
+| `claude setup-token` | 为 CI 和脚本生成长期 OAuth 令牌。将令牌打印到终端而不保存。需要 Claude 订阅。请参阅 [生成长期令牌](https://code.claude.com/docs/zh-CN/authentication#generate-a-long-lived-token) | `claude setup-token` |
+| `claude stop ` | 停止 [后台会话](https://code.claude.com/docs/zh-CN/agent-view#manage-sessions-from-the-shell) 。也接受 `claude kill` | `claude stop 7c5dcf5d` |
+| `claude ultrareview [target]` | 非交互式运行 [ultrareview](https://code.claude.com/docs/zh-CN/ultrareview#run-ultrareview-non-interactively) 。将发现结果打印到标准输出，成功时退出代码 0，失败时退出代码 1。使用 `--json` 获取原始有效负载，使用 `--timeout ` 覆盖 30 分钟的默认值 | `claude ultrareview 1234 --json` |
+
+如果您输入错误的子命令，Claude Code 会建议最接近的匹配项并退出而不启动会话。例如， `claude udpate` 会打印 `Did you mean claude update?`。
+
+## CLI 标志
+
+使用这些命令行标志自定义 Claude Code 的行为。 `claude --help` 不会列出每个标志，因此标志在 `--help` 中的缺失并不意味着它不可用。
+
+| 标志 | 描述 | 示例 |
+| --- | --- | --- |
+| `--add-dir` | 为 Claude 添加额外的工作目录以读取和编辑文件。授予文件访问权限；大多数 `.claude/` 配置 [不会从这些目录中发现](https://code.claude.com/docs/zh-CN/permissions#additional-directories-grant-file-access-not-configuration) 。验证每个路径是否存在为目录。要在会话间持久化这些目录，请在设置中设置 [`permissions.additionalDirectories`](https://code.claude.com/docs/zh-CN/settings#permission-settings) | `claude --add-dir ../apps ../lib` |
+| `--advisor ` | 为此会话启用服务器端 [advisor tool](https://code.claude.com/docs/zh-CN/advisor) ，使用模型别名： `opus` 、 `sonnet` 或 `fable` （v2.1.170+），或完整模型 ID。优先于会话的 `advisorModel` 设置。需要 Claude Code v2.1.98 或更高版本 | `claude --advisor opus` |
+| `--agent` | 为当前会话指定代理（覆盖 `agent` 设置） | `claude --agent my-custom-agent` |
+| `--agents` | 通过 JSON 动态定义自定义 subagents。使用与 subagent [frontmatter](https://code.claude.com/docs/zh-CN/sub-agents#supported-frontmatter-fields) 相同的字段名称，加上代理指令的 `prompt` 字段 | `claude --agents '{"reviewer":{"description":"Reviews code","prompt":"You are a code reviewer"}}'` |
+| `--allow-dangerously-skip-permissions` | 将 `bypassPermissions` 添加到 `Shift+Tab` 模式循环中而不启动它。允许您以不同的模式（如 `plan` ）开始，稍后切换到 `bypassPermissions` 。请参阅 [权限模式](https://code.claude.com/docs/zh-CN/permission-modes#skip-all-checks-with-bypasspermissions-mode) | `claude --permission-mode plan --allow-dangerously-skip-permissions` |
+| `--allowedTools`, `--allowed-tools` | 无需提示权限即可执行的工具。请参阅 [权限规则语法](https://code.claude.com/docs/zh-CN/settings#permission-rule-syntax) 了解模式匹配。要限制哪些工具可用，请改用 `--tools` | `"Bash(git log *)" "Bash(git diff *)" "Read"` |
+| `--append-system-prompt` | 将自定义文本附加到默认系统提示的末尾 | `claude --append-system-prompt "Always use TypeScript"` |
+| `--append-system-prompt-file` | 从文件加载额外的系统提示文本并附加到默认提示 | `claude --append-system-prompt-file ./extra-rules.txt` |
+| `--ax-screen-reader` | 渲染屏幕阅读器友好的输出：没有装饰性边框或动画的平面文本。强制使用经典渲染器，因此 [`tui`](https://code.claude.com/docs/zh-CN/settings#available-settings) 设置在会话期间无效。优先于 [`CLAUDE_AX_SCREEN_READER`](https://code.claude.com/docs/zh-CN/env-vars) 和 [`axScreenReader`](https://code.claude.com/docs/zh-CN/settings#available-settings) 设置。需要 Claude Code v2.1.181 或更高版本 | `claude --ax-screen-reader` |
+| `--bare` | 最小模式：跳过 hooks、skills、plugins、MCP 服务器、自动内存和 CLAUDE.md 的自动发现，以便脚本化调用启动更快。Claude 可以访问 Bash、文件读取和文件编辑工具。设置 [`CLAUDE_CODE_SIMPLE`](https://code.claude.com/docs/zh-CN/env-vars) 。请参阅 [bare mode](https://code.claude.com/docs/zh-CN/headless#start-faster-with-bare-mode) | `claude --bare -p "query"` |
+| `--betas` | 要包含在 API 请求中的 Beta 标头（仅限 API 密钥用户） | `claude --betas interleaved-thinking` |
+| `--bg`, `--background` | 启动会话作为 [后台代理](https://code.claude.com/docs/zh-CN/agent-view) 并立即返回。打印会话 ID 和管理命令。与 `--exec` 结合以作为后台作业运行 shell 命令而不是 Claude 会话，或与 `--agent` 结合以运行特定的 subagent | `claude --bg "investigate the flaky test"` |
+| `--channels` | （研究预览）MCP 服务器，其 [channel](https://code.claude.com/docs/zh-CN/channels) 通知 Claude 应在此会话中侦听。以空格分隔的 `plugin:@` 条目列表。需要 Claude.ai 身份验证 | `claude --channels plugin:my-notifier@my-marketplace` |
+| `--chrome` | 启用 [Chrome 浏览器集成](https://code.claude.com/docs/zh-CN/chrome) 以进行网络自动化和测试 | `claude --chrome` |
+| `--continue`, `-c` | 加载当前目录中最近的对话。包括使用 `/add-dir` 添加此目录的会话 | `claude --continue` |
+| `--dangerously-load-development-channels` | 启用不在批准的允许列表中的 [channels](https://code.claude.com/docs/zh-CN/channels-reference#test-during-the-research-preview) ，用于本地开发。接受 `plugin:@` 和 `server:` 条目。提示确认 | `claude --dangerously-load-development-channels server:webhook` |
+| `--dangerously-skip-permissions` | 跳过权限提示。等同于 `--permission-mode bypassPermissions` 。请参阅 [权限模式](https://code.claude.com/docs/zh-CN/permission-modes#skip-all-checks-with-bypasspermissions-mode) 了解此操作跳过和不跳过的内容 | `claude --dangerously-skip-permissions` |
+| `--debug` | 启用调试模式，可选类别过滤（例如， `"api,hooks"` 或 `"!statsig,!file"` ） | `claude --debug "api,mcp"` |
+| `--debug-file ` | 将调试日志写入特定文件路径。隐式启用调试模式。优先于 `CLAUDE_CODE_DEBUG_LOGS_DIR` | `claude --debug-file /tmp/claude-debug.log` |
+| `--disable-slash-commands` | 为此会话禁用所有 skills 和命令 | `claude --disable-slash-commands` |
+| `--disallowedTools`, `--disallowed-tools` | 拒绝规则。裸工具名称从模型的上下文中删除匹配的工具： `"Edit"` 删除 Edit， `"*"` 删除每个工具， `"mcp__*"` 删除每个 MCP 工具。作用域规则（如 `Bash(rm *)` ）使工具保持可用，仅拒绝匹配的调用 | `"Bash(git log *)" "Bash(git diff *)" "Edit"` |
+| `--effort` | 为当前会话设置 [工作量级别](https://code.claude.com/docs/zh-CN/model-config#adjust-effort-level) 。选项： `low` 、 `medium` 、 `high` 、 `xhigh` 、 `max` ；可用级别取决于模型。覆盖此会话的 [`effortLevel`](https://code.claude.com/docs/zh-CN/settings#available-settings) 设置，不会持久化 | `claude --effort high` |
+| `--enable-auto-mode` | 在 v2.1.111 中移除。Auto mode 现在默认在 `Shift+Tab` 循环中；使用 `--permission-mode auto` 以它开始 | `claude --permission-mode auto` |
+| `--exclude-dynamic-system-prompt-sections` | 将每台机器的部分从系统提示（工作目录、环境信息、内存路径、git 状态）移到第一条用户消息中。改进在运行相同任务的不同用户和机器之间的提示缓存重用。仅适用于默认系统提示；当设置 `--system-prompt` 或 `--system-prompt-file` 时忽略。与 `-p` 一起用于脚本化的多用户工作负载 | `claude -p --exclude-dynamic-system-prompt-sections "query"` |
+| `--exec` | 运行 shell 命令作为 PTY 支持的后台作业而不是启动 Claude 会话。与 `--bg` 一起使用以从 shell 启动 | `claude --bg --exec 'pytest -x'` |
+| `--fallback-model` | 当主模型过载或不可用时启用自动回退到指定的模型，例如已停用的模型。接受逗号分隔的列表，按顺序尝试。请参阅 [Fallback model chains](https://code.claude.com/docs/zh-CN/model-config#fallback-model-chains) 。要在会话间持久化链，请使用 [`fallbackModel` 设置](https://code.claude.com/docs/zh-CN/settings#available-settings) ，此标志会覆盖它 | `claude --fallback-model sonnet,haiku` |
+| `--fork-session` | 恢复时，创建新的会话 ID 而不是重用原始 ID（与 `--resume` 或 `--continue` 一起使用） | `claude --resume abc123 --fork-session` |
+| `--from-pr` | 恢复链接到特定拉取请求的会话。接受 PR 号、GitHub 或 GitHub Enterprise PR URL、GitLab 合并请求 URL 或 Bitbucket 拉取请求 URL。当 Claude 创建拉取请求时会自动链接会话 | `claude --from-pr 123` |
+| `--ide` | 如果恰好有一个有效的 IDE 可用，则在启动时自动连接到 IDE | `claude --ide` |
+| `--init` | 在会话前运行带有 `init` 匹配器的 [Setup hooks](https://code.claude.com/docs/zh-CN/hooks#setup) （仅打印模式） | `claude -p --init "query"` |
+| `--init-only` | 运行 [Setup](https://code.claude.com/docs/zh-CN/hooks#setup) 和 `SessionStart` hooks，然后退出而不启动对话 | `claude --init-only` |
+| `--include-hook-events` | 在输出流中包含所有 hook 生命周期事件。需要 `--output-format stream-json` | `claude -p --output-format stream-json --verbose --include-hook-events "query"` |
+| `--include-partial-messages` | 在输出中包含部分流事件。需要 `--print` 和 `--output-format stream-json` | `claude -p --output-format stream-json --verbose --include-partial-messages "query"` |
+| `--input-format` | 为打印模式指定输入格式（选项： `text` 、 `stream-json` ） | `claude -p --output-format json --input-format stream-json` |
+| `--json-schema` | 在代理完成其工作流后获得与 JSON Schema 匹配的验证 JSON 输出（仅打印模式，请参阅 [结构化输出](https://code.claude.com/docs/zh-CN/agent-sdk/structured-outputs) ） | `claude -p --json-schema '{"type":"object","properties":{...}}' "query"` |
+| `--maintenance` | 在会话前运行带有 `maintenance` 匹配器的 [Setup hooks](https://code.claude.com/docs/zh-CN/hooks#setup) （仅打印模式） | `claude -p --maintenance "query"` |
+| `--max-budget-usd` | API 调用前停止的最大美元金额（仅打印模式） | `claude -p --max-budget-usd 5.00 "query"` |
+| `--max-turns` | 限制代理转数（仅打印模式）。达到限制时以错误退出。默认无限制 | `claude -p --max-turns 3 "query"` |
+| `--mcp-config` | 从 JSON 文件或字符串加载 MCP 服务器（以空格分隔） | `claude --mcp-config ./mcp.json` |
+| `--model` | 为当前会话设置模型，使用最新模型的别名（ `sonnet` 、 `opus` 、 `haiku` 或 `fable` ）或模型的完整名称。覆盖 [`model`](https://code.claude.com/docs/zh-CN/settings#available-settings) 设置和 [`ANTHROPIC_MODEL`](https://code.claude.com/docs/zh-CN/model-config#environment-variables) | `claude --model claude-sonnet-4-6` |
+| `--name`, `-n` | 为会话设置显示名称，显示在 `/resume` 和终端标题中。您可以使用 `claude --resume ` 恢复命名会话。 [`/rename`](https://code.claude.com/docs/zh-CN/commands) 在会话中更改名称，也会在提示栏中显示 | `claude -n "my-feature-work"` |
+| `--no-chrome` | 为此会话禁用 [Chrome 浏览器集成](https://code.claude.com/docs/zh-CN/chrome) | `claude --no-chrome` |
+| `--no-session-persistence` | 禁用会话持久化，以便会话不会保存到磁盘且无法恢复。仅打印模式。 [`CLAUDE_CODE_SKIP_PROMPT_HISTORY`](https://code.claude.com/docs/zh-CN/env-vars) 环境变量在任何模式下都做同样的事情 | `claude -p --no-session-persistence "query"` |
+| `--output-format` | 为打印模式指定输出格式（选项： `text` 、 `json` 、 `stream-json` ） | `claude -p "query" --output-format json` |
+| `--permission-mode` | 以指定的 [权限模式](https://code.claude.com/docs/zh-CN/permission-modes) 开始。接受 `default` 、 `acceptEdits` 、 `plan` 、 `auto` 、 `dontAsk` 或 `bypassPermissions` 。覆盖设置文件中的 `defaultMode` | `claude --permission-mode plan` |
+| `--permission-prompt-tool` | 指定 MCP 工具以在非交互模式下处理权限提示 | `claude -p --permission-prompt-tool mcp_auth_tool "query"` |
+| `--plugin-dir` | 仅为此会话从目录或 `.zip` 存档加载插件。每个标志采用一个路径。重复该标志以获取多个插件： `--plugin-dir A --plugin-dir B.zip` | `claude --plugin-dir ./my-plugin` |
+| `--plugin-url` | 仅为此会话从 URL 获取插件 `.zip` 存档。重复该标志以获取多个插件，或在单个引用值中传递以空格分隔的 URL | `claude --plugin-url https://example.com/plugin.zip` |
+| `--print`, `-p` | 打印响应而不进入交互模式（请参阅 [Agent SDK 文档](https://code.claude.com/docs/zh-CN/agent-sdk/overview) 了解编程使用详情） | `claude -p "query"` |
+| `--prompt-suggestions` | 在每轮后发出 `prompt_suggestion` 消息，带有预测的下一个用户提示。需要 `--print` 、 `--output-format stream-json` 和 `--verbose` 。请参阅 [提示建议](https://code.claude.com/docs/zh-CN/interactive-mode#prompt-suggestions) | `claude -p --prompt-suggestions --output-format stream-json --verbose "query"` |
+| `--remote` | 在 claude.ai 上创建新的 [网络会话](https://code.claude.com/docs/zh-CN/claude-code-on-the-web) ，提供任务描述 | `claude --remote "Fix the login bug"` |
+| `--remote-control`, `--rc` | 启动启用了 [Remote Control](https://code.claude.com/docs/zh-CN/remote-control#start-a-remote-control-session) 的交互式会话，以便您也可以从 claude.ai 或 Claude 应用控制它。可选地为会话传递名称 | `claude --remote-control "My Project"` |
+| `--remote-control-session-name-prefix ` | 当未设置显式名称时， [Remote Control](https://code.claude.com/docs/zh-CN/remote-control) 自动生成会话名称的前缀。默认为您的机器的主机名，生成名称如 `myhost-graceful-unicorn` 。设置 `CLAUDE_REMOTE_CONTROL_SESSION_NAME_PREFIX` 以获得相同效果 | `claude remote-control --remote-control-session-name-prefix dev-box` |
+| `--replay-user-messages` | 从 stdin 重新发出用户消息到 stdout 以进行确认。需要 `--input-format stream-json` 和 `--output-format stream-json` | `claude -p --input-format stream-json --output-format stream-json --verbose --replay-user-messages` |
+| `--resume`, `-r` | 按 ID 或名称恢复特定会话，或显示交互式选择器以选择会话。选择器和名称搜索包括使用 `/add-dir` 添加此目录的会话；传递会话 ID 仅搜索当前项目目录及其 git worktrees。截至 v2.1.144， [后台会话](https://code.claude.com/docs/zh-CN/agent-view) 在选择器中显示，标记为 `bg` | `claude --resume auth-refactor` |
+| `--safe-mode` | 以所有自定义禁用的状态启动以排查损坏的配置：CLAUDE.md、skills、plugins、hooks、MCP 服务器、自定义命令和代理、输出样式、工作流、自定义主题、自定义快捷键、状态行和文件建议命令、LSP 服务器和自动内存不加载。身份验证、模型选择、内置工具和权限正常工作，这与 [`--bare`](https://code.claude.com/docs/zh-CN/headless#start-faster-with-bare-mode) 不同。托管设置策略仍然适用，包括策略配置的 hooks、状态行和文件建议命令；托管插件、托管 skills、托管 CLAUDE.md 和策略配置的 MCP 服务器不适用。用于检查自定义是否触发 [从 Fable 5 自动回退](https://code.claude.com/docs/zh-CN/model-config#automatic-model-fallback) 。设置 [`CLAUDE_CODE_SAFE_MODE`](https://code.claude.com/docs/zh-CN/env-vars) | `claude --safe-mode` |
+| `--session-id` | 为对话使用特定的会话 ID（必须是有效的 UUID） | `claude --session-id "550e8400-e29b-41d4-a716-446655440000"` |
+| `--setting-sources` | 逗号分隔的设置源列表以加载（ `user` 、 `project` 、 `local` ） | `claude --setting-sources user,project` |
+| `--settings` | 设置 JSON 文件的路径或内联 JSON 字符串。您在此处设置的值会覆盖此会话的 `settings.json` 文件中的相同键。您省略的键保留其基于文件的值。请参阅 [设置优先级](https://code.claude.com/docs/zh-CN/settings#settings-precedence) | `claude --settings ./settings.json` |
+| `--strict-mcp-config` | 仅使用来自 `--mcp-config` 的 MCP 服务器，忽略所有其他 MCP 配置 | `claude --strict-mcp-config --mcp-config ./mcp.json` |
+| `--system-prompt` | 用自定义文本替换整个系统提示 | `claude --system-prompt "You are a Python expert"` |
+| `--system-prompt-file` | 从文件加载系统提示，替换默认提示 | `claude --system-prompt-file ./custom-prompt.txt` |
+| `--teleport` | 在本地终端中恢复 [网络会话](https://code.claude.com/docs/zh-CN/claude-code-on-the-web) | `claude --teleport` |
+| `--teammate-mode` | 设置 [agent team](https://code.claude.com/docs/zh-CN/agent-teams) 队友的显示方式： `in-process` （默认）、 `auto` 、 `tmux` 或 } `iterm2` （在 v2.1.186 中添加）。默认值在 v2.1.179 中从 `auto` 更改。覆盖此会话的 [`teammateMode`](https://code.claude.com/docs/zh-CN/settings#available-settings) 设置。请参阅 [选择显示模式](https://code.claude.com/docs/zh-CN/agent-teams#choose-a-display-mode) | `claude --teammate-mode auto` |
+| `--tmux` | 为 worktree 创建 tmux 会话。需要 `--worktree` 。在可用时使用 iTerm2 原生窗格；传递 `--tmux=classic` 以使用传统 tmux | `claude -w feature-auth --tmux` |
+| `--tools` | 限制 Claude 可以使用的内置工具。使用 `""` 禁用所有， `"default"` 表示全部，或工具名称如 `"Bash,Edit,Read"` 。MCP 工具不受影响；要拒绝这些工具，请改用 `--disallowedTools "mcp__*"` ，或传递 `--strict-mcp-config` 而不带 `--mcp-config` 以便不加载 MCP 服务器 | `claude --tools "Bash,Edit,Read"` |
+| `--verbose` | 启用详细日志记录，显示完整的逐轮输出。覆盖此会话的 [`viewMode`](https://code.claude.com/docs/zh-CN/settings#available-settings) 设置 | `claude --verbose` |
+| `--version`, `-v` | 输出版本号 | `claude -v` |
+| `--worktree`, `-w` | 在隔离的 [git worktree](https://code.claude.com/docs/zh-CN/worktrees) 中启动 Claude，位于 `/.claude/worktrees/` 。如果未给出名称，则自动生成一个。传递 `#` 或 GitHub 拉取请求 URL 以从 `origin` 获取该 PR 并从其分支 worktree | `claude -w feature-auth` |
+
+### 系统提示标志
+
+Claude Code 提供四个标志用于自定义系统提示。所有四个都在交互和非交互模式下工作。
+
+| 标志 | 行为 | 示例 |
+| --- | --- | --- |
+| `--system-prompt` | 替换整个默认提示 | `claude --system-prompt "You are a Python expert"` |
+| `--system-prompt-file` | 用文件内容替换 | `claude --system-prompt-file ./prompts/review.txt` |
+| `--append-system-prompt` | 附加到默认提示 | `claude --append-system-prompt "Always use TypeScript"` |
+| `--append-system-prompt-file` | 将文件内容附加到默认提示 | `claude --append-system-prompt-file ./style-rules.txt` |
+
+`--system-prompt` 和 `--system-prompt-file` 互斥。附加标志可以与任一替换标志组合。
+
+根据 Claude Code 的默认身份是否仍然适合您的任务来选择。当 Claude 应该保持编码助手身份同时遵循您的额外规则时，使用附加标志：每次调用的指令、输出格式或 `-p` 脚本的域上下文。附加保留默认工具指导、安全指令和编码约定，因此您只需提供不同的部分。当表面、身份或权限模型与 Claude Code 的不同时，使用替换标志，例如管道中没有人监视的非编码代理。替换会删除整个默认提示，包括工具指导和安全指令，因此您需要对任务仍然需要的任何内容负责。
+
+这些标志仅适用于当前调用。对于可以在项目中切换和共享的持久化角色，请使用 [输出样式](https://code.claude.com/docs/zh-CN/output-styles) 。对于 Claude 应该始终遵循的项目约定，请使用 [CLAUDE.md](https://code.claude.com/docs/zh-CN/memory) 。 [Agent SDK 系统提示指南](https://code.claude.com/docs/zh-CN/agent-sdk/modifying-system-prompts#decide-on-a-starting-point) 更深入地涵盖了相同的决策。
+
+## 另请参阅
+
+- [Chrome 扩展](https://code.claude.com/docs/zh-CN/chrome) - 浏览器自动化和网络测试
+- [交互模式](https://code.claude.com/docs/zh-CN/interactive-mode) - 快捷键、输入模式和交互功能
+- [快速入门指南](https://code.claude.com/docs/zh-CN/quickstart) - Claude Code 入门
+- [常见工作流](https://code.claude.com/docs/zh-CN/common-workflows) - 高级工作流和模式
+- [设置](https://code.claude.com/docs/zh-CN/settings) - 配置选项
+- [Agent SDK 文档](https://code.claude.com/docs/zh-CN/agent-sdk/overview) - 编程使用和集成
+
+[命令](https://code.claude.com/docs/zh-CN/commands)
\ No newline at end of file
diff --git a/Claude/Claude + Obsidian The Memory Stack That Compounds.md b/Claude/Claude + Obsidian The Memory Stack That Compounds.md
new file mode 100644
index 0000000..777febe
--- /dev/null
+++ b/Claude/Claude + Obsidian The Memory Stack That Compounds.md
@@ -0,0 +1,292 @@
+---
+title: "Claude + Obsidian: The Memory Stack That Compounds"
+source: "https://x.com/nyk_builderz/status/2030904887186514336"
+author:
+ - "[[@nyk_builderz]]"
+published: 2026-03-09
+created: 2026-04-24
+description: "Teams burn 30-40 minutes per Claude session re-explaining what they already knew yesterday. That's a full workday per week lost to repetitio..."
+tags:
+ - "clippings"
+---
+
+
+Teams burn 30-40 minutes per Claude session re-explaining what they already knew yesterday. That's a full workday per week lost to repetition.
+
+Everything is a context problem. When people say AI can't do real work, what they're actually saying is that they gave it bad context.
+
+Every session starts from zero. Your architecture, your conventions, that edge case it debugged for an hour — gone when the window closes. Everyone says the fix is bigger context, but the real failure isn't reasoning, it's drift.
+
+I wired Claude into an Obsidian vault with a three-tier memory system. Sessions stopped resetting. Output compounds instead of plateauing.
+
+
+
+Here's the checklist, every config file, and the exact runbook. save this
+
+## The Memory Problem Nobody Talks About
+
+Cowan's research places the limit of active attention at 4 chunks, plus or minus 1. Not words, not tokens — chunks of meaningful structure.
+
+A 200k context window doesn't change this constraint. It changes how much raw text the model can scan. Scanning is not knowing.
+
+I call this context amnesia — your AI is intelligent, even brilliant, but it has no continuity. Every session IS a first date.
+
+The symptoms are predictable:
+
+- It re-asks questions you've answered before
+- It proposes patterns you've explicitly rejected
+- It loses track of decisions made three sessions ago
+- It treats your codebase like it's seeing it for the first time
+
+Coding was solved first because the structure was already there. Codebases were text files with relationships between them. The agent reads the code, follows the imports, and understands the architecture. That was natural because this is how programmers already worked with code.
+
+Knowledge work doesn't have that structure yet. Mostly outdated knowledge bases or wikis nobody reads.
+
+But some people were building it anyway. The obsidian and tools for thought nerds spent years figuring out how to structure knowledge. Notes that link to each other, ideas that are atomic and composable, maps of content that give you the topology of an entire domain,
+
+Turns out they accidentally engineered the perfect architecture for LLMs,
+
+The fix isn't a smarter model. It's giving the model a memory system so that it can operate.
+
+## The 3-Layer Memory Architecture
+
+Here's the structure. Each layer solves a different failure mode
+
+```text
+┌─────────────────────────────────────────┐
+│ Layer 3: Ingestion Pipeline │
+│ video/audio → structured knowledge │
+├─────────────────────────────────────────┤
+│ Layer 2: Knowledge Graph │
+│ obsidian vault + mcp bridge │
+├─────────────────────────────────────────┤
+│ Layer 1: Session Memory │
+│ CLAUDE.md + auto-memory directory │
+└─────────────────────────────────────────┘
+```
+
+Layer 1 teaches the model who you are. Layer 2 gives it a searchable brain. Layer 3 feeds that brain from the real world.
+
+They compound.
+
+Skip one, and the others degrade.
+
+## Layer 1: Session Memory
+
+You're not configuring a tool. You are teaching a colleague what you've already decided.
+
+CLAUDE.md is where this starts. It's the first thing Claude reads in every session. Most people treat it like a config file. The teams that get the most leverage treat it like a teaching document.
+
+Here's the framing we use in ours:
+
+```text
+This vault is your exosuit
+
+When you join this session, you put on
+the accumulated knowledge of the entire organization
+
+You are not an assistant
+
+You are the driving force behind a company
+that compounds knowledge into competitive advantage
+```
+
+what belongs in CLAUDE.md:
+
+- architecture decisions that don't change weekly
+- naming conventions and code patterns
+- workflow preferences (which tools, which package managers)
+- explicit boundaries (what to never do)
+
+Then there's the auto-memory directory. This is where Claude persists in observations across sessions — patterns it noticed, solutions that worked, and things you corrected it on.
+
+```text
+~/.claude/projects//memory/
+├── MEMORY.md # always loaded into context
+├── debugging.md # solutions to recurring problems
+├── patterns.md # confirmed codebase conventions
+├── architecture.md # key architectural decisions
+└── preferences.md # user workflow preferences
+```
+
+The rule: MEMORY.md stays under 200 lines. It's a routing document, not a dump. Detailed notes go in topic files and get linked from MEMORY.md.
+
+This is the cheapest layer to implement, and it changes the feel of every session, immediately. But it has a ceiling — it can only hold what fits in context at session start.
+
+That's where layer 2 comes in.
+
+## Layer 2: Knowledge Graph
+
+Do we call it vibe note-taking? maybe. But there's a deeper point.
+
+An Obsidian vault is not a note-taking app. It's a persistence layer your AI can query at runtime. The system IS the files.
+
+You don't need Obsidian for knowledge management with Claude Code — you need markdown files with wikilinks in folders. Obsidian is one good interface for that.
+
+The graph is what matters.
+
+The bridge between Claude and your vault is the MCP — model context protocol. Two servers make this work:
+
+> smart-connections — semantic search over your entire vault. Claude can find relevant notes even when it doesn't know the exact title or path
+
+> qmd — structured queries, collection management, and metadata operations. The precision tool for when Claude needs specific notes by path or tag
+
+Here's the MCP config that wires them in:
+
+```text
+{
+ "mcpServers": {
+ "smart-connections": {
+ "command": "python",
+ "args": ["smart-connections-mcp/server.py"],
+ "env": {
+ "OBSIDIAN_VAULT_PATH": "~/obsidian/your-vault"
+ }
+ },
+ "qmd": {
+ "command": "qmd",
+ "args": ["mcp"],
+ "env": {
+ "HOME": "~"
+ }
+ },
+ "obsidian": {
+ "command": "npx",
+ "args": ["-y", "obsidian-mcp"]
+ }
+ }
+}
+```
+
+But the bridge is only as good as what's on the other side. Vault structure matters A LOT. An unstructured dump of notes doesn't scale.
+
+You need wikilinks as semantic connections, atomic composable markdown notes, maps of content for navigation and attention management, metadata for queries, and progressive disclosure.
+
+I use four levels of filtering so retrieval finds the right depth on the first pass:
+
+```text
+obsidian-vault/
+├── 00-home/ # maps of content
+│ ├── index.md
+│ ├── daily/
+│ └── top-of-mind.md
+├── atlas/ # structural overview
+│ ├── projects.md
+│ ├── research.md
+│ └── vault-information-arch.md
+├── inbox/ # unprocessed captures
+│ └── queue-generated/
+├── knowledge/ # curated knowledge graph
+│ ├── graph/
+│ │ ├── agent-daily/
+│ │ ├── research/
+│ │ └── repo-research/
+│ └── memory/
+├── sessions/ # raw session transcripts
+└── voice-notes/ # transcribed voice captures
+```
+
+The key insight is prose-as-title. Notes are named as claims, not categories
+
+Not memory-systems.md but memory graphs beat giant memory files.md
+
+Not retrieval-notes.md, but hybrid retrieval outperforms pure semantic search.md
+
+When Claude searches the vault, the result titles alone tell it whether a note is relevant — before it reads a single line of content
+
+Links work the same way. wiki-link-as-prose — links that read as sentences:
+
+we learned that \[\[memory graphs beat giant memory files\]\] when we \[\[benchmark retrieval like search infrastructure\]\]
+
+The graph becomes self-documenting. retrieval becomes navigation.
+
+## Layer 3: Ingestion Pipeline
+
+The first two layers give Claude a brain and a way to access it. But brains need feeding.
+
+Most knowledge doesn't start as text in your vault. It starts as a YouTube video, a conference talk, a voice memo, a podcast episode. The gap between "I watched something useful" and "Claude can find it" is where most knowledge dies.
+
+The hardest knowledge to capture isn't in documents, it's in people's heads.
+
+When your CTO decides on PostgreSQL over MongoDB, maybe the decision gets written down. But the reasoning, the tradeoffs she considered, the context that made it obvious to her but invisible to everyone else — that's lost.
+
+Meetings used to be the ultimate time sink, but now you can record conversations, an agent mines them exhaustively, and the tacit knowledge locked in people's heads becomes a structured graph state. This is not about meeting summaries nobody reads; it's active synchronization with your thinking and the externalized representation of your thinking.
+
+Brain-ingest closes the gap. One command:
+
+brain-ingest "[https://youtube.com/watch?v=](https://youtube.com/watch?v=)..." --apply
+
+```text
+brain-ingest "https://youtube.com/watch?v=..." --apply
+```
+
+What happens under the hood:
+
+1. downloads and transcribes locally (no data leaves your machine)
+2. extracts structured knowledge: claims, frameworks, action items
+3. generates an Obsidian note with proper frontmatter and wiki-links
+4. drops it into your vault's inbox for review
+
+A raw transcript is noise — thousands of filler words, false starts, repetition. A structured knowledge note is a signal
+
+From a single 90-minute conference talk, brain-ingest typically extracts:
+
+- 12-18 distinct claims worth preserving
+- 3-5 named frameworks or mental models
+- 5-8 actionable techniques
+- 2-4 concrete examples with context
+
+That's the material that compounds when Claude retrieves it six weeks later during an unrelated problem.
+
+You can also feed it local files and raw transcripts:
+
+```text
+brain-ingest "/path/to/recording.mp4" --apply
+brain-ingest --transcript "/path/to/notes.txt" --title "Team Retro" --apply
+```
+
+## The graph improves itself
+
+This is the part that changes everything:
+
+Agents don't get bored with maintenance, and they don't skip the update because they're late for a meeting. The thing that killed every wiki is the exact thing agents are built for:
+
+The agent notices when two notes contradict each other and flags the tension. It notices when the spec is out of sync with your codebase. While working, friction signals accumulate automatically, and when enough observations pile up, the agent proposes structural changes to the system itself.
+
+It refactors its own instructions. It evolves its own architecture when the current one creates too much drag.
+
+Btw, humans have externalized knowledge for thousands of years, this is what really enabled progress. Each medium — cave paintings, parchment, books, digital information — let the next generation build on what came before instead of starting from scratch.
+
+Agents live in context windows like humans live in lifespans. They are temporary, bounded and forget everything when the session ends. They need externalized knowledge for the same reason we needed writing: to transcend the limits of individual memory.
+
+A knowledge graph is an agent's library. Every session, it picks up the accumulated knowledge of the entire organization and operates from there.
+
+## The Setup Checklist
+
+Copy this. Do it in order:
+
+1. Create CLAUDE.md in your project/system root with architecture decisions, conventions, and boundaries
+2. Enable auto-memory in Claude Code settings — let it persist observations across sessions
+3. Set up an Obsidian vault with the folder structure above (or adapt to your existing vault)
+4. Install Smart-Connections MCP server — pip install smart-connections-mcp
+5. Install qmd MCP server — npx -y [@tobilu/qmd](https://x.com/@tobilu/qmd) mcp
+6. Add the MCP config JSON to your Claude settings
+7. Run brain-ingest on your last 3 most valuable video/audio sources
+8. Set up LACP hooks for session orientation and quality gates — [github.com/0xNyk/lacp](https://github.com/0xNyk/lacp) (coming soon)
+9. Commit to the session rhythm: orient → work → persist
+
+The first session after setup feels different. Not because Claude got smarter. Because it stopped forgetting
+
+## Closing
+
+Memory is not a feature. It's an operating system for attention
+
+What happened to software development with vibe coding is about to happen to knowledge work. 2025 was agents writing code, 2026 is agents disrupting knowledge work.
+
+The best context window in the world can't replace a system that compounds what it learns.
+
+Your AI is already a graph traverser. The question is whether you gave it a graph worth traversing.
+
+I started a private Telegram channel where I’ll be sharing insights and updates regularly: [https://t.me/+GJ-FEpzcZrtmMTky](https://t.me/+GJ-FEpzcZrtmMTky)
+
+What's your memory stack? Drop your setup below 👇
\ No newline at end of file
diff --git a/Claude/Claude Code + SKILL.md build your first auto-invoking AI workflow in 15 minutes.md b/Claude/Claude Code + SKILL.md build your first auto-invoking AI workflow in 15 minutes.md
new file mode 100644
index 0000000..b20b227
--- /dev/null
+++ b/Claude/Claude Code + SKILL.md build your first auto-invoking AI workflow in 15 minutes.md
@@ -0,0 +1,202 @@
+---
+title: "Claude Code + SKILL.md: build your first auto-invoking AI workflow in 15 minutes"
+source: "https://x.com/nyk_builderz/status/2048115264680829238"
+author:
+ - "[[@nyk_builderz]]"
+published: 2026-04-26
+created: 2026-04-26
+description: "I took the official docs, the top community skills (superpowers at 96K stars, gstack at 20K stars), and my own experience building skills fo..."
+tags:
+ - "clippings"
+---
+
+
+I took the official docs, the top community skills (superpowers at 96K stars, gstack at 20K stars), and my own experience building skills for the x-article-factory pipeline - and put together the guide I wish existed when I started.
+
+This is a long article. At the end of it, you will have a working skill. To help you navigate, here are the 7 sections:
+
+1. What a skill actually is
+2. The SKILL.md structure
+3. Your first skill in 5 minutes
+4. Auto-invocation (the magic part)
+5. Adding templates and supporting files
+6. 5 beginner skill types to start with
+7. The mistakes that waste your time
+
+Let's get into it.
+
+## 1: What a skill actually is
+
+A skill is a markdown file that teaches Claude Code how to do something it could not do before, or prevents a failure mode that costs you time every session.
+
+That is it.
+
+Not a plugin. Not a framework. Not an API integration. A markdown file in a folder.
+
+When Claude encounters a task that matches the skill's description, it loads the skill automatically and follows the instructions. You do not need to invoke it manually. You do not need to write a prompt. The skill fires when relevant.
+
+**Skills vs commands vs agents:**
+
+- **commands** Wait for you to type /command-name. They are manual triggers.
+- **Agents** are specialized subagent personas that run in their own context window.
+- **skills** Watch the conversation and activate when the task matches. They are automatic.
+
+The difference matters. Commands are buttons. Agents are specialists. Skills are instincts.
+
+## 2: The SKILL.md structure
+
+Every skill lives in its own directory with a SKILL.md file as the entry point.
+
+The SKILL.md has two parts: frontmatter and instructions.
+
+```text
+---
+name: security-review
+description: Comprehensive security audit. Use when reviewing
+ code for vulnerabilities, before deployments, or when the
+ user mentions security.
+allowed-tools: Read, Grep, Glob
+---
+Analyze the codebase for security vulnerabilities:
+
+1. SQL injection and XSS risks
+2. Exposed credentials or secrets
+3. Insecure configurations
+4. Authentication gaps
+
+Report findings with severity ratings and specific fixes.
+Reference @DETAILED_GUIDE.md for our security standards.
+```
+
+The **description is everything.** Claude reads it to decide whether to activate the skill. If the description is vague ("help with code"), the skill fires too often. If it is too narrow ("only for Python 3.11 FastAPI handlers"), it never fires.
+
+The **sweet spot:** Describe the trigger condition, not the skill itself. "Use when reviewing code for vulnerabilities" is better than "security review tool."
+
+## 3: Your first skill in 5 minutes
+
+Let's build one. Right now.
+
+The most useful beginner skill: a code review skill that checks for the problems your team actually has.
+
+Step **1:** Create the directory.
+
+```text
+mkdir -p .claude/skills/code-review
+```
+
+Step **2:** Write the SKILL.md.
+
+```text
+---
+name: code-review
+description: Expert code reviewer. Use PROACTIVELY when
+ reviewing PRs, checking for bugs, or before merging.
+allowed-tools: Read, Grep, Glob
+---
+You are a senior code reviewer focused on correctness.
+
+When reviewing code:
+- Flag bugs, not style issues
+- Suggest specific fixes, not vague improvements
+- Check for edge cases and error handling gaps
+- Note performance concerns only at scale
+
+Output format:
+## Critical (must fix)
+## Warnings (should fix)
+## Notes (consider)
+```
+
+Step **3:** Restart Claude Code or start a new session.
+
+Step **4:** Ask Claude to review some code. It will automatically load the skill.
+
+That is a working skill. Total time: under 5 minutes.
+
+## 4: auto-invocation (the magic part)
+
+This is where skills become powerful.
+
+Claude reads the description field in the frontmatter and decides whether to activate the skill based on the current task. You never type /code-review. Claude sees you working on a PR and loads the skill because the description matches.
+
+How **to control when skills fire:**
+
+Make the description specific. "Use when the user asks to review a pull request or when checking code before merging" fires on PR reviews. "Use for code" fires on everything.
+
+The **allowed-tools field** restricts what the skill can do. A security review skill should only Read, Grep, and Glob — it should not Write or Edit files. This is your safety net.
+
+**Path-scoped activation.** Add a paths field to the frontmatter:
+
+```text
+---
+paths:
+ - "src/api/**/*.ts"
+ - "src/handlers/**/*.ts"
+---
+```
+
+Now the skill only activates when Claude is working with files that match those patterns. An API conventions skill that only fires for API files. A test standards skill that only fires for test files.
+
+## 5: Adding templates and supporting files
+
+Skills can bundle supporting files alongside them. This is what makes them packages, not just prompts.
+
+```text
+.claude/skills/security-review/
+├── SKILL.md
+├── DETAILED_GUIDE.md # referenced with @
+├── templates/
+│ └── security-report.md # output template
+└── examples/
+ └── good-review.md # example output
+```
+
+Reference files with [@FILENAME](https://x.com/@FILENAME).md in the skill instructions. Claude will load them when the skill activates.
+
+When **to add supporting files:**
+
+- The skill needs a detailed guide that would bloat the SKILL.md
+- You want a consistent output template
+- You have example outputs that show "what good looks like"
+
+When **not to:** If the SKILL.md is under 50 lines, you probably do not need supporting files. Keep it simple.
+
+## 6: 5 beginner skill types to start with
+
+Do not try to build a complex multi-file skill on day one. Start with one of these.
+
+**1\. Review skill.** Checks code for specific problems. The example above. Easiest to build, highest immediate value.
+
+**2\. Debugging skill.** Forces a methodology before Claude starts guessing. "reproduce → isolate → hypothesize → verify" prevents the "guess and apply" loop that creates new bugs.
+
+**3\. Documentation skill.** Generates docs in your team's format. Give it a template, let it fill it in. Consistency without manual effort.
+
+**4\. Test-first skill.** Forces Claude to write the test before the implementation. Reverses the default behavior of "write code, then backfill tests that pass by definition."
+
+**5\. Deploy skill.** Runs your deployment checklist. Version bump, changelog, build, test, tag, push. Automates the sequence you always do manually.
+
+Start with whichever matches your biggest daily friction. Install one tonight.
+
+## 7: The mistakes that waste your time
+
+Mistake **1: descriptions** are **too broad.** "help with coding" matches everything and adds noise to every session. Be specific about the trigger.
+
+**mistake 2: too many instructions.** A 200-line SKILL.md eats context without adding proportional value. If you cannot say it in 50 lines, split it into a SKILL.md and a [@DETAILED\_GUIDE](https://x.com/@DETAILED_GUIDE).md.
+
+**mistake 3: no examples.** Claude follows examples better than rules. If you want a specific output format, show it — do not just describe it.
+
+**mistake 4: skipping allowed-tools.** Without tool restrictions, a skill can read, write, edit, and execute anything. Always specify the minimum tools needed.
+
+**mistake 5: building multi-file skills before single-file skills work.** Master the basics. Add complexity only when the simple version fails.
+
+## You can now build a skill
+
+The most important thing I learned: a skill is not code. It is a behavioral contract written in markdown. The better you define the trigger, the instructions, and the output format, the more reliably Claude delivers.
+
+Start with one skill. Test it on 5 real tasks. Improve one thing. Then build the next one.
+
+Personal skills go in ~/.claude/skills/ (available in every project). Project skills go in .claude/skills/ (shared with your team via git).
+
+The skill marketplace has 80,000+ entries. But the one you write yourself — for your specific workflow, your specific gotchas, your specific team — will always outperform a generic download.
+
+What is the one task Claude keeps getting wrong that a 20-line skill would fix?
\ No newline at end of file
diff --git a/Claude/Claude Code Masterclass The Complete Guide From Zero to Shipping.md b/Claude/Claude Code Masterclass The Complete Guide From Zero to Shipping.md
new file mode 100644
index 0000000..1eeb0b4
--- /dev/null
+++ b/Claude/Claude Code Masterclass The Complete Guide From Zero to Shipping.md
@@ -0,0 +1,264 @@
+---
+title: "Claude Code Masterclass: The Complete Guide From Zero to Shipping"
+source: "https://x.com/cyrilXBT/status/2047710536981176777"
+author:
+ - "[[@cyrilXBT]]"
+published: 2026-04-25
+created: 2026-04-26
+description: "I am going to tell you something that took me months to figure out.Claude Code is not a coding assistant.That framing is what keeps most peo..."
+tags:
+ - "clippings"
+---
+
+
+I am going to tell you something that took me months to figure out.
+
+Claude Code is not a coding assistant.
+
+That framing is what keeps most people stuck at the surface. They open it, ask it to write a function, get the function, close it. They think they used Claude Code.
+
+They did not.
+
+Claude Code is an autonomous agent that lives in your terminal and can build, test, deploy, and maintain entire software projects with minimal supervision. The gap between using it as a coding assistant and using it as an autonomous builder is the gap between getting occasional help and having an engineer who works around the clock.
+
+This guide closes that gap completely. Every concept. Every command. Every workflow. From installation to shipping your first real product.
+
+## Part One: Installation and Setup
+
+Installing Claude Code
+
+Claude Code runs in your terminal. You need Node.js version 18 or higher installed first.
+
+Check your Node version:
+
+node --version
+
+If you need to install or update Node, go to [nodejs.org](https://nodejs.org/) and download the current LTS version.
+
+Install Claude Code globally:
+
+npm install -g [@anthropic/claude-code](https://x.com/@anthropic/claude-code)
+
+Verify the installation worked:
+
+claude --version
+
+Setting Up Your API Key
+
+Get your API key from [console.anthropic.com](https://console.anthropic.com/). Create an account if you do not have one. Navigate to API Keys and create a new key.
+
+Set it in your terminal:
+
+export ANTHROPIC\_API\_KEY=your\_key\_here
+
+To make this permanent so you never have to set it again, add it to your shell profile. For most Mac and Linux users:
+
+echo 'export ANTHROPIC\_API\_KEY=your\_key\_here' >> ~/.zshrc source ~/.zshrc
+
+For Windows users using PowerShell:
+
+\[System.Environment\]::SetEnvironmentVariable('ANTHROPIC\_API\_KEY', 'your\_key\_here', 'User')
+
+Launching Claude Code
+
+Navigate to your project directory and launch:
+
+cd your-project-folder claude
+
+Or start a new project from scratch:
+
+mkdir my-new-project cd my-new-project claude
+
+Claude Code will initialize in that directory. Everything it does will be scoped to that folder unless you explicitly tell it otherwise.
+
+## Part Two: The CLAUDE.md File
+
+This is the most important concept in the entire guide.
+
+Most people who struggle with Claude Code have never built a proper CLAUDE.md file. Most people who get extraordinary results from Claude Code have one that is deeply specific and detailed.
+
+The CLAUDE.md file is a markdown document you create at the root of your project. Claude Code reads it every single time it launches in that directory. It is the permanent context that tells Claude Code who you are, what you are building, how your project is structured, and exactly what you want it to do.
+
+Without a CLAUDE.md Claude Code starts every session from zero. With a strong CLAUDE.md it starts every session already deeply briefed on your entire project.
+
+Here is a complete CLAUDE.md template:
+
+\# PROJECT: \[Your Project Name\] ## What This Is \[One paragraph describing exactly what you are building and who it is for\] ## Tech Stack - Frontend: \[React / Next.js / Vue / plain HTML\] - Backend: \[Node / Python / etc\] - Database: \[Supabase / PostgreSQL / MongoDB\] - Styling: \[Tailwind / CSS / SCSS\] - Deployment: \[Vercel / Railway / etc\] ## Project Structure \[Describe your folder structure and what lives where\] src/ components/ — reusable UI components pages/ — route-level page components lib/ — utility functions and helpers api/ — API route handlers ## Coding Standards - \[Your language preferences — TypeScript vs JavaScript\] - \[Error handling approach\] - \[How you name files and functions\] - \[Any patterns you always use\] ## What I Am Building Right Now \[The current feature or task you are working on\] ## What Claude Code Should Never Do - Never modify files in \[protected folders\] - Never read or access .env files - Never push to git without my explicit instruction - Never delete files without confirming with me first ## Important Context \[Anything else Claude Code needs to know about your project\]
+
+Save this as CLAUDE.md in your project root. The more specific you make it the better Claude Code performs.
+
+## Part Three: Core Commands and Workflows
+
+Basic Commands
+
+Once Claude Code is running you interact with it in plain English. But knowing which phrases trigger which behaviors makes you dramatically more effective.
+
+**Starting a new feature:**
+
+Build a user authentication system with email and password login. Use Supabase for the backend. Store sessions in cookies. Create the login page, signup page, and a protected dashboard route.
+
+**Reviewing existing code:**
+
+Read all the files in the src/components folder. Tell me what each component does and identify any code quality issues, duplicate logic, or patterns that should be refactored.
+
+**Fixing a bug:**
+
+There is a bug where \[describe the behavior\]. It happens when \[describe the trigger\]. I expect it to \[describe expected behavior\]. Find the cause and fix it.
+
+**Adding to existing code:**
+
+I want to add \[feature\] to the existing \[component/file\]. Read the current implementation first, then extend it. Do not change anything that is currently working. Only add what is needed for the new feature.
+
+The /compact Command
+
+When your conversation gets long, context fills up and Claude Code starts to lose track of earlier decisions. Run this regularly:
+
+/compact
+
+This summarizes the conversation so far and clears the context while preserving the essential information. Think of it as hitting save on a long session.
+
+The /clear Command
+
+If Claude Code seems confused or is building in the wrong direction, start fresh:
+
+/clear
+
+This completely resets the conversation. Your files are not affected. Only the conversation history is cleared.
+
+Asking Claude Code to Review Its Own Work
+
+One of the most powerful prompts in Claude Code is asking it to evaluate what it just produced:
+
+Review what you just built. Identify any lines of code that seem fragile, any edge cases not handled, any security issues, and any places where the code could break in production. Fix everything you find before we move on.
+
+This self-review prompt catches more issues than most code reviews and costs you nothing but ten seconds to type.
+
+## Part Four: Building Skills
+
+Skills are the most underused feature in Claude Code and the one that makes the biggest difference for power users.
+
+A skill is a markdown file stored in a skills folder inside your project that defines a reusable workflow. Instead of typing out complex instructions every time you want to do the same process, you store the instructions once and call them by name.
+
+Create your skills folder:
+
+mkdir .claude mkdir .claude/skills
+
+Example Skill: Code Review
+
+Create .claude/skills/code-review.md:
+
+\# Skill: Code Review ## Trigger When the user says "review this file" or "code review" or "check this code" ## Process 1. Read the specified file completely before making any comments 2. Evaluate the code against these criteria: - Correctness: does the code do what it claims to do - Security: are there any injection vulnerabilities, exposed secrets, or unsafe operations - Performance: are there obvious inefficiencies or N+1 query patterns - Readability: is the code clear and well-named - Edge cases: what inputs or states could cause this to fail 3. Provide feedback organized by severity: - CRITICAL: must fix before shipping - IMPORTANT: should fix soon - SUGGESTION: would be better with this change 4. For each issue, provide the specific line or section and a suggested fix ## Output format Numbered list organized by severity. For each item: what the issue is, why it matters, and how to fix it. End with a summary score out of 10 and one sentence on overall code quality.
+
+Now whenever you say "review this file" Claude Code runs the full review process exactly as you defined it.
+
+Example Skill: Feature Planning
+
+Create .claude/skills/plan-feature.md:
+
+\# Skill: Plan Feature ## Trigger When the user says "plan this feature" or "help me plan" or "feature planning" ## Process 1. Ask clarifying questions if the feature is ambiguous — do not proceed until you understand what is being built 2. Identify all the files that will need to be created or modified 3. List the potential side effects on existing functionality 4. Identify any dependencies or blockers 5. Break the work into steps ordered by dependency — what must be built first ## Output A numbered implementation plan where: - Each step is specific enough to execute immediately - Dependencies are clearly marked - Estimated complexity is noted (simple / medium / complex) - Any risks or edge cases are flagged upfront
+
+Example Skill: Bug Investigation
+
+Create .claude/skills/debug.md:
+
+\# Skill: Debug ## Trigger When the user says "debug this" or "find the bug" or "this is broken" ## Process 1. Read all files relevant to the reported behavior before forming any hypothesis 2. State your hypothesis about the cause — do not start fixing yet 3. Ask the user to confirm the hypothesis is correct 4. Only after confirmation, implement the fix 5. After fixing, explain what was wrong and why the fix works ## Key rules - Never guess and change code simultaneously. Diagnose first, fix second. - If you are not confident in the diagnosis after reading the files, say so and ask for more information. - Always test the fix mentally — trace through the code after the change to confirm the bug is actually resolved.
+
+## Part Five: Multi-File Projects and Architecture
+
+Where Claude Code becomes genuinely powerful is in its ability to hold large amounts of context and work across multiple files simultaneously.
+
+Reading a Full Codebase
+
+When starting work on an existing project:
+
+Read all the files in this project. Start with the package.json to understand dependencies. Then read the main entry point. Then systematically read through each folder. After reading everything, give me a two paragraph summary of what this codebase does and how it is structured.
+
+This briefing step takes a few minutes but saves hours of confusion later. Claude Code will have a complete mental model of your project before touching anything.
+
+Maintaining Consistency Across Files
+
+When adding a feature that touches multiple files:
+
+I want to add a \[feature\] that will require changes to: - \[file 1\] to add the new data model - \[file 2\] to add the API endpoint - \[file 3\] to add the frontend component - \[file 4\] to add the test Before you write any code, read all four files and tell me what currently exists in each one. Then implement the changes in dependency order, reading each file again immediately before modifying it.
+
+The "read each file immediately before modifying it" instruction is critical. If Claude Code read a file twenty messages ago the information may be stale. Reading immediately before editing ensures accuracy.
+
+Refactoring Large Codebases
+
+Refactoring is where Claude Code significantly outperforms what most developers expect:
+
+This codebase has \[describe the structural problem\]. I want to refactor it to \[describe the target architecture\]. Before making any changes: 1. List every file that will need to change 2. Describe exactly what will change in each file 3. Identify the order of changes to avoid breaking anything mid-refactor 4. Flag any changes that could break existing functionality After I approve the plan, execute it step by step. Show me what you changed in each file before moving to the next one.
+
+## Part Six: Security and Best Practices
+
+Protecting Sensitive Files
+
+Add this to every CLAUDE.md you create:
+
+\## Security Rules - Never read, access, reference, or modify any .env files - Never read any file containing the words "secret", "key", "token", or "password" in the filename - Never commit sensitive information to git - Never log environment variables or API keys - If you encounter hardcoded credentials anywhere in the codebase, flag them immediately instead of using them
+
+Safe Git Practices
+
+Never let Claude Code push to git without your explicit review:
+
+Show me every file you changed in this session. Give me a git diff summary. Do not commit or push anything until I confirm.
+
+When you are ready to commit:
+
+Create a git commit with a descriptive commit message that explains what was changed and why. Stage only the files relevant to this feature. Do not stage anything else. Show me the staged files and the commit message before committing.
+
+Reviewing Before Production
+
+Before any code goes live:
+
+Do a production readiness review of everything we built today. Check for: 1. Exposed API keys or secrets in any file 2. Missing error handling on any async operation 3. SQL injection or other security vulnerabilities 4. Missing input validation on any user-facing form or API endpoint 5. Console.log statements that should be removed 6. Any TODO comments that represent unfinished work 7. Missing loading states or error states in the UI Give me a list of everything that needs to be fixed before this ships.
+
+## Part Seven: Automations and Routines
+
+Claude Code supports scheduled automations called routines. These let you run Claude Code workflows on a schedule without manual triggering.
+
+Creating a Daily Code Review Routine
+
+Create .claude/routines/daily-review.md:
+
+\# Routine: Daily Code Review ## Schedule Every day at 9am ## Process 1. Read all files modified in the last 24 hours using git log 2. Run the code review skill on each modified file 3. Compile all issues found into a daily report 4. Save the report to .claude/reports/\[date\]-daily-review.md ## Output A markdown file with all issues organized by file and severity. Include the total issue count and a recommended priority order for fixing.
+
+Creating a Weekly Dependency Audit
+
+Create .claude/routines/weekly-audit.md:
+
+\# Routine: Weekly Dependency Audit ## Schedule Every Monday at 8am ## Process 1. Read package.json 2. Check for any dependencies flagged as having security vulnerabilities 3. Identify any dependencies that have not been updated in more than 6 months 4. Check if any major version updates are available for core dependencies 5. Save findings to .claude/reports/\[date\]-dependency-audit.md
+
+## Part Eight: From Zero to Shipped
+
+Here is the exact workflow I use to go from idea to live product using Claude Code.
+
+Day One: Architecture and Scaffold
+
+I am building \[describe your product in 2-3 sentences\]. The users are \[describe your target user\]. The core value is \[describe the one thing users get from this\]. Tech stack decision: - Frontend: Next.js 14 with TypeScript - Backend: Next.js API routes - Database: Supabase - Auth: Supabase Auth - Styling: Tailwind CSS - Deployment: Vercel Create the full project scaffold with: - Folder structure as described in my CLAUDE.md - Basic layout component - Supabase client setup - Environment variable template (.env.example — do not create .env) - Package.json with all dependencies - Basic README with setup instructions Do not start with placeholder content. Make every file production-ready from the start.
+
+Day Two: Core Features
+
+The scaffold is working. Now build the core features in this order: 1. Database schema — create the Supabase migration files for \[describe your data model\] 2. API layer — create the API routes that handle \[describe your operations\] 3. UI components — create the reusable components needed for \[describe your interface\] 4. Page assembly — assemble the components into pages After each step, tell me what you built and what the next step depends on before continuing.
+
+Day Three: Polish and Deploy
+
+The core features are working. Now do a full production readiness pass: 1. Error handling — every API call and async operation needs proper error handling 2. Loading states — every data-fetching operation needs a loading state 3. Empty states — every list or data display needs an empty state 4. Mobile responsiveness — every page needs to work on mobile 5. SEO — add proper meta tags to every page 6. Performance — identify and fix any obvious performance issues After the readiness pass, set up deployment to Vercel. Create a deployment checklist and walk me through each step.
+
+## The Real Edge
+
+The people who get extraordinary results from Claude Code are not the ones who know more programming.
+
+They are the ones who treat Claude Code as a system rather than a tool.
+
+They have CLAUDE.md files that are deeply specific. They have skills for every repeating workflow. They have routines running automatically in the background. They read files before editing them. They do production readiness reviews before shipping. They ask Claude Code to evaluate its own work.
+
+The gap between using Claude Code as a coding assistant and using it as an autonomous builder is not about what Claude Code can do.
+
+It is about what you set up to let it do.
+
+This guide is the setup.
+
+Bookmark it. Build the CLAUDE.md. Create the skills. Ship the first project.
+
+The compound starts the moment you do. Follow [@cyrilXBT](https://x.com/@cyrilXBT) for daily Claude Code builds, AI tools, and the exact systems behind everything I ship.
\ No newline at end of file
diff --git a/Claude/Claude Code 命令大全.md b/Claude/Claude Code 命令大全.md
new file mode 100644
index 0000000..453bc2f
--- /dev/null
+++ b/Claude/Claude Code 命令大全.md
@@ -0,0 +1,403 @@
+---
+title: "Claude Code 命令大全"
+source: "https://x.com/shachepi/status/2032036375894413458"
+author:
+ - "[[Unknown]]"
+published: 2026-03-08
+created: 2026-03-13
+description: "Be quiet"
+tags:
+ - "clippings"
+---
+本文内容基于Claude Code 2.1.74 整理。不同版本、套餐、插件、技能和平台，具体显示可能有少量差异。但是万变不离其宗。
+
+发过一篇比较简单的，这篇更详细。**Codex和Gemini的命令版本请查看文章底部**
+
+这篇教程的目标很简单：
+
+1. 用最短路径让你能马上上手。
+2. 用最清晰的结构让你不再混淆。
+3. 用最少的记忆成本建立命令地图。
+
+## 一、先把命令体系分成三层
+
+Claude Code 的命令体系只有三层，记住它，你就不会乱。
+
+第一层：终端主命令，也就是你在 Shell 里直接输入的 claude，作用是“打开程序”。 第二层：终端子命令，也就是 claude 后面跟的功能菜单（auth、mcp、plugins 等），作用是“管理功能”。 第三层：Slash 命令，也就是进入交互界面后输入的 /help、/model、/clear 等，作用是“控制会话”。
+
+一句话总结：主命令打开程序，子命令管理功能，Slash 命令控制会话。
+
+## 二、新手先记住 3 条主命令
+
+这 3 条是你 90% 的入口：
+
+1. claude：进入交互式 Claude Code 会话。
+2. claude -p "任务"：非交互模式，执行完直接输出后退出，适合脚本和批处理。
+3. claude -c：继续当前目录最近一次会话，适合断点续聊。
+
+如果你只记 3 个入口，优先记这三个。
+
+## 三、主命令常见参数（最有实际价值的一组）
+
+下面是最常见、最有实际价值的一组全局参数：
+
+- \--add-dir：额外开放更多目录给 Claude Code 访问，适合跨目录读写。
+- \--agent：指定当前会话使用哪个 agent。
+- \--agents：临时传入自定义 agents 的 JSON 定义。
+- \--allowed-tools：只允许指定工具运行。
+- \--disallowed-tools：显式禁止某些工具运行。
+- \--tools：自定义本次会话可用工具集合。
+- \--system-prompt：直接覆盖本次会话的系统提示词。
+- \--append-system-prompt：在默认系统提示词后追加补充说明。
+- \--model：指定模型。
+- \--fallback-model：默认模型拥堵时，为 print 模式指定备用模型。
+- \--effort：指定思考强度，常见有 low、medium、high、max。
+- \--permission-mode：指定权限策略，例如 default、auto、plan、dontAsk。
+- \--settings：加载额外 settings 文件或 JSON。
+- \--setting-sources：限制只从哪些设置层读取配置。
+- \--mcp-config：加载 MCP 配置文件或 JSON。
+- \--strict-mcp-config：只使用命令行指定的 MCP 配置。
+- \--plugin-dir：临时加载某些插件目录。
+- \--debug：开启调试模式。
+- \--debug-file：把调试日志写入指定路径。
+- \--verbose：强制打开 verbose。
+- \-p 或 --print：输出后退出，不进入交互模式。
+- \--output-format：控制输出格式，可用于 text、json、stream-json。
+- \--input-format：控制输入格式，常用于 text 或 stream-json。
+- \--json-schema：要求输出满足指定 JSON Schema。
+- \--include-partial-messages：流式输出时包含中途分片。
+- \--max-budget-usd：限制本次 print 模式的 API 花费上限。
+- \--no-session-persistence：不保存会话到本地。
+- \--session-id：手动指定会话 ID。
+- \--fork-session：恢复旧会话时，分叉出一个新会话。
+- \--from-pr：从关联 PR 恢复会话。
+- \--ide：如果本机只有一个可用 IDE，自动连接。
+- \--chrome 或 --no-chrome：开启或关闭 Claude in Chrome 集成。
+- \-w 或 --worktree：为本次任务创建新的 Git worktree。
+- \--tmux：配合 worktree 建立 tmux 会话。
+- \--file：启动时下载文件资源到指定相对路径。
+
+如果从“小白优先级”来排序，最值得先记的是下面 5 个：
+
+1. \--model：指定模型。
+2. \--effort：指定思考强度。
+3. \--permission-mode：控制工具权限策略。
+4. \--debug：开启调试模式。
+5. \--output-format：控制输出格式。
+
+## 四、哪些参数要格外谨慎
+
+下面这一组要特别小心，因为它们会直接影响 Claude Code 的行为边界和安全边界：
+
+1. \--dangerously-skip-permissions：跳过权限确认。
+2. \--allow-dangerously-skip-permissions：允许跳过权限确认。
+3. \--tools：自定义可用工具集合。
+4. \--system-prompt：直接覆盖系统提示词。
+5. \--permission-mode：切换权限策略。
+
+## 五、一级子命令总览
+
+Claude Code 当前常见一级子命令如下：
+
+- claude agents：列出或管理 agents。
+- claude auth：管理登录状态和账号认证。
+- claude auto-mode：查看 auto mode 分类器配置。
+- claude doctor：检查 Claude Code 安装和更新器健康状态。
+- claude install：安装 Claude Code 原生版本。
+- claude mcp：管理 MCP 服务器。
+- claude plugin 或 claude plugins：管理插件。
+- claude setup-token：设置长期认证 token。
+- claude update 或 claude upgrade：检查并安装新版本。
+
+## 六、auth 认证命令
+
+这一组用于登录、退出和查看认证状态：
+
+- claude auth login：登录 Anthropic 账号。
+- claude auth login --email 你的邮箱：预填邮箱。
+- claude auth login --sso：强制走 SSO 登录。
+- claude auth logout：退出当前账号。
+- claude auth status：查看认证状态。
+- claude auth status --json：以 JSON 格式显示。
+- claude auth status --text：以人类可读文本显示。
+
+## 七、auto-mode 命令
+
+这一组用于看自动模式的规则：
+
+- claude auto-mode config：输出当前生效的 auto mode 配置。
+- claude auto-mode defaults：输出默认的 auto mode 规则。
+
+这类命令更像“查看系统策略”，不是日常高频，但排障时很有用。
+
+## 八、doctor、install、update、setup-token
+
+- claude doctor：检查安装状态和自动更新器健康情况。
+- claude install stable：安装稳定版。
+- claude install latest：安装最新版。
+- claude install 具体版本号：安装指定版本。
+- claude install --force：即使已安装，也强制重新安装。
+- claude update：检查更新并安装。
+- claude upgrade：与 update 类似，用于升级。
+- claude setup-token：配置长期认证 token。
+
+## 九、MCP 命令详解
+
+MCP 可以理解成 Claude Code 对外接工具、服务和上下文桥接的一套机制。
+
+常见命令如下：
+
+- claude mcp list：列出已经配置的 MCP 服务器。
+- claude mcp get 名称：查看某个 MCP 的详情。
+- claude mcp add 名称 命令或URL：新增一个 MCP。
+- claude mcp add-json 名称 JSON：用 JSON 直接添加 MCP。
+- claude mcp add-from-claude-desktop：从 Claude Desktop 导入 MCP。
+- claude mcp remove 名称：删除 MCP。
+- claude mcp reset-project-choices：重置当前项目里 MCP 的批准和拒绝记录。
+- claude mcp serve：启动 Claude Code 自己的 MCP 服务端。
+
+claude mcp add 常见参数：
+
+- \--transport：传输方式，常见是 stdio、sse、http。
+- \--env：给 MCP 进程注入环境变量。
+- \--header：给 HTTP 或相关连接设置请求头。
+- \--scope：指定配置范围，常见是 local、user、project。
+- \--client-id：某些 OAuth 场景下使用。
+- \--client-secret：输入 OAuth client secret。
+- \--callback-port：固定 OAuth 回调端口。
+
+如果你是普通用户，最常见的是这三种场景：
+
+1. 用 claude mcp list 看现在连了哪些服务。
+2. 用 claude mcp add 增加一个本地或远程 MCP。
+3. 用 claude mcp remove 删除不用的连接。
+
+## 十、插件命令详解
+
+插件命令既可以写成 claude plugin，也可以写成 claude plugins。
+
+常见命令如下：
+
+- claude plugins list：查看已安装插件。
+- claude plugins list --json：以 JSON 显示。
+- claude plugins list --available --json：连同市场可安装插件一起显示。
+- claude plugins install 插件名：安装插件。
+- claude plugins enable 插件名：启用插件。
+- claude plugins disable 插件名：禁用插件。
+- claude plugins disable -a：禁用全部已启用插件。
+- claude plugins update 插件名：更新插件。
+- claude plugins uninstall 插件名：卸载插件。
+- claude plugins validate 路径：校验插件或 marketplace 的 manifest。
+
+常见 scope 参数：
+
+- user：对当前用户生效。
+- project：对当前项目生效。
+- local：更偏向本地目录层的配置。
+
+## 十一、插件市场 marketplace 命令
+
+这一组用于管理插件来源：
+
+- claude plugins marketplace list：列出已配置市场。
+- claude plugins marketplace list --json：以 JSON 显示。
+- claude plugins marketplace add 来源：添加一个市场。
+- claude plugins marketplace remove 名称：删除一个市场。
+- claude plugins marketplace update：更新全部市场。
+- claude plugins marketplace update 名称：只更新指定市场。
+
+claude plugins marketplace add 常见参数：
+
+- \--scope：指定市场定义写到 user、project 或 local 哪一层。
+- \--sparse：如果来源是 monorepo，可用稀疏检出只拉一部分目录。
+
+## 十二、交互式 Slash 命令总览
+
+使用方式很简单：先输入 claude 进入交互会话，再输入 /help、/model、/clear 这种命令。
+
+注意三点：
+
+1. Slash 命令属于“会话内部快捷指令”。
+2. 不同版本、账号权限、插件、技能、平台，列表会略有变化。
+3. 下面优先列的是本机交互菜单里实际抓到的命令。
+
+## 十三、上下文与会话管理类 Slash 命令
+
+- /help：显示帮助和可用命令列表。
+- /exit：退出当前 REPL。
+- /clear：清空会话历史，释放上下文。
+- /compact：压缩上下文，保留摘要。
+- /resume：恢复之前的对话。
+- /rewind：把代码和或对话回退到某个历史点。
+- /fork：从当前点分叉一个新会话。
+- /rename：重命名当前会话。
+- /copy：复制 Claude 最近一条回复，或其中的代码块。
+- /export：导出当前对话到文件或剪贴板。
+- /diff：查看未提交变更和每轮差异。
+
+理解这组命令的关键：
+
+1. /clear 是彻底清空当前上下文。
+2. /compact 是缩短上下文，但保留摘要记忆。
+3. /resume 是恢复过去会话。
+4. /rewind 是退回到过去某个检查点。
+5. /fork 是从当前状态开一个分支，不是回退。
+
+## 十四、运行环境与界面设置类 Slash 命令
+
+- /status：查看当前状态，例如版本、模型、环境。
+- /stats：查看使用统计和活动。
+- /usage：查看套餐额度和使用限制。
+- /cost：查看当前会话总成本和耗时。
+- /config：打开配置面板。
+- /model：切换当前模型。
+- /plan：进入或查看 Plan 模式。
+- /permissions：管理工具权限规则。
+- /sandbox：查看或配置沙箱状态。
+- /theme：切换主题。
+- /vim：切换 Vim 编辑模式和普通编辑模式。
+- /fast：切换 Fast Mode。
+- /ide：管理 IDE 集成和连接状态。
+- /add-dir：添加工作目录。
+- /statusline：设置状态栏 UI。
+- /terminal-setup：为终端安装 Shift+Enter 换行支持。
+
+## 十五、账号、安装与连接类 Slash 命令
+
+- /login：登录或切换 Anthropic 账号。
+- /logout：退出账号。
+- /upgrade：升级套餐。
+- /doctor：检查安装状态。
+- /init：初始化新的 CLAUDE.md 文件。
+- /desktop：把当前会话继续到 Claude Desktop。
+- /chrome：管理 Claude in Chrome 的设置。
+- /mobile：显示二维码以下载 Claude 手机 App。
+
+## 十六、扩展能力类 Slash 命令
+
+- /agents：管理 agent 配置。
+- /hooks：管理工具事件 hooks。
+- /skills：列出当前可用 skills。
+- /mcp：管理 MCP 服务器。
+- /plugin：管理 Claude Code 插件。
+- /reload-plugins：让当前会话应用等待生效的插件改动。
+- /install-github-app：为仓库接入 Claude GitHub Actions。
+- /install-slack-app：安装 Claude Slack App。
+
+## 十七、工程流程与自动化类 Slash 命令
+
+- /tasks：查看和管理后台任务。
+- /loop：按固定间隔重复执行一个 prompt 或 slash 命令。
+- /batch：用于大规模改动，先调研和规划，再推进执行。
+- /review：审查 Pull Request。
+- /pr-comments：读取 GitHub Pull Request 评论。
+- /security-review：从安全角度检查当前待提交改动。
+- /simplify：检查修改后的代码是否还能继续简化、复用或提质。
+- /debug：开启本会话调试日志，辅助排障。
+- /feedback：提交使用反馈。
+- /stickers：订购 Claude Code 贴纸。
+- /insights：生成 Claude Code 会话分析报告。
+- /release-notes：查看发布说明。
+
+## 十八、动态技能型 Slash 命令
+
+这一类命令不是每台机器都一样，因为它们取决于你本机装了哪些 skills、插件和扩展。
+
+例如某些机器在交互补全里会看到：
+
+- /x-post-reply-skill：X 内容生成技能。
+- /claude-api：Claude API / Anthropic SDK 使用指导。
+
+要分清两件事：
+
+1. 内置 Slash 命令，是 Claude Code 自己稳定提供的。
+2. 动态技能命令，是当前环境额外挂进去的。
+
+## 十九、最推荐记住的高频命令
+
+如果你不想一口气记太多，最值得优先掌握的是下面这些。
+
+终端高频：
+
+1. claude：启动交互式 Claude Code 会话。
+2. claude -p "任务"：一次性执行并输出后退出。
+3. claude -c：继续当前目录最近一次会话。
+4. claude --help：查看总帮助。
+5. claude mcp list：列出当前 MCP 连接。
+6. claude plugins list --json：以 JSON 查看插件列表。
+
+Slash 高频：
+
+1. /help：显示帮助和可用命令列表。
+2. /clear：清空会话历史。
+3. /compact：压缩上下文并保留摘要。
+4. /status：查看当前状态。
+5. /model：切换模型。
+6. /permissions：管理工具权限规则。
+7. /config：打开配置面板。
+8. /mcp：管理 MCP 服务器。
+9. /plugin：管理插件。
+10. /review：审查 PR。
+
+## 二十、小白最容易混淆的 5 组区别
+
+第一组：claude 和 claude -p
+
+1. claude：进入交互界面。
+2. claude -p：一次性执行，打印结果后退出。
+
+第二组：/clear 和 /compact
+
+1. /clear：直接清掉上下文。
+2. /compact：压缩上下文，但保留摘要。
+
+第三组：/resume 和 /rewind
+
+1. /resume：找回旧对话。
+2. /rewind：把当前状态退回过去。
+
+第四组：MCP 和插件
+
+1. MCP：外部能力连接器。
+2. 插件：Claude Code 内部扩展模块。
+
+第五组：终端子命令和 Slash 命令
+
+1. 终端子命令：在 Shell 里运行。
+2. Slash 命令：在 Claude Code 会话内部运行。
+
+## 二十一、最后给你的使用路线
+
+如果你是第一次系统学 Claude Code，建议按这个顺序上手：
+
+1. 先掌握 claude、claude -p、claude -c。
+2. 再掌握 /help、/clear、/compact、/model。
+3. 然后理解 auth、mcp、plugins 这三组命令。
+4. 最后再碰 /permissions、/sandbox、/batch、/review 这类偏工程化的功能。
+
+这样学，最稳，也最不容易乱。
+
+## 二十二、极简结论
+
+Claude Code 的命令体系并不乱，它只是分成了终端入口、终端子命令、会话内 Slash 命令三层。
+
+你真正需要先掌握的，不是“背完整清单”，而是先把三层地图建立起来。
+
+如果你只想现在立刻开始用，那就记住下面这一组：
+
+1. claude：启动交互式 Claude Code 会话。
+2. claude -p "任务"：一次性执行并输出后退出。
+3. claude -c：继续当前目录最近一次会话。
+4. /help：显示帮助和可用命令列表。
+5. /clear：清空会话历史。
+6. /compact：压缩上下文但保留摘要。
+7. /model：切换当前模型。
+8. /mcp：管理 MCP 服务器。
+9. /plugin：管理插件。
+10. /review：审查 PR。
+
+## 其他相关：
+
+> 3月8日
+
+> 3月7日
\ No newline at end of file
diff --git a/Claude/Claude Code 替代方案终极对比：10 款最强 CLI AI 编程工具全解析.md b/Claude/Claude Code 替代方案终极对比：10 款最强 CLI AI 编程工具全解析.md
new file mode 100644
index 0000000..eb3edb7
--- /dev/null
+++ b/Claude/Claude Code 替代方案终极对比：10 款最强 CLI AI 编程工具全解析.md
@@ -0,0 +1,16 @@
+---
+title: "Claude Code 替代方案终极对比：10 款最强 CLI AI 编程工具全解析"
+source: "https://www.kevnu.com/zh/posts/the-ultimate-comparison-of-claude-code-alternatives-a-complete-analysis-of-the-10-strongest-cli-ai-programming-tools"
+author:
+ - "[[KevinYouu]]"
+published: 2025-11-05
+created: 2026-03-13
+description: "收录 OpenCode、Crush、Qwen Code、Gemini CLI、Kimi CLI、Aider、Codex CLI、Grok CLI、Claude Code 与 Copilot CLI 的安装、模型支持与优缺点，让你轻松选择最适合的 AI 编码伙伴。"
+tags:
+ - "clippings"
+---
+
+
+开源工具详解 1. Gemini CLI ⭐ 81,300+ Stars 官方仓库: https://github.com/google-gemini/gemini-cli 核心优势 完全免费 ：个人 Google 账号每分钟 60 次请求，每天 1000 次 超大上下文 ：Gemini 2.5 Pro 原生支持 100 万 token 上下文窗口 多模态能力 ：支持文本、图像、PDF、视频等多种输入 Google 搜索集成 ：实时访问网页和最新信息 MCP 支持 ：内置 Model Context Protocol 支持 完全开源 ：Apache 2.0 许可，代码透明 支持的模型 Gemini 2.5 Pro（默认，免费） Gemini 2.5 Flash（快速） 通过 Vertex AI 可使用其他模型 安装方法 bash# NPM 安装（需要 Node.js 18+） npm install -g @google/gemini-cli # 验证安装 gemini --version 认证配置 方法一：个人 Google 账号（免费，推荐） bash# 首次运行自动引导认证 gemini # 按提示登录 Google 账号 方法二：Google AI Studio API Key bash# 设置 API Key export GOOGLE\_API\_KEY="your\_api\_key" # 启动 gemini 方法三：Vertex AI（企业用户） bashexport GOOGLE\_GENAI\_USE\_VERTEXAI=true export GOOGLE\_API\_KEY="your\_vertex\_api\_key" gemini 基本使用 bash# 启动 Gemini CLI gemini # 程序化模式 gemini -p "创建一个 REST API" # JSON 输出 gemini -p "分析代码架构" --output-format json 高级配置 项目上下文 （创建 GEMINI.md 或者直接使用 /init 自动总结生成） MCP 服务器配置 （ ~/.gemini/settings.json ） json{ "mcpServers": { "github": { "transport": "stdio", "command": "npx", "args": \["@modelcontextprotocol/server-github"\] } } } 免费额度 个人账号 ：60 RPM / 1,000 RPD ：无需信用卡 2. Aider ⭐ 35,200+ Stars https://github.com/Aider-AI/aider 最成熟的开源方案 ：拥有最活跃的社区 顶级模型支持 ：与 Claude、DeepSeek、OpenAI、Gemini 等配合 智能代码库映射 ：创建整个代码库的地图 Git 深度集成 ：自动提交更改并附带有意义的提交信息 Architect/Editor 模式 ：支持推理模型与编辑模型的双模式协作 Anthropic Claude 系列（推荐） OpenAI GPT 系列（包括 o1、o3-mini） DeepSeek R1 & Chat V3 Google Gemini 本地模型（Ollama、LM Studio 等） bashpython -m pip install aider-install aider-install bash # Change directory into your codebase cd /to/your/project # DeepSeek aider --model deepseek --api-key deepseek= # Claude 3.7 Sonnet aider --model sonnet --api-key anthropic= # o3-mini aider --model o3-mini --api-key openai= 3. OpenCode ⭐ 31,500+ Stars https://github.com/sst/opencode 极致的终端体验 ：由 neovim 用户打造 模型无关架构 ：支持 Anthropic、OpenAI、Google 甚至本地模型 客户端/服务器架构 ：可以远程驱动，支持移动应用控制 现代 TUI 设计 ：使用 Zig + SolidJS 构建 OpenAI GPT 系列 本地 LLM 模型 bash# YOLO curl -fsSL https://opencode.ai/install | bash # Package managers npm i -g opencode-ai@latest # or bun/pnpm/yarn scoop bucket add extras; scoop install extras/opencode # Windows choco install opencode # Windows brew install opencode # macOS and Linux paru -S opencode-bin # Arch Linux 4. Qwen Code ⭐ 14,900+ Stars https://github.com/QwenLM/qwen-code 专为 Qwen3-Coder 优化 ：阿里巴巴开源，针对中文场景优化 超大上下文窗口 ：原生支持 256K tokens，扩展可达 1M tokens 免费使用选项 ：通过 OpenRouter 或 ModelScope 免费使用 代码库理解 ：超越传统上下文窗口限制 工作流自动化 ：自动化 PR 处理、复杂 rebase、格式化等任务 Qwen3-Coder-480B-A35B-Instruct（旗舰版） Qwen3-Coder-Flash（快速版） Qwen3-Coder-Plus 通过 OpenAI 兼容 API 支持其他模型 bash# 一键安装 curl -qL https://www.npmjs.com/install.sh | sh # 或者 NPM 全局安装（推荐，需要 Node.js 20+） npm install -g @qwen-code/qwen-code@latest # 或源码安装 git clone https://github.com/QwenLM/qwen-code.git cd qwen-code npm install npm install -g. # 或使用 Homebrew brew install qwen-code # 验证安装 qwen --version 配置与使用 通过 Qwen OAuth（免费） bash# 首次运行，按提示配置 qwen # 在项目目录中使用 cd your-project/ qwen Token 使用限制（~/.qwen/settings.json） json{ "sessionTokenLimit": 32000 } 5. Crush ⭐ 14,400+ Stars https://github.com/charmbracelet/crush 优雅的用户体验 ：由 Charm 团队打造 LSP 增强 ：使用 LSP 提供额外的上下文 MCP 可扩展性 ：通过 MCP（http、stdio 和 sse）添加功能 多平台支持 ：支持 macOS、Linux、Windows、FreeBSD、OpenBSD 和 NetBSD 会话管理 ：支持每个项目多个工作会话和上下文 多模型选择：广泛的 LLM 支持 OpenAI 或 Anthropic 兼容 API 可在会话中切换 LLM 同时保留上下文 bash# Homebrew brew install charmbracelet/tap/crush # go 安装 go install github.com/charmbracelet/crush@latest # NPM npm install -g @charmland/crush # Arch Linux (btw) yay -S crush-bin # Nix nix run github:numtide/nix-ai-tools#crush # Windows # Winget winget install charmbracelet.crush # Scoop scoop bucket add charm https://github.com/charmbracelet/scoop-bucket.git scoop install crush # Debian/Ubuntu sudo mkdir -p /etc/apt/keyrings curl -fsSL https://repo.charm.sh/apt/gpg.key | sudo gpg --dearmor -o /etc/apt/keyrings/charm.gpg echo "deb \[signed-by=/etc/apt/keyrings/charm.gpg\] https://repo.charm.sh/apt/ \* \*" | sudo tee /etc/apt/sources.list.d/charm.list sudo apt update && sudo apt install crush 使用方法 最快的入门方法是获取您喜欢的提供商（如 Anthropic、OpenAI、Groq 或 OpenRouter）的 API 密钥，然后即可开始。系统会提示您输入 API 密钥。 Environment Variable Provider ANTHROPIC\_API\_KEY Anthropic OPENAI\_API\_KEY OpenAI OPENROUTER\_API\_KEY OpenRouter GEMINI\_API\_KEY CEREBRAS\_API\_KEY Cerebras HF\_TOKEN Huggingface Inference VERTEXAI\_PROJECT Google Cloud VertexAI (Gemini) VERTEXAI\_LOCATION GROQ\_API\_KEY Groq AWS\_ACCESS\_KEY\_ID AWS Bedrock (Claude) AWS\_SECRET\_ACCESS\_KEY AWS\_REGION AWS\_PROFILE AWS Bedrock (Custom Profile) AWS\_BEARER\_TOKEN\_BEDROCK AWS Bedrock AZURE\_OPENAI\_API\_ENDPOINT Azure OpenAI models AZURE\_OPENAI\_API\_KEY Azure OpenAI models (optional when using Entra ID) AZURE\_OPENAI\_API\_VERSION 配置示例（~/.crush/config.json） json{ "$schema": "https://charm.land/crush.json", "providers": { "anthropic": { "type": "anthropic", "base\_url": "https://api.anthropic.com/v1", "api\_key": "$ANTHROPIC\_API\_KEY", "models": \[ { "id": "claude-sonnet-4-20250514", "name": "Claude Sonnet 4" } \] } } } 6. Kimi CLI ⭐ 2,000+ Stars https://github.com/MoonshotAI/kimi-cli 强大的 Kimi K2 模型 ：1T 参数 MoE 模型，32B 激活参数 Shell 模式 ：Ctrl-K 切换，可直接执行 shell 命令 Zsh 深度集成 ：无缝集成到 Zsh 工作流 ACP 支持 ：Agent Client Protocol，可与 Zed 编辑器集成 MCP 工具访问 ：连接 Linear、GitHub 等外部服务 中文优先 ：对中文场景优化 Kimi K2 Instruct（推荐） Kimi K1.5 Kimi For Coding（会员专属） 自定义模型（通过配置） bash# 使用 uv（推荐，需要 Python 3.13+） # 安装 uv curl -LsSf https://astral.sh/uv/install.sh | sh # 安装 Kimi CLI uv tool install --python 3.13 kimi-cli # 验证安装 kimi --version # 升级 uv tool upgrade kimi-cli --no-cache bash# 启动 Kimi CLI kimi # 首次运行，使用 /setup 配置 > /setup 7. Codex CLI (OpenAI) ⭐ 49700+ Stars https://github.com/openai/codex 注意 ：Codex CLI 虽然开源,但属于 OpenAI 产品生态,需要 OpenAI API 或 ChatGPT 订阅 GPT-5-Codex 驱动 ：专为 agentic coding 优化的 GPT-5 版本 极快的推理速度 ：针对编码任务优化 多模态输入 ：支持文本、截图、图表输入 三种审批模式 ：Suggest、Auto Edit、Full Auto 云端协同 ：可在 IDE、终端、云端、GitHub、手机间无缝切换 Rust 构建 ：速度和效率优化 GPT-5-Codex（推荐,默认） GPT-5 o1-mini o3-mini 其他 OpenAI Responses API 模型 bash# NPM 全局安装 npm install -g @openai/codex # 或使用 Homebrew (macOS) brew install --cask codex # 升级到最新版本 codex --upgrade # 验证安装 codex --version 需要以下之一 ： ChatGPT Plus 订阅（$20/月） ChatGPT Pro 订阅（$200/月） ChatGPT Business / Edu / Enterprise OpenAI API Key（按量计费） 交互模式 bash# 启动 Codex codex # 在 Codex 中输入请求 > Add error handling to all API endpoints > Refactor this component to use hooks > Fix the TypeScript errors in this file 审批模式 bash# Suggest 模式（默认）- 每次都需要批准 codex --suggest # Auto Edit 模式 - 自动应用编辑,命令需批准 codex --auto-edit # Full Auto 模式 - 完全自动 codex --full-auto # 会话中切换模式 > /mode auto-edit 高级功能 配置文件 ~/.codex/config.toml toml\[default\] model = "gpt-5-codex" auto\_approve = false max\_tokens = 4096 \[sandbox\] enable = true container = "docker" AGENTS.md 文件 在项目根目录创建 AGENTS.md 8. Grok CLI ⭐ 社区项目 https://github.com/superagent-ai/grok-cli 最智能模型 ：Grok 4 在多个基准测试中排名第一 Grok Code Fast 1 ：专为 agentic coding 优化,响应速度快（92 tokens/秒） 原生工具使用 ：代码解释器、终端、文件编辑等 X 平台集成 ：可访问 X 的实时信息和趋势 开源 ：MIT 许可,社区驱动 高速代码编辑 ：单次操作即可修改多个文件 Grok 4（最新旗舰） Grok 4 Code Fast 1（专为编码优化） Grok 3.5（快速版本） Grok 2（经典版本） bash# NPM 安装 npm install -g @vibe-kit/grok-cli # 或使用 Bun bun add -g @vibe-kit/grok-cli # 验证安装 grok --version 配置 获取 API Key 访问 https://console.x.ai/ 创建账号并获取 API key 配置环境变量 bash# 设置 API Key export GROK\_API\_KEY=your\_api\_key\_here # 或在项目中创建.env 文件 echo "GROK\_API\_KEY=your\_api\_key\_here" >.env bash# 启动 Grok CLI grok # 使用环境变量文件启动 grok --api-key your\_api\_key\_here 创建 ~/.grok/user-settings.json json{ "apiKey": "your\_api\_key\_here", "baseURL": "https://api.x.ai/v1", "defaultModel": "grok-code-fast-1", "models": \[ "grok-code-fast-1", "grok-4-latest", "grok-3-latest", "grok-3-fast", "grok-3-mini-fast" \] } 闭源工具对比 1. GitHub Copilot CLI ⭐ 微软出品 官方文档 https://docs.github.com/en/copilot/how-tos/use-copilot-cli 深度 GitHub 集成 ：开箱即用访问仓库、issues 和 pull requests Agentic 能力 ：AI 协作者可以规划和执行复杂任务 MCP 扩展性 ：内置 GitHub MCP 服务器，支持自定义扩展 完全控制 ：执行前预览每个操作，需明确批准 企业级 ：支持团队管理和使用监控 Claude Sonnet 4.5（新增，推荐） GPT-4o GPT-4 Turbo Claude Haiku 4.5（快速版） Gemini 2.5 Pro（实验性） bash# 使用 npm 安装 npm install -g @github/copilot # 验证安装 copilot --version bash# 交互模式 copilot 订阅计划 计划 价格 功能 Copilot Pro $10/月 CLI + IDE 插件，个人使用 Copilot Pro+ $39/月 更高额度，Claude Sonnet 4.5 Copilot Business $19/用户/月 团队管理，使用监控 Copilot Enterprise $39/用户/月 企业定制，私有知识库 2. Claude Code ⭐ Anthropic 官方 官方网站 https://www.anthropic.com/claude-code https://docs.claude.com/en/docs/claude-code 官方打造 ：Anthropic 官方出品，与 Claude 模型深度集成 内置记忆功能 ：保留会话、命令和风格指南信息 最强模型 ：Claude Sonnet 4.5 是目前最智能的编码模型之一 无缝体验 ：与 Claude API 完全集成，无需额外配置 企业级支持 ：专业的技术支持和 SLA 保证 Claude Sonnet 4.5（最新最强） Claude Opus 4.1/4（旗舰版） Claude Sonnet 4（标准版） Claude Haiku（快速响应） bash# 需要 Node.js 18+ npm install -g @anthropic-ai/claude-code # 一键安装 curl -fsSL https://claude.ai/install.sh | bash # Homebrew brew install --cask claude-code # Windows irm https://claude.ai/install.ps1 | iex # 验证安装 claude --version # 启动 claude bash# 启动 Claude Code claude # 指定模型 claude --model claude-opus-4 # 限制消费（团队管理） claude --spending-limit 100 # 程序化模式 claude -p "重构这个组件使用 TypeScript" 定价 按量计费（API） Claude Sonnet 4.5: 输入: $3 / 1M tokens 输出: $15 / 1M tokens 缓存输入: $0.30 / 1M tokens Claude Opus 4: 输入: $15 / 1M tokens 输出: $75 / 1M tokens 劣势 成本较高 ：大型团队使用可能昂贵 闭源限制 ：无法查看和修改源代码 依赖 Anthropic ：只能使用 Claude 模型 完整功能对比表 功能特性 Gemini CLI Aider OpenCode Qwen Code Crush Kimi CLI Copilot CLI Claude Code GitHub Stars 81.3K 35.2K 29.7K 14.9K 14.4K 2.0K 闭源 开源/闭源 许可证 Apache 2.0 MIT 专有 安装难度 简单 中等 ✅ 大额 ❌ 多模型支持 ❌ 仅 Gemini ✅ 全面 ✅ 有限 ❌ 仅 Kimi ✅ 多家 ❌ 仅 Claude 本地模型 ✅ LSP 集成 ⭐⭐⭐ ⭐⭐⭐⭐⭐ ⭐⭐⭐⭐ 上下文窗口 1M 200K+ 128K+ 256K-1M Windows 支持 开发中 企业支持 有限 社区 商业 官方 文档质量 社区活跃度 选择指南 按使用场景选择 个人学习/小项目 推荐 - 完全免费，功能强大 - 免费额度 + 中文友好 - 开源 + 灵活配置 理由 ：零成本或低成本，学习曲线适中 开源项目维护 - 社区最活跃，Git 集成最强 - 终端体验优秀 - 优雅且易用 ：开源工具支持开源项目，社区驱动 创业公司/小团队（5-10 人） Aider + Claude Sonnet 4 - $45-90/月（整个团队） GitHub Copilot CLI Business - $95-190/月 - 最低成本 ：平衡成本与性能，易于管理 中大型企业（50+人） GitHub Copilot CLI Enterprise - 完整生态 - 最佳性能 - 自主部署方案 ：企业级支持，合规性保证 按技术栈选择 Python 开发者 ：Aider、Qwen Code、Claude Code 理由：Python 支持最完善，测试覆盖最好 JavaScript/TypeScript 开发者 ：GitHub Copilot CLI、OpenCode、Gemini CLI 理由：前端生态集成最好 多语言团队 ：Aider、Crush、Claude Code 理由：全面的多语言支持 AI/ML 开发者 ：Qwen Code、Gemini CLI、Claude Code 理由：对数据科学库支持最好 按工作方式选择 终端重度用户 - 极致终端体验 - Zsh 深度集成 - 优雅的 TUI 设计 IDE 依赖者 GitHub Copilot CLI - VS Code 无缝集成 - 编辑器插件丰富 - VS Code 扩展 命令行新手 - 最易上手 - 界面友好 - 文档完善 按地域选择 中国大陆用户 - 本地化最好 - 国内厂商 Aider + 国产模型 - 灵活配置 注意事项 某些工具需要代理访问 考虑 API 延迟问题 优先选择国内厂商或有国内节点的服务 国际用户 - 全球可用 + 免费 - 全球覆盖 - 性能最佳 工具成熟度评估 生产就绪度 完全成熟（可直接用于生产） - Google 官方，81K+ stars - 35K+ stars，久经考验 - 微软官方，企业级 - Anthropic 官方，稳定可靠 - 29K+ stars，活跃开发 基本成熟（适合大部分场景） - 14K+ stars，Charm 团队出品 - 14.9K+ stars，阿里出品 快速发展中（建议谨慎用于关键项目） - 2K+ stars，月之暗面出品，持续改进 最佳实践建议 新手入门路线 第一阶段：免费尝试（1-2 周） 安装 （完全免费） 学习基本命令和交互方式 尝试简单的代码生成任务 体验 Google 搜索集成 bash# 快速开始 npm install -g @google/gemini-cli gemini 第二阶段：探索开源（2-4 周） 配置 Claude Sonnet 4 模型 在真实项目中使用 学习 Git 集成和版本控制 bash# 安装 Aider pip install aider-chat # 配置环境变量 export ANTHROPIC\_API\_KEY=your-key aider 第三阶段：进阶选择（1 个月后） 如果追求性能 → 如果在乎成本 → 继续 或 如果用 GitHub → 如果中文为主 → 团队采用策略 评估阶段（第 1 个月） 选择 2-3 个工具进行试点 在非关键项目上测试 收集团队反馈 评估成本和性能 试点阶段（第 2-3 个月） 选定主力工具 制定团队规范和最佳实践 培训团队成员 设置成本监控 推广阶段（第 4 个月起） 全团队采用 持续优化工作流 分享成功案例 定期评估 ROI 安全与隐私考量 数据安全对比 工具 代码存储 数据加密 本地运行 企业合规 云端 本地 N/A ✅ 可选 企业使用建议 高度敏感项目 ✅ 推荐： + 本地模型（Ollama） + 私有部署 谨慎：任何云端服务 一般商业项目 （有 DPA） （Anthropic 有强隐私承诺） （Google 企业版） 开源项目 ✅ 任何工具都可以使用 ✅ 优先开源工具支持开源社区 社区资源与支持 常见问题解答 Q1: 如何选择第一个工具？ A: 建议从免费工具开始： 完全新手 → Gemini CLI（免费+简单） 有经验 → Aider（功能强+开源） 企业用户 → GitHub Copilot CLI 试用版 Q2: 开源工具和闭源工具差距大吗？ 性能差距 ：顶级模型（Claude、GPT）略胜一筹 功能差距 ：开源工具通过灵活配置可以达到相近效果 成本差距 ：开源工具可以选择便宜模型或免费工具，成本更可控 结论 ：对大多数场景，开源工具完全够用 Q3: 哪个工具最适合团队使用？: 取决于团队情况： 小团队（<10 人） → Aider（最经济，$45-90/月） 中型团队（10-50 人） → GitHub Copilot Business（$190-950/月） 大型团队（50+人） → Copilot Enterprise 或 Claude Code（企业级） Q4: 如何控制成本？ 使用免费工具（Gemini CLI、Qwen Code 免费版） 启用提示缓存（可节省 90% 输入成本） 使用 Aider 的 --map-tokens 限制上下文 只在需要时使用昂贵模型 设置每月消费上限 Q5: 代码会被用来训练模型吗？ Aider/OpenCode/Crush: 使用你自己的 API，取决于 API 提供商 GitHub Copilot: 企业版保证不用于训练: Anthropic 承诺不用于训练: Google 承诺不用于训练（需查看隐私设置） 建议: 查看各家的隐私政策，企业用户选择有 DPA 的服务 🎬 总结 综合推荐 最佳免费工具 完全免费（60 RPM / 1,000 RPD） 100 万 token 上下文 Google 生态集成 81.3K stars 最佳性价比 Gemini: 完全免费 Aider: $1.80-9/月，功能最强 最佳企业工具 GitHub 深度集成 合理定价（$39/用户/月） 最佳性能 Claude Sonnet 4.5 SWE-Bench 领先 官方优化 最佳中文工具 中文原生（14.9K stars） 超大上下文（256K-1M） 最佳终端体验 现代 TUI 设计（29.7K stars） 极致性能 最佳新手工具 易于上手 文档清晰 界面友好 2025 年推荐矩阵 性能 ↑ | Claude Code ● | ● Copilot CLI | Aider ● | | Qwen Code ● | ● Kimi CLI | OpenCode ● | ● Crush | Gemini CLI ● | | ←────────────────┼────────────────→ 免费/低成本 高成本 最终建议 如果你只能选一个工具 预算有限 → Gemini CLI（完全免费） 追求灵活 → Aider（最成熟开源） 追求性能 → Claude Code（最强模型） 企业使用 → GitHub Copilot CLI Enterprise → Qwen Code 最好的工具是你实际会使用的工具 。建议： 先从免费工具尝试（Gemini CLI、Qwen Code） 体验开源工具的灵活性（Aider、OpenCode） 根据实际需求决定是否付费升级 不要害怕同时使用多个工具 参考资源 官方链接 开源工具 Gemini CLI GitHub Aider 官方文档 OpenCode 官方网站 Qwen Code GitHub Crush 官方仓库 Kimi CLI GitHub 闭源工具 GitHub Copilot CLI 文档 Claude Code 文档 深度阅读 SWE-Bench 基准测试 Model Context Protocol Anthropic API 文档 Google Gemini API 文档
+
+Claude Code 替代方案终极对比：10 款最强 CLI AI 编程工具全解析 | 理想的彼岸
\ No newline at end of file
diff --git a/Claude/Claude Code 网页抓取：5 种方案怎么选？ 1.md b/Claude/Claude Code 网页抓取：5 种方案怎么选？ 1.md
new file mode 100644
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+++ b/Claude/Claude Code 网页抓取：5 种方案怎么选？ 1.md
@@ -0,0 +1,196 @@
+---
+title: "Claude Code 网页抓取：5 种方案怎么选？"
+source: "https://x.com/yanhua1010/status/2029042247577288915"
+author:
+ - "[[Unknown]]"
+published: 2026-03-04
+created: 2026-03-07
+description:
+tags:
+ - "clippings"
+---
+用 Claude Code 做内容创作，有一个绑不开的需求：抓网页内容。
+
+保存一篇好文章、抓取推文数据、批量采集竞品信息，都需要从网页拿数据。但 Claude Code 生态里能用的方案至少有 5 种，从内置工具到云端服务到专业爬虫框架，看起来功能重叠，第一次接触很难选。
+
+我把这 5 种方案都实测了一遍，这篇文章就是对比结果。**每种方案擅长什么、不擅长什么、什么场景该用哪个**，看完就清楚了。
+
+## 先说一个关键前提：它们不在同一个层次
+
+这 5 种方案最容易踩的坑，就是拿来直接横向比较。它们其实是不同技术层次的东西，就像螺丝刀、电钻和装修公司，各自解决不同层面的问题。
+
+**① WebFetch**（网络层）→ 最轻量的 HTTP 请求工具
+
+**② Playwright MCP**（浏览器层）→ 模拟真人操作的真实浏览器
+
+**③ Scrapling**（爬虫框架层）→ 自适应解析的 Python 爬虫框架
+
+**④ Firecrawl**（云端服务层）→ 网页数据提取的 SaaS 服务
+
+**⑤ Agent-Reach**（聚合编排层）→ 11+ 社交平台的统一接入层
+
+从上到下，层级递增，能力递增，复杂度也递增。**互补关系，不是替代关系。**
+
+
+
+下面逐个说。
+
+## 方案一：WebFetch
+
+WebFetch 是 Claude Code 自带的内置工具，零配置，开箱即用。工作方式很直接：发一个 HTTP 请求，拿到 HTML，转成 Markdown 返回。
+
+**好处**是真的轻。不用装依赖，不用配置，对静态页面几秒钟就有结果。安全性也最高，有内置沙箱，自动 HTTPS 升级，还带 15 分钟缓存。
+
+**问题**在于它不执行 JavaScript。现代网页大量依赖 JS 渲染内容，WebFetch 拿到的可能只是一个空壳。不支持登录，不能点击、滚动、填表单，碰到企业级反爬策略也可能被直接拦截。
+
+简单说，WebFetch 拿到的是「服务器返回的原始文档」，不是「你在浏览器里看到的页面」。
+
+**适合**：快速看一个博客文章、抓公开的 API 文档、获取静态页面文本。
+
+**不适合**：X/Twitter、SPA 单页应用、任何需要登录的页面。
+
+我实测了一下，用它抓 X 帖子，直接返回 "JavaScript is not available"，完全拿不到内容。
+
+## 方案二：Playwright MCP
+
+Playwright 是微软维护的浏览器自动化框架，通过 MCP 协议接入 Claude Code。它启动一个真实的 Chromium 浏览器，完整加载页面、执行 JS、渲染 DOM。
+
+和 WebFetch 最大的区别：**Playwright 拿到的是你眼睛看到的那个页面**。JS 渲染的内容、动态加载的数据，全都有。而且它能交互，点按钮、填表单、模拟滚动、截图，甚至可以在浏览器里走完登录流程。微软维护，社区成熟，文档丰富，数据也全部在本地处理。
+
+代价是重。每次操作都启动完整的 Chromium，资源消耗不小。一次只能操作一个页面，本质上是测试/自动化工具，不是为批量爬取设计的。如果你只想看一个静态博客写了什么，上 Playwright 确实有点大材小用。
+
+**适合**：JS 渲染页面（SPA、React/Vue 应用）、需要登录的内容、需要交互操作、截图。
+
+**不适合**：大规模批量爬取、高频率抓取。
+
+同样的 X 帖子测试，Playwright 完整拿到了全文、作者信息、互动数据（回复/转发/点赞/书签/浏览量）、图片链接。未登录状态就能抓取 Article 类型帖子。
+
+## 方案三：Scrapling
+
+Scrapling 是一个 Python 爬虫框架，GitHub 20.6K stars，92% 测试覆盖率。和前两个不同，它不只是「发请求拿内容」，而是一套完整的爬虫工程体系。
+
+它提供三种 Fetcher 模式可以切换：普通 HTTP 请求（Fetcher，类似 WebFetch），反反爬模式（StealthyFetcher，内置绕过 Cloudflare），浏览器渲染（DynamicFetcher，类似 Playwright）。
+
+Scrapling 最值得说的特性是**自适应解析**。做过爬虫的都知道，网站一改版，选择器就失效，得手动更新。Scrapling 用相似度算法自动重新定位元素，DOM 结构变了也能找到对应内容。对于长期运行的爬虫任务，这个能力非常实用。
+
+性能方面，官方数据是解析速度比 BeautifulSoup 快 400-600 倍。v0.4 还引入了 Spider 框架，类 Scrapy API，支持异步并发、断点续爬、流式处理。
+
+**但门槛也不低**：需要 Python 3.10+ 环境，学习曲线比前两个陡很多，也不适合交互场景。如果你只是偶尔抓一个页面，Scrapling 有点杀鸡用牛刀。它的价值在长期的、批量的、需要应对反爬的数据采集任务。
+
+它也内置了 MCP Server，可以直接在 Claude Code 中调用。
+
+## 方案四：Firecrawl
+
+Firecrawl 走的是完全不同的路线：云端 SaaS。YC 孵化，$14.5M A 轮，GitHub 69K+ stars，35 万+开发者在用。
+
+你给它一个 URL，它在云端完成所有脏活（浏览器渲染、反爬绕过、内容清洗、结构化提取），直接返回干净的 Markdown 或结构化 JSON。除了单页抓取，还支持全站爬取、站点地图发现、搜索+抓取一体化。2026 年 1 月推出的 /agent 功能可以智能导航复杂网站，自动搜索和采集数据。
+
+**最大的好处是省心**。不用装本地依赖，不用管版本兼容，有官方 MCP Server，npx -y firecrawl-mcp 一行搞定。
+
+**最大的顾虑是隐私**。你抓取的所有内容都会经过 Firecrawl 的服务器。抓公开信息（博客、文档、新闻）问题不大，但涉及敏感数据的话，建议走本地方案。另外是费用，有免费额度，但大量使用需要付费。还有服务商依赖的问题，Firecrawl 挂了你就挂了。
+
+## 方案五：Agent-Reach
+
+Agent-Reach 和前四个都不一样。它不是一个独立的爬虫引擎，而是一个「脚手架」，把 X/Twitter、YouTube、B 站、小红书、Reddit、LinkedIn 等 11+ 平台的最佳抓取工具整合在一起，通过 MCP 协议提供统一入口。
+
+YouTube 用 yt-dlp（148K stars），X 用 xreach CLI（cookie 认证），通用网页用 Jina Reader。每个平台挑最合适的工具来做。本地运行，免费开源。
+
+问题是**依赖链太长**。底层挂着 yt-dlp、xreach、Jina Reader、feedparser、gh CLI 这一堆工具，任何一个出问题都会影响整体。项目相对较新（GitHub 4.2K stars，v1.2.0），稳定性还在验证中，多工具版本兼容的维护成本也不低。
+
+它的价值在「广度」而不是「深度」。不会比 Firecrawl 更擅长抓网页，也不会比 Scrapling 更擅长反爬，但如果你需要同时从多个社交平台采集信息，它能省不少事。
+
+## 横向对比
+
+把几个关键维度放在一起看：
+
+**JS 渲染**：WebFetch 不行，Playwright 和 Firecrawl 原生支持，Scrapling 可选，Agent-Reach 取决于底层工具。
+
+**交互能力**（点击、填表、滚动）：只有 Playwright 能做，其他四个都不行。
+
+**批量爬取**：Scrapling 和 Firecrawl 的核心能力，其他三个不适合。
+
+**反反爬**：Scrapling 内置，Firecrawl 云端处理，Playwright 有限支持，WebFetch 和 Agent-Reach 基本没有。
+
+**数据隐私**：Firecrawl 的数据经过云端，其他四个都是本地处理。
+
+**配置难度**：WebFetch 零配置，Firecrawl 低门槛（API Key），Playwright 和 Agent-Reach 中等，Scrapling 最高。
+
+**费用**：只有 Firecrawl 收费（有免费额度），其他都免费。
+
+## 实测：抓 X 帖子
+
+光说不练没用，我拿一条自己的 X 帖子做了测试。
+
+**WebFetch**：失败。返回 "JavaScript is not available"。X 是 SPA 应用，内容全靠 JS 渲染，WebFetch 拿到的就是一个空壳。
+
+**Playwright MCP**：成功。完整拿到全文、作者、发布时间、互动数据、图片链接。未登录状态就能搞定。
+
+
+
+结论很直接：X 这类现代 SPA 应用，必须用浏览器级别的方案。需要登录才能看的内容，可以在 Playwright 中走登录流程，或者用 Agent-Reach 的 xreach（cookie 认证）。
+
+## 选型建议
+
+别纠结「哪个最好」，按场景选：
+
+**快速看一个静态页面** → WebFetch，零配置最轻量。
+
+**页面需要 JS 渲染或登录** → Playwright MCP，需要真实浏览器。
+
+**需要点击、填表等交互** → Playwright MCP，五个方案里唯一能做的。
+
+**批量提取结构化数据** → Scrapling，这是它的主场。
+
+**网站有 Cloudflare 防护** → Scrapling StealthyFetcher，专门干这个的。
+
+**长期监控网站变化** → Scrapling，自适应解析 + 断点续爬。
+
+**大规模全站爬取** → Firecrawl，云端处理，省心。
+
+**跨多个社交平台采集** → Agent-Reach，11+ 平台统一入口。
+
+**在意数据隐私** → Scrapling 或 Agent-Reach，纯本地。
+
+**不想维护工具链** → Firecrawl，SaaS 零维护。
+
+## 我在用的组合
+
+
+
+实际用下来，单选一个是不够的，组合才好用。
+
+**日常组合：WebFetch + Playwright MCP**
+
+大部分场景用 WebFetch 先试，静态页面直接搞定。遇到 JS 渲染或需要交互的再切 Playwright。这两个覆盖了我 80% 的日常需求。
+
+**进阶组合：加上 Scrapling MCP**
+
+如果你有批量采集或长期监控的需求，Scrapling 的批量调度和自适应解析是前两个做不到的。三者都本地运行，数据不外传。
+
+**全覆盖组合：再加 Agent-Reach**
+
+需要同时从 YouTube、B 站、小红书等多平台采集，Agent-Reach 能省不少事。但要有心理准备，依赖链长，维护成本也高。
+
+## 几个容易忽略的点
+
+**WebFetch 不等于浏览器。** 很多人第一次用 WebFetch 会困惑「为什么拿不到内容」，原因就是它不执行 JS。现在大部分网站都有 JS 渲染的内容，所以 WebFetch 的适用范围比你想象的窄。
+
+**Playwright 不适合批量。** 它启动的是完整的 Chromium，一次操作一个页面。拿它做批量爬虫，就像开法拉利送外卖，性能浪费严重。
+
+**Firecrawl 的隐私问题不能忽视。** 所有数据经过第三方服务器处理。如果你只是抓公开文章，问题不大。但如果涉及商业敏感信息或用户数据，请选本地方案。
+
+**Agent-Reach 本质是编排层。** 它自己不做抓取，底层全靠第三方工具。升级某个依赖可能引发连锁问题，心里要有数。
+
+## 最后
+
+这 5 种方案各自擅长不同层次的问题，没有「最好」的方案，只有「最合适」的方案。
+
+如果你刚开始用 Claude Code 做网页抓取，我的建议是：**先从 WebFetch 开始，不够用了上 Playwright，有批量需求再上 Scrapling。** 按需升级，别过度配置。
+
+附录：
+
+Scrapling: [https://github.com/D4Vinci/Scrapling?tab=readme-ov-file](https://github.com/D4Vinci/Scrapling?tab=readme-ov-file)，
+
+Agent fetch: [https://github.com/Panniantong/Agent-Reach](https://github.com/Panniantong/Agent-Reach)
+
+Playwright: [https://github.com/microsoft/playwright](https://github.com/microsoft/playwright)
\ No newline at end of file
diff --git a/Claude/Claude Code 网页抓取：5 种方案怎么选？.md b/Claude/Claude Code 网页抓取：5 种方案怎么选？.md
new file mode 100644
index 0000000..0711b4d
--- /dev/null
+++ b/Claude/Claude Code 网页抓取：5 种方案怎么选？.md
@@ -0,0 +1,196 @@
+---
+title: "Claude Code 网页抓取：5 种方案怎么选？"
+source: "https://x.com/yanhua1010/status/2029042247577288915"
+author:
+ - "[[Unknown]]"
+published: 2026-03-04
+created: 2026-03-04
+description:
+tags:
+ - "clippings"
+---
+用 Claude Code 做内容创作，有一个绑不开的需求：抓网页内容。
+
+保存一篇好文章、抓取推文数据、批量采集竞品信息，都需要从网页拿数据。但 Claude Code 生态里能用的方案至少有 5 种，从内置工具到云端服务到专业爬虫框架，看起来功能重叠，第一次接触很难选。
+
+我把这 5 种方案都实测了一遍，这篇文章就是对比结果。**每种方案擅长什么、不擅长什么、什么场景该用哪个**，看完就清楚了。
+
+## 先说一个关键前提：它们不在同一个层次
+
+这 5 种方案最容易踩的坑，就是拿来直接横向比较。它们其实是不同技术层次的东西，就像螺丝刀、电钻和装修公司，各自解决不同层面的问题。
+
+**① WebFetch**（网络层）→ 最轻量的 HTTP 请求工具
+
+**② Playwright MCP**（浏览器层）→ 模拟真人操作的真实浏览器
+
+**③ Scrapling**（爬虫框架层）→ 自适应解析的 Python 爬虫框架
+
+**④ Firecrawl**（云端服务层）→ 网页数据提取的 SaaS 服务
+
+**⑤ Agent-Reach**（聚合编排层）→ 11+ 社交平台的统一接入层
+
+从上到下，层级递增，能力递增，复杂度也递增。**互补关系，不是替代关系。**
+
+
+
+下面逐个说。
+
+## 方案一：WebFetch
+
+WebFetch 是 Claude Code 自带的内置工具，零配置，开箱即用。工作方式很直接：发一个 HTTP 请求，拿到 HTML，转成 Markdown 返回。
+
+**好处**是真的轻。不用装依赖，不用配置，对静态页面几秒钟就有结果。安全性也最高，有内置沙箱，自动 HTTPS 升级，还带 15 分钟缓存。
+
+**问题**在于它不执行 JavaScript。现代网页大量依赖 JS 渲染内容，WebFetch 拿到的可能只是一个空壳。不支持登录，不能点击、滚动、填表单，碰到企业级反爬策略也可能被直接拦截。
+
+简单说，WebFetch 拿到的是「服务器返回的原始文档」，不是「你在浏览器里看到的页面」。
+
+**适合**：快速看一个博客文章、抓公开的 API 文档、获取静态页面文本。
+
+**不适合**：X/Twitter、SPA 单页应用、任何需要登录的页面。
+
+我实测了一下，用它抓 X 帖子，直接返回 "JavaScript is not available"，完全拿不到内容。
+
+## 方案二：Playwright MCP
+
+Playwright 是微软维护的浏览器自动化框架，通过 MCP 协议接入 Claude Code。它启动一个真实的 Chromium 浏览器，完整加载页面、执行 JS、渲染 DOM。
+
+和 WebFetch 最大的区别：**Playwright 拿到的是你眼睛看到的那个页面**。JS 渲染的内容、动态加载的数据，全都有。而且它能交互，点按钮、填表单、模拟滚动、截图，甚至可以在浏览器里走完登录流程。微软维护，社区成熟，文档丰富，数据也全部在本地处理。
+
+代价是重。每次操作都启动完整的 Chromium，资源消耗不小。一次只能操作一个页面，本质上是测试/自动化工具，不是为批量爬取设计的。如果你只想看一个静态博客写了什么，上 Playwright 确实有点大材小用。
+
+**适合**：JS 渲染页面（SPA、React/Vue 应用）、需要登录的内容、需要交互操作、截图。
+
+**不适合**：大规模批量爬取、高频率抓取。
+
+同样的 X 帖子测试，Playwright 完整拿到了全文、作者信息、互动数据（回复/转发/点赞/书签/浏览量）、图片链接。未登录状态就能抓取 Article 类型帖子。
+
+## 方案三：Scrapling
+
+Scrapling 是一个 Python 爬虫框架，GitHub 20.6K stars，92% 测试覆盖率。和前两个不同，它不只是「发请求拿内容」，而是一套完整的爬虫工程体系。
+
+它提供三种 Fetcher 模式可以切换：普通 HTTP 请求（Fetcher，类似 WebFetch），反反爬模式（StealthyFetcher，内置绕过 Cloudflare），浏览器渲染（DynamicFetcher，类似 Playwright）。
+
+Scrapling 最值得说的特性是**自适应解析**。做过爬虫的都知道，网站一改版，选择器就失效，得手动更新。Scrapling 用相似度算法自动重新定位元素，DOM 结构变了也能找到对应内容。对于长期运行的爬虫任务，这个能力非常实用。
+
+性能方面，官方数据是解析速度比 BeautifulSoup 快 400-600 倍。v0.4 还引入了 Spider 框架，类 Scrapy API，支持异步并发、断点续爬、流式处理。
+
+**但门槛也不低**：需要 Python 3.10+ 环境，学习曲线比前两个陡很多，也不适合交互场景。如果你只是偶尔抓一个页面，Scrapling 有点杀鸡用牛刀。它的价值在长期的、批量的、需要应对反爬的数据采集任务。
+
+它也内置了 MCP Server，可以直接在 Claude Code 中调用。
+
+## 方案四：Firecrawl
+
+Firecrawl 走的是完全不同的路线：云端 SaaS。YC 孵化，$14.5M A 轮，GitHub 69K+ stars，35 万+开发者在用。
+
+你给它一个 URL，它在云端完成所有脏活（浏览器渲染、反爬绕过、内容清洗、结构化提取），直接返回干净的 Markdown 或结构化 JSON。除了单页抓取，还支持全站爬取、站点地图发现、搜索+抓取一体化。2026 年 1 月推出的 /agent 功能可以智能导航复杂网站，自动搜索和采集数据。
+
+**最大的好处是省心**。不用装本地依赖，不用管版本兼容，有官方 MCP Server，npx -y firecrawl-mcp 一行搞定。
+
+**最大的顾虑是隐私**。你抓取的所有内容都会经过 Firecrawl 的服务器。抓公开信息（博客、文档、新闻）问题不大，但涉及敏感数据的话，建议走本地方案。另外是费用，有免费额度，但大量使用需要付费。还有服务商依赖的问题，Firecrawl 挂了你就挂了。
+
+## 方案五：Agent-Reach
+
+Agent-Reach 和前四个都不一样。它不是一个独立的爬虫引擎，而是一个「脚手架」，把 X/Twitter、YouTube、B 站、小红书、Reddit、LinkedIn 等 11+ 平台的最佳抓取工具整合在一起，通过 MCP 协议提供统一入口。
+
+YouTube 用 yt-dlp（148K stars），X 用 xreach CLI（cookie 认证），通用网页用 Jina Reader。每个平台挑最合适的工具来做。本地运行，免费开源。
+
+问题是**依赖链太长**。底层挂着 yt-dlp、xreach、Jina Reader、feedparser、gh CLI 这一堆工具，任何一个出问题都会影响整体。项目相对较新（GitHub 4.2K stars，v1.2.0），稳定性还在验证中，多工具版本兼容的维护成本也不低。
+
+它的价值在「广度」而不是「深度」。不会比 Firecrawl 更擅长抓网页，也不会比 Scrapling 更擅长反爬，但如果你需要同时从多个社交平台采集信息，它能省不少事。
+
+## 横向对比
+
+把几个关键维度放在一起看：
+
+**JS 渲染**：WebFetch 不行，Playwright 和 Firecrawl 原生支持，Scrapling 可选，Agent-Reach 取决于底层工具。
+
+**交互能力**（点击、填表、滚动）：只有 Playwright 能做，其他四个都不行。
+
+**批量爬取**：Scrapling 和 Firecrawl 的核心能力，其他三个不适合。
+
+**反反爬**：Scrapling 内置，Firecrawl 云端处理，Playwright 有限支持，WebFetch 和 Agent-Reach 基本没有。
+
+**数据隐私**：Firecrawl 的数据经过云端，其他四个都是本地处理。
+
+**配置难度**：WebFetch 零配置，Firecrawl 低门槛（API Key），Playwright 和 Agent-Reach 中等，Scrapling 最高。
+
+**费用**：只有 Firecrawl 收费（有免费额度），其他都免费。
+
+## 实测：抓 X 帖子
+
+光说不练没用，我拿一条自己的 X 帖子做了测试。
+
+**WebFetch**：失败。返回 "JavaScript is not available"。X 是 SPA 应用，内容全靠 JS 渲染，WebFetch 拿到的就是一个空壳。
+
+**Playwright MCP**：成功。完整拿到全文、作者、发布时间、互动数据、图片链接。未登录状态就能搞定。
+
+
+
+结论很直接：X 这类现代 SPA 应用，必须用浏览器级别的方案。需要登录才能看的内容，可以在 Playwright 中走登录流程，或者用 Agent-Reach 的 xreach（cookie 认证）。
+
+## 选型建议
+
+别纠结「哪个最好」，按场景选：
+
+**快速看一个静态页面** → WebFetch，零配置最轻量。
+
+**页面需要 JS 渲染或登录** → Playwright MCP，需要真实浏览器。
+
+**需要点击、填表等交互** → Playwright MCP，五个方案里唯一能做的。
+
+**批量提取结构化数据** → Scrapling，这是它的主场。
+
+**网站有 Cloudflare 防护** → Scrapling StealthyFetcher，专门干这个的。
+
+**长期监控网站变化** → Scrapling，自适应解析 + 断点续爬。
+
+**大规模全站爬取** → Firecrawl，云端处理，省心。
+
+**跨多个社交平台采集** → Agent-Reach，11+ 平台统一入口。
+
+**在意数据隐私** → Scrapling 或 Agent-Reach，纯本地。
+
+**不想维护工具链** → Firecrawl，SaaS 零维护。
+
+## 我在用的组合
+
+
+
+实际用下来，单选一个是不够的，组合才好用。
+
+**日常组合：WebFetch + Playwright MCP**
+
+大部分场景用 WebFetch 先试，静态页面直接搞定。遇到 JS 渲染或需要交互的再切 Playwright。这两个覆盖了我 80% 的日常需求。
+
+**进阶组合：加上 Scrapling MCP**
+
+如果你有批量采集或长期监控的需求，Scrapling 的批量调度和自适应解析是前两个做不到的。三者都本地运行，数据不外传。
+
+**全覆盖组合：再加 Agent-Reach**
+
+需要同时从 YouTube、B 站、小红书等多平台采集，Agent-Reach 能省不少事。但要有心理准备，依赖链长，维护成本也高。
+
+## 几个容易忽略的点
+
+**WebFetch 不等于浏览器。** 很多人第一次用 WebFetch 会困惑「为什么拿不到内容」，原因就是它不执行 JS。现在大部分网站都有 JS 渲染的内容，所以 WebFetch 的适用范围比你想象的窄。
+
+**Playwright 不适合批量。** 它启动的是完整的 Chromium，一次操作一个页面。拿它做批量爬虫，就像开法拉利送外卖，性能浪费严重。
+
+**Firecrawl 的隐私问题不能忽视。** 所有数据经过第三方服务器处理。如果你只是抓公开文章，问题不大。但如果涉及商业敏感信息或用户数据，请选本地方案。
+
+**Agent-Reach 本质是编排层。** 它自己不做抓取，底层全靠第三方工具。升级某个依赖可能引发连锁问题，心里要有数。
+
+## 最后
+
+这 5 种方案各自擅长不同层次的问题，没有「最好」的方案，只有「最合适」的方案。
+
+如果你刚开始用 Claude Code 做网页抓取，我的建议是：**先从 WebFetch 开始，不够用了上 Playwright，有批量需求再上 Scrapling。** 按需升级，别过度配置。
+
+附录：
+
+Scrapling: [https://github.com/D4Vinci/Scrapling?tab=readme-ov-file](https://github.com/D4Vinci/Scrapling?tab=readme-ov-file)，
+
+Agent fetch: [https://github.com/Panniantong/Agent-Reach](https://github.com/Panniantong/Agent-Reach)
+
+Playwright: [https://github.com/microsoft/playwright](https://github.com/microsoft/playwright)
\ No newline at end of file
diff --git a/Claude/Claude Code 速查表.md b/Claude/Claude Code 速查表.md
new file mode 100644
index 0000000..b24a6e8
--- /dev/null
+++ b/Claude/Claude Code 速查表.md
@@ -0,0 +1,367 @@
+---
+title: "Claude Code 速查表"
+source: "https://banwagong1.com/claude-code.html"
+author:
+published:
+created: 2026-03-25
+description:
+tags:
+ - "clippings"
+---
+CtrlC 取消输入/生成
+
+CtrlD 退出会话
+
+CtrlL 清屏
+
+CtrlO 切换详细输出
+
+CtrlR 反向搜索历史
+
+CtrlG 在编辑器中打开提示
+
+CtrlB 后台运行任务
+
+CtrlT 切换任务列表
+
+AltV 粘贴图片
+
+CtrlF 终止后台代理（按两次）
+
+EscEsc 回退 / 撤销
+
+ShiftTab 循环切换权限模式
+
+AltP 切换模型
+
+AltT 切换思考模式
+
+输入
+
+\\Enter 换行（快捷）
+
+CtrlJ 换行（控制序列）
+
+前缀
+
+/ 斜杠命令
+
+! 直接执行 bash
+
+@ 文件引用 + 自动补全
+
+↑↓ 导航
+
+←→ 展开/折叠
+
+P 预览
+
+R 重命名
+
+/ 搜索
+
+A 所有项目
+
+B 当前分支
+
+\--transport http 远程 HTTP（推荐）
+
+\--transport stdio 本地进程
+
+\--transport sse 远程 SSE
+
+Local ~/.claude.json（每个项目）
+
+Project.mcp.json（共享/VCS）
+
+User ~/.claude.json（全局）
+
+管理
+
+/mcp 交互式界面
+
+claude mcp list 列出所有服务器
+
+claude mcp serve CC 作为 MCP 服务器
+
+Elicitation 服务器在任务中请求输入NEW
+
+会话
+
+/clear 清除对话
+
+/compact \[focus\] 压缩上下文
+
+/resume 恢复/切换会话
+
+/rename \[name\] 命名当前会话
+
+/branch \[name\] 分支对话（/fork 别名）
+
+/cost Token 用量统计
+
+/context 可视化上下文（网格）
+
+/diff 交互式差异查看器
+
+/copy 复制上次回复
+
+/export 导出对话
+
+配置
+
+/config 打开设置
+
+/model \[model\] 切换模型（←→ 努力度）
+
+/fast \[on|off\] 切换快速模式
+
+/vim 切换 vim 模式
+
+/theme 更改颜色主题
+
+/permissions 查看/更新权限
+
+/effort \[level\] 设置努力度（low/med/high）NEW
+
+/color \[color\] 设置提示栏颜色
+
+工具
+
+/init 创建 CLAUDE.md
+
+/memory 编辑 CLAUDE.md 文件
+
+/mcp 管理 MCP 服务器
+
+/hooks 管理钩子
+
+/skills 列出可用技能
+
+/agents 管理代理
+
+/chrome Chrome 集成
+
+/reload-plugins 热重载插件
+
+特殊
+
+/btw 附带提问（无上下文）
+
+/plan \[desc\] 计划模式（+ 自动启动）
+
+/loop \[interval\] 调度周期性任务
+
+/voice 按键说话语音（20 种语言）
+
+/doctor 诊断安装问题
+
+/rc 启用远程控制
+
+/stats 使用连续记录和偏好
+
+/insights 分析会话报告
+
+/desktop 在桌面应用中继续
+
+/remote-control 桥接终端到 claude.ai/codeNEW
+
+/stickers 订购贴纸！🎉
+
+./CLAUDE.md 项目（团队共享）
+
+~/.claude/CLAUDE.md 个人（所有项目）
+
+/etc/claude-code/ 管理（组织级）
+
+.claude/rules/\*.md 项目规则
+
+~/.claude/rules/\*.md 用户规则
+
+paths: frontmatter 路径特定规则
+
+@path/to/file 在 CLAUDE.md 中导入
+
+~/.claude/projects//memory/
+
+MEMORY.md + 主题文件，自动加载
+
+ShiftTab Normal → Auto → Plan
+
+\--permission-mode plan 以计划模式启动
+
+AltT 切换思考开/关
+
+"ultrathink" 当前轮次最大努力度
+
+CtrlO 查看思考（详细模式）
+
+/effort ○ low · ◐ med · ● highNEW
+
+Git Worktrees
+
+\--worktree name 每个功能独立分支
+
+isolation: worktree 代理在独立 worktree 中
+
+sparsePaths 仅检出所需目录NEW
+
+/batch 自动创建 worktrees
+
+/voice 启用按键说话
+
+Space (hold) 录音，释放发送
+
+20 种语言 EN, ES, FR, DE, CZ, PL…
+
+/context 用量 + 优化建议
+
+/compact \[focus\] 带焦点压缩
+
+Auto-compact 约 95% 容量时触发
+
+1M context Opus 4.6 (Max/Team/Ent)
+
+CLAUDE.md 压缩后保留！
+
+claude -c 继续上次对话
+
+claude -r "name" 按名称恢复
+
+/btw question 附带提问，无上下文开销
+
+claude -p "query" 非交互式
+
+\--output-format json 结构化输出
+
+\--max-budget-usd 5 费用上限
+
+cat file | claude -p 管道输入
+
+/loop 5m msg 周期性任务
+
+/rc 远程控制
+
+\--remote claude.ai 上的 Web 会话
+
+~/.claude/settings.json 用户设置
+
+.claude/settings.json 项目（共享）
+
+.claude/settings.local.json 仅本地
+
+~/.claude.json OAuth, MCP, 状态
+
+.mcp.json 项目 MCP 服务器
+
+modelOverrides 模型选择器映射自定义 ID
+
+autoMemoryDirectory 自定义记忆目录
+
+worktree.sparsePaths 稀疏检出目录NEW
+
+ANTHROPIC\_API\_KEY
+
+ANTHROPIC\_MODEL
+
+CLAUDE\_CODE\_EFFORT\_LEVEL low/med/high
+
+MAX\_THINKING\_TOKENS 0=关闭
+
+ANTHROPIC\_CUSTOM\_MODEL\_OPTION 自定义 /model 条目
+
+CLAUDE\_CODE\_PLUGIN\_SEED\_DIR 多个插件种子目录
+
+/simplify 代码审查（3 个并行代理）
+
+/batch 大规模并行修改（5-30 worktrees）
+
+/debug \[desc\] 从调试日志排查问题
+
+/loop \[interval\] 周期性调度任务
+
+/claude-api 加载 API + SDK 参考
+
+.claude/skills// 项目技能
+
+~/.claude/skills// 个人技能
+
+description 自动调用触发器
+
+allowed-tools 跳过权限提示
+
+model 覆盖技能模型
+
+effort 覆盖努力度NEW
+
+context: fork 在子代理中运行
+
+$ARGUMENTS 用户输入占位符
+
+${CLAUDE\_SKILL\_DIR} 技能自身目录
+
+!\`cmd\` 动态上下文注入
+
+Explore 快速只读 (Haiku)
+
+Plan 计划模式研究
+
+General 全工具，复杂任务
+
+Bash 终端独立上下文
+
+permissionMode default/acceptEdits/dontAsk/plan
+
+isolation: worktree 在 git worktree 中运行
+
+memory: user|project 持久化记忆
+
+background: true 后台任务
+
+maxTurns 限制代理轮次
+
+SendMessage 恢复代理（替代 resume）NEW
+
+claude 交互式
+
+claude "q" 带提示
+
+claude -p "q" 无头模式
+
+claude -c 继续上次
+
+claude -r "n" 恢复
+
+claude update 更新
+
+\--model 设置模型
+
+\-w Git worktree
+
+\-n / --name 会话名称
+
+\--add-dir 添加目录
+
+\--agent 使用代理
+
+\--allowedTools 预批准
+
+\--output-format json/stream
+
+\--json-schema 结构化
+
+\--max-turns 限制轮次
+
+\--max-budget-usd 费用上限
+
+\--console 通过 Anthropic Console 认证
+
+\--verbose 详细模式
+
+\--bare 最小化无头（无 hooks/LSP）NEW
+
+\--channels 权限中继 / MCP 推送NEW
+
+\--remote Web 会话
+
+\--chrome Chrome
\ No newline at end of file
diff --git a/Claude/Claude Code's Limits Are Generous. The Problem Is Your Harness.md b/Claude/Claude Code's Limits Are Generous. The Problem Is Your Harness.md
new file mode 100644
index 0000000..49c31d7
--- /dev/null
+++ b/Claude/Claude Code's Limits Are Generous. The Problem Is Your Harness.md
@@ -0,0 +1,274 @@
+---
+title: "Claude Code's Limits Are Generous. The Problem Is Your Harness."
+source: "https://x.com/PawelHuryn/status/2048170309396926577"
+author:
+ - "[[@PawelHuryn]]"
+published: 2026-04-26
+created: 2026-04-26
+description: "Same PM workflow as my $750/mo era. Anthropic fixed 3 bugs. The 4 root causes still on your side, with copy-paste templates.I'm on Claude Co..."
+tags:
+ - "clippings"
+---
+
+
+Same PM workflow as my $750/mo era. Anthropic fixed 3 bugs. The 4 root causes still on your side, with copy-paste templates.
+
+I'm on Claude Code Max (20x). 3 days in. 12% used. Same workflow that cost me $750/mo (Max 20x + extra usage) a month ago. No model swap, no skipped sessions.
+
+Between March 23 and April 23, some users on Max burned weekly quotas in 1-2 days. Anthropic shipped 3 bug fixes (**v2.1.116+**) and reset all subscriber limits. Full writeup in their [April 23 postmortem](https://x.com/ClaudeDevs/status/2047371123185287223).
+
+Four root causes still on your side:
+
+1. **Cache misses**
+2. **Context bloat**
+3. **Wrong model or effort**
+4. **Wrong input format**
+
+
+
+# 1\. Cache Misses
+
+The prompt cache is the single biggest lever in Claude Code. Most people never look at it.
+
+The math:
+
+- Cache **read**: 0.1× the input price
+- Cache **write** (5-min TTL): 1.25×
+- Cache **write** (1-hour TTL): 2×
+- Cache **refresh on hit**: free (charged at the read price)
+
+Every hit on a cached prefix resets its TTL at no extra cost. So a long session with steady tool use stays warm indefinitely as long as the prefix doesn't change.
+
+Thariq spelled out the rules in ["Lessons from Building Claude Code"](https://x.com/trq212/status/2024574133011673516). The two that move the needle:
+
+- **Lock your tools at session start.** Adding or removing a tool mid-session invalidates the cached prefix and forces a full re-read. Never add or remove tools mid-session.
+- **Lock your model at session start.** Switching models mid-session blows the cache for the same reason.
+
+
+
+Source: Lessons from Building Claude Code: Prompt Caching Is Everything by Thariq ([https://x.com/trq212/status/2024574133011673516](https://x.com/trq212/status/2024574133011673516))
+
+**~90% is healthy on the 5-min default.** On the 1-hour TTL it climbs to ~97-99%, but that one is API-only and isn't priced into Pro / Max / Team subscriptions.
+
+**What to Do:**
+
+- **Protect the prefix.** Don't add MCP servers mid-session, don't **/model** mid-session.
+- **Watch the hit rate.** See § Watch the Number below.
+- **Consider the 1h cache** for long sessions on the API. Write costs more (2× vs 1.25×), but the higher hit rate pays it back.
+
+# 2\. Context Bloat
+
+For Opus 4.7, 1M context is the default. That's expensive. Long sessions sprawl, and auto-compact fires later than it should. Disable it and fall back to 200K. 200K is enough for almost any task.
+
+On 200K, auto-compact fires at ~155K (~80%), the behavior Boris Cherny [described](https://x.com/bcherny/status/1977163445205450783) for the previous Opus. The trick is to compact before you hit the auto-trigger. Once it fires, it pushes you over and warms a fresh prefix. Compact early instead.
+
+The settings I run:
+
+```json
+{
+ "env": {
+ "CLAUDE_CODE_DISABLE_1M_CONTEXT": "1",
+ "CLAUDE_AUTOCOMPACT_PCT_OVERRIDE": "80"
+ }
+}
+```
+
+That disables 1M context and pins the auto-compact threshold at 80%.
+
+## 2.1 Five Session Moves
+
+
+
+Source: Using Claude Code: Session Management & 1M Context by Thariq ([https://x.com/trq212/status/2044548257058328723](https://x.com/trq212/status/2044548257058328723))
+
+- **/compact** at 50% or after every task. Don't wait for auto.
+- **/clear** between unrelated work. New session = fresh prefix.
+- **/rewind** when a turn went sideways. Cheaper than re-prompting around the bad context.
+- **Subagents** for anything that doesn't need the parent's reasoning.
+
+## 2.2 Subagents Are the Underused Move
+
+Anything that's bulk-mechanical, scoped research, or parallelizable should run in a subagent. The parent context stays clean and you parallelize across cheaper models.
+
+A CLAUDE.md task-delegation block I keep in every project:
+
+```markdown
+## Task Delegation
+
+Spawn subagents to isolate context, parallelize independent work, or offload bulk mechanical tasks. Don't spawn when the parent needs the reasoning, when synthesis requires holding things together, or when spawn overhead dominates.
+
+Pick the cheapest model that can do the subtask well:
+- Haiku: bulk mechanical work, no judgment
+- Sonnet: scoped research, code exploration, in-scope synthesis
+- Opus: subtasks needing real planning or tradeoffs
+
+If a subagent realizes it needs a higher tier than itself, return to the parent.
+
+Parent owns final output and cross-spawn synthesis. User instructions override.
+```
+
+## 2.3 Skills Can Also Be Invoked as Agents
+
+Add **agent: true** and **model:** to the frontmatter and the skill runs in its own subagent context with its own model. For example:
+
+```markdown
+---
+name: tldr-pdf
+description: Extract a 200-word TL;DR from a PDF without loading the full text into the parent context
+agent: true
+model: sonnet
+---
+
+You receive a path to a PDF.
+
+1. Run \`pdftotext "$1" -\` to extract the text.
+2. Read the output.
+3. Return only:
+ - 5-bullet TL;DR
+ - 3 quotes worth keeping
+ - Any URLs cited
+
+Never return the full text. Never expand beyond the structure above.
+```
+
+The parent gets back 200 words. The full PDF never touches its context.
+
+## 2.4 A Few More Tools that Pay for Themselves on Long Sessions
+
+- **Load lean.** Disable unused MCP servers, tools, skills, and plugins. Move rules out of CLAUDE.md and into skills, custom tools, or referenced .MD files (progressive disclosure).
+- **Spec prompts.** Write requests like a spec: file paths, components, expected I/O, constraints. Vague requests burn turns and tokens.
+- **Skip the search.** When you know the file, tag it @ docs/design.md or @ research.md. Claude doesn't need to grep for what you can hand it directly.
+- [rtk-ai/rtk](https://github.com/rtk-ai/rtk). CLI proxy that strips redundant whitespace and compresses tool output. ~20-30% token reduction in my measurements.
+
+
+
+- [juliusbrussee/caveman](https://github.com/juliusbrussee/caveman). Terse-output skill. Drops conversational filler from responses without affecting the model's actual reasoning.
+
+
+
+# 3\. Wrong Model or Effort
+
+Three separate dials. Both burn tokens fast if you leave them on the wrong setting.
+
+## 3.1 Effort
+
+Default reasoning burns ~2× the tokens of medium for most tasks. Set it on the prompt that needs the headroom, not the whole session.
+
+```markdown
+/effort low # quick fixes, mechanical tasks
+/effort medium # most prompts (huge savings vs default)
+/effort high # demanding reasoning
+/effort xhigh # default for agentic coding (4.7)
+/effort max # diminishing returns; rarely worth the ~2× xhigh cost
+```
+
+
+
+Source: Introducing Claude Opus 4.7 ([https://www.anthropic.com/news/claude-opus-4-7](https://www.anthropic.com/news/claude-opus-4-7))
+
+Per prompt, not per task or session.
+
+## 3.2 Route in (CLAUDE.md)
+
+Pick the session model at start. You can't switch mid-session without nuking the cache (§1). Two options:
+
+- **Sonnet session.** Cheaper, but no Opus available. Good when you know the work is in-scope for Sonnet.
+- **Opus session + delegate.** Pay for Opus on the parent (planning, tradeoffs), delegate the rest. My default for mixed work.
+
+Then spell out delegation in CLAUDE.md: Haiku for mechanical work, Sonnet for scoped research, Opus for tradeoffs. Opus 4.7 delegates less than 4.6, so you have to ask. See § 2 (Task Delegation) for the full block.
+
+## 3.3 Route Out
+
+If you're hitting Pro / Max / Team limits but want to keep the Claude Code interface, route to OpenRouter or another provider. GLM-5.1 ≈ Opus at ~1/12× the cost.
+
+```json
+{
+ "env": {
+ "ANTHROPIC_BASE_URL": "https://openrouter.ai/api",
+ "ANTHROPIC_AUTH_TOKEN": "{YOUR-API-KEY}",
+ "ANTHROPIC_API_KEY": ""
+ },
+ "model": "z-ai/glm-5.1"
+}
+```
+
+# 4\. Wrong Input Format
+
+Some inputs are token-expensive by default. Three swaps cover most of it.
+
+## 4.1 Screenshots and Chrome Scraping → agent-browser
+
+[vercel-labs/agent-browser](https://github.com/vercel-labs/agent-browser) browses pages via the accessibility tree instead of rendering and screenshotting. ~82% fewer tokens than Playwright MCP for the same task. Use it instead of "Claude in Chrome" when you're scraping or doing research from web pages.
+
+```powershell
+npm install -g agent-browser
+agent-browser install # Download Chrome from Chrome for Testing (first time only)
+```
+
+## 4.2 PDFs → pdftotext, Not Claude's PDF Reader
+
+The Read tool loads PDFs as images, which is expensive. Extract text first, then read:
+
+The CLAUDE.md fragment that codifies these defaults (4.1, 4.2):
+
+```markdown
+## Preferred Tools
+
+### Data Fetching
+
+1. **WebFetch**: free, text-only, works on public pages that don't block bots.
+2. **agent-browser CLI**: free, local Rust CLI + Chrome via CDP. For dynamic pages or auth walls that WebFetch can't handle. Returns the accessibility tree with element refs (@e1, @e2). ~82% fewer tokens than screenshot-based tools. Install: \`npm i -g agent-browser && agent-browser install\`. Use \`snapshot\` for AI-friendly DOM state, element refs for interaction.
+3. **Notice recurring fetch patterns and propose wrapping them as dedicated tools.** When the same fetch/parse logic comes up more than once, suggest wrapping it as a named tool (e.g. a skill file or a .py script that calls \`agent-browser\` with the snapshot and extraction steps baked in for that source). Add the entry to \`## Dedicated Tools\` below and reference it by name on future calls.
+
+### PDF Files
+
+Use 'pdftotext', not the 'Read' tool. Use 'Read' only when the user directly asks to analyze images or charts inside the document. Read loads PDFs as images.
+
+## Dedicated Tools
+
+
+```
+
+## 4.3 Large Repos → Code Graph, Not Raw File Reads
+
+[tirth8205/code-review-graph](https://github.com/tirth8205/code-review-graph) builds a persistent AST map of your codebase. Claude reads only what matters. Claimed 6.8× fewer tokens on reviews, up to 49× on daily coding tasks.
+
+
+
+AI coding tools re-read your entire codebase on every task. code-review-graph fixes that. It builds a structural map of your code with Tree-sitter, tracks changes incrementally, and gives your AI assistant precise context via MCP so it reads only what matters.
+
+# 5\. Watch the Number
+
+Three dashboards, depending on your tier:
+
+## 5.1 Historical (Pro / Max / Team)
+
+[phuryn/claude-usage](https://github.com/phuryn/claude-usage)**:** Long-term breakdown by session, day, week, and all-time. Use it to find where the spend went.
+
+
+
+[https://github.com/phuryn/claude-usage](https://github.com/phuryn/claude-usage)
+
+## 5.2 Real-Time
+
+[Gronsten/claude-usage-monitor](https://github.com/Gronsten/claude-usage-monitor)**:** Current 5-hour window + active session tokens, with color thresholds. Use it to know how close you are to your cap right now.
+
+
+
+[https://github.com/Gronsten/claude-usage-monitor](https://github.com/Gronsten/claude-usage-monitor)
+
+## 5.3 API: Anthropic's Own Cache Dashboard (API users)
+
+At platform·claude·com/usage/cache. API only, separate from Pro / Max / Team subscriptions monitoring.
+
+
+
+Anthropic's Own Cache Dashboard (API users)
+
+If you can't see the cache hit rate, you can't fix it.
+
+# Closing
+
+Anthropic fixed their half. Your harness is the other.
+
+Three weeks ago I was on the wrong side of this. Downgrading to $100/mo now, same workflow.
\ No newline at end of file
diff --git a/Claude/Claude Cowork 10 Insanely Powerful Prompts That Do the Work for You.md b/Claude/Claude Cowork 10 Insanely Powerful Prompts That Do the Work for You.md
new file mode 100644
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--- /dev/null
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@@ -0,0 +1,229 @@
+---
+title: "Claude Cowork: 10 Insanely Powerful Prompts That Do the Work for You"
+source: "https://x.com/coreyganim/status/2032427184040693948"
+author:
+ - "[[Unknown]]"
+published: 2026-03-14
+created: 2026-03-14
+description: "ai research engineer • designing agent runtimes, memory & retrieval systems for autonomous agents"
+tags:
+ - "clippings"
+---
+Claude Cowork is the most underrated tool on the market right now.
+
+While everyone's debating which AI is "best," a small group of operators are using Cowork to automate entire workflows that used to take hours.
+
+What separates the true operators from everyone else is they simply know exactly what to ask for.
+
+Here are 10 prompts that do the work for you. Copy them. Paste them. Watch your output multiply.
+
+**PS: Want to go deeper than copy-paste prompts? Inside our Build With AI community, we'll teach you how to build custom agents and automations that run your business 24/7.**
+
+**Join the waitlist (launching soon!):** [https://return-my-time.kit.com/1bd2720397](https://return-my-time.kit.com/1bd2720397)
+
+\------------------------------------------
+
+## 1\. The Morning Dashboard Builder
+
+**Prompt:**
+
+```markdown
+Open Chrome and create a morning dashboard for me. Open these tabs in order:
+1. Gmail inbox
+2. Google Calendar (week view)
+3. Notion (my workspace)
+4. Twitter/X notifications
+Arrange them so I can see Calendar and Gmail side by side. Then summarize what's on my calendar for today.
+```
+
+**Why it works:** Instead of manually opening 6 tabs every morning, Cowork sets up your entire workspace in seconds. The summary at the end means you start with context, not confusion.
+
+## 2\. The Research Compiler
+
+**Prompt:**
+
+```markdown
+I need to research [TOPIC].
+Open 5 browser tabs with the top articles on this topic from the last 30 days. Read each one and create a Google Doc summarizing:
+- Key points from each source
+- Common themes across all articles
+- Contrarian takes or unique angles
+- Links to all sources
+Save it as "[TOPIC] Research - [Today's Date]"
+```
+
+**Why it works:** This turns 2 hours of research into 10 minutes of review. Cowork does the hunting. You do the thinking.
+
+## 3\. The Email Batch Processor
+
+**Prompt:**
+
+```markdown
+Open Gmail. Find all unread emails from the last 24 hours.
+For each email:
+1. Categorize it (Urgent / Needs Response / FYI / Archive)
+2. Draft a response for anything in "Needs Response"
+3. Archive anything that's FYI only
+Show me the drafts before sending. List the urgent items at the top.
+```
+
+**Why it works:** Email is a time black hole. This prompt turns inbox zero from a 45-minute task into a 5-minute review.
+
+## 4\. The Meeting Prep Assistant
+
+**Prompt:**
+
+```markdown
+I have a meeting with [PERSON NAME] from [COMPANY] in 30 minutes.
+Research them:
+1. Open their LinkedIn profile
+2. Open their company website
+3. Find any recent news or posts they've shared
+4. Check if we have any email history in Gmail
+Create a one-page brief with:
+- Their role and background
+- Company overview
+- 3 talking points based on their recent activity
+- Any previous context from our emails
+```
+
+**Why it works:** Walking into a meeting cold is amateur hour. This gives you unfair advantage in 5 minutes.
+
+## 5\. The Content Repurposer
+
+**Prompt:**
+
+```markdown
+Open [URL to my YouTube video/podcast/article].
+Watch/read the content and create:
+1. 5 Twitter/X posts (each highlighting a different insight)
+2. 1 LinkedIn post (more detailed, professional tone)
+3. 1 email newsletter draft
+4. A list of 10 short-form video clip ideas with timestamps
+Save everything to a Google Doc called "Content Repurpose - [Title]"
+```
+
+**Why it works:** One piece of content should become 10. Most people don't repurpose because it's tedious. Cowork makes it automatic.
+
+## 6\. The Competitor Spy
+
+**Prompt:**
+
+```markdown
+Research [COMPETITOR NAME].
+Open their:
+1. Website (homepage + pricing page)
+2. Twitter/X profile (last 20 posts)
+3. LinkedIn company page
+4. Any recent press or Product Hunt launches
+Create a competitive analysis doc with:
+- Their positioning and key messaging
+- Pricing structure
+- What they're talking about on social
+- Gaps or weaknesses I can exploit
+- 3 ways we're different/better
+```
+
+**Why it works:** Know thy enemy. This gives you a competitive brief in 10 minutes that would take an analyst a full day.
+
+## 7\. The Weekly Review Automator
+
+**Prompt:**
+
+```markdown
+Help me do my weekly review.
+1. Open Google Calendar and list everything I did this week
+2. Open my task manager and show completed vs incomplete tasks
+3. Check my email sent folder for key conversations
+4. Open my notes app for any meeting notes from this week
+Create a weekly review doc with:
+- Top 3 wins this week
+- What didn't get done (and why)
+- Key decisions made
+- Focus areas for next week
+```
+
+**Why it works:** Weekly reviews are powerful but most people skip them because they're manual. Cowork does the data gathering. You just reflect.
+
+## 8\. The Invoice/Expense Processor
+
+**Prompt:**
+
+```markdown
+I need to process my business expenses.
+1. Open my email and find all receipts from the last 30 days
+2. Open my accounting software [Quickbooks/Xero/etc]
+3. For each receipt:
+- Extract the vendor, amount, and date
+- Categorize it (Software, Travel, Meals, etc.)
+- Log it in the accounting software
+Create a summary of total spend by category.
+```
+
+**Why it works:** Expense tracking is death by a thousand paper cuts. This batches it into one session that Cowork handles.
+
+## 9\. The Social Media Scheduler
+
+**Prompt:**
+
+```markdown
+I want to schedule my social media for the week.
+Open my content calendar [Notion/Sheets/etc] and review what's planned.
+For each piece of content:
+1. Open the scheduling tool [Buffer/Hypefury/etc]
+2. Create the post with the content from my calendar
+3. Set the scheduled time based on the calendar
+4. Add any images or links specified
+Confirm each post is scheduled and show me a summary of what's going live this week.
+```
+
+**Why it works:** Scheduling posts is repetitive clicking. Cowork handles the tedium while you focus on creating.
+
+## 10\. The End-of-Day Shutdown
+
+**Prompt:**
+
+```markdown
+Help me shut down for the day.
+1. Check my task manager for anything urgent I missed
+2. Look at tomorrow's calendar and flag any prep I need
+3. Close all browser tabs except [essential ones]
+4. Create a sticky note on my desktop with my top 3 priorities for tomorrow
+5. Open Spotify and play my wind-down playlist
+Tell me if there's anything critical I need to handle before logging off.
+```
+
+**Why it works:** A clean shutdown means you start tomorrow fresh. This prompt creates a ritual that actually happens.
+
+## The Meta-Prompt (Use This First)
+
+Before running any of these, prime Cowork with context:
+
+```markdown
+You are my executive assistant. You have access to my computer and can control apps, browser, and files.
+When I give you tasks:
+- Ask clarifying questions if needed
+- Narrate what you're doing so I can follow along
+- Pause before any destructive actions (deleting, sending, etc.)
+- Save important outputs to Google Docs for my review
+My key apps are: [list your main tools]
+My browser is: [Chrome/Arc/Safari]
+```
+
+This context makes every other prompt work better.
+
+# The Productivity Shift
+
+What I love about Cowork is that it isn't just a chatbot. It's a digital employee that can actually do things on your computer.
+
+Most people use AI to write. Smart operators use AI to execute.
+
+Start with one of these prompts. Run it tomorrow morning. See what happens.
+
+Then build your own.
+
+**And if you want to go deeper than copy-paste prompts you'll love our Build With AI community.**
+
+**Inside we're teaching you how to build custom agents and automations that run your business 24/7.**
+
+**Join the waitlist (launching soon!):** [https://return-my-time.kit.com/1bd2720397](https://return-my-time.kit.com/1bd2720397)
\ No newline at end of file
diff --git a/Claude/Claude Cowork The Definitive Setup Guide.md b/Claude/Claude Cowork The Definitive Setup Guide.md
new file mode 100644
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--- /dev/null
+++ b/Claude/Claude Cowork The Definitive Setup Guide.md
@@ -0,0 +1,293 @@
+---
+title: "Claude Cowork: The Definitive Setup Guide"
+source: "https://x.com/witcheer/status/2027759832523051263"
+author:
+ - "[[Unknown]]"
+published: 2026-02-28
+created: 2026-03-01
+description: "|| Cybersecurity and Cloud Instructor || I make complex concepts simple || CyberGirlie || WiCys West Africa ||AWS Solutions Architect-Professional"
+tags:
+ - "clippings"
+---
+## What Cowork Actually Is
+
+Cowork is an agentic desktop tool built into the Claude Desktop app. It gives Claude direct read/write access to folders on your computer, the ability to execute multi-step tasks autonomously, and the capacity to coordinate parallel sub-agents, all without a terminal or command line.
+
+It launched **January 12, 2026**. Availability expanded to **Pro subscribers on January 16**, **Team and Enterprise on January 23**, and **Windows (x64) on February 10**. On **February 24**, Anthropic shipped a major enterprise update with new plugins, connectors, and admin controls.
+
+**Key distinction from regular Claude chat:** Chat is prompt-response. Cowork is task delegation. You describe an outcome, Claude makes a plan, breaks it into subtasks, executes in a sandboxed VM on your machine, and delivers finished files to your folder. You can step away and come back to completed work.
+
+Cowork is built on the same agentic architecture as Claude Code (Anthropic's terminal-based coding tool). Boris Cherny reportedly built Cowork in ~10 days using Claude Code itself.
+
+> **Still a research preview.** Anthropic is explicit: agent safety is under active development. Treat it accordingly.
+
+## Requirements
+
+**Platform** macOS (universal) or Windows (x64 only). No arm64 Windows. No mobile. No web. **Subscription** Any paid plan: Pro ($20/mo), Max 5x ($100/mo), Max 20x ($200/mo), Team ($25+/seat/mo), Enterprise (custom) **Desktop App** Latest version from [claude.com/download](https://claude.com/download) **Internet** Active connection required throughout the session **App must stay open** Closing the desktop app kills the session. Sleep is fine, quitting is not.
+
+## Step 1: Install and Access Cowork
+
+1. Download the Claude Desktop app from [claude.com/download](https://claude.com/download).
+2. Sign in with your paid account.
+3. Open the app. Find the mode selector at the top, click the **"Cowork"** tab to switch from Chat to Tasks mode.
+4. You're in Cowork.
+
+## Step 2: Set Up Your Working Folder
+
+Cowork operates on a folder-permission model. You grant Claude access to a specific directory, and it can read, edit, and create files within it.
+
+The dedicated workspace approach
+
+Create a dedicated folder for Cowork rather than pointing it at your entire home directory or Documents folder. This is a safety best practice confirmed by Anthropic's own safety documentation.
+
+~/Claude-Workspace/ ├── context/ # Your standing context files ├── projects/ # Active project folders │ ├── client-a/ │ └── client-b/ └── outputs/ # Where Claude delivers finished work
+
+**Why this matters:** Cowork runs in a VM but has real read/write/delete access to any folder you share. Anthropic's safety guide explicitly warns: "Claude can take potentially destructive actions (such as deleting local files) if it's instructed to." A dedicated workspace limits blast radius.
+
+**Backup first.** Before your first real task, back up anything in the folders you plan to share. cp -R ~/Claude-Workspace/ ~/Claude-Workspace-Backup/ is cheap insurance.
+
+Cloud sync for cross-device access
+
+Cowork is desktop-only with no built-in sync. If you work across machines, put your workspace in a cloud-synced folder (iCloud Drive, Dropbox, OneDrive) so at least your files are consistent. This won't sync Cowork sessions, just the files.
+
+## Step 3: Write Your Context Files
+
+This is the highest-leverage setup step. The quality of Cowork's output is directly proportional to the quality of context you provide in files.
+
+Create these in your context/ folder as .md (Markdown) files. Markdown is the most token-efficient format for Claude to read.
+
+[about-me.md](https://about-me.md/)
+
+```bash
+# About Me
+
+## Role & Responsibilities
+- [Your name, title, company]
+- [What you do day-to-day]
+- [Key stakeholders you work with]
+- [What success looks like in your role]
+
+## Domain Context
+- [Industry/sector specifics]
+- [Key terminology or frameworks you use]
+- [Tools and platforms in your workflow]
+
+## Example Work
+[Paste 1-2 examples of output you're proud of — reports, emails, analyses.
+This gives Claude a concrete reference for quality and style.]
+```
+
+[brand-voice.md](https://brand-voice.md/)
+
+```bash
+# Communication Style
+
+## Tone
+- [e.g., Direct and concise. No filler. Technical when warranted.]
+- [Phrases you use naturally]
+- [Phrases that sound wrong to you]
+
+## Writing Samples
+[Paste 2-3 short examples of your actual writing — emails, posts, docs.
+These are more useful than abstract descriptions of your style.]
+
+## Anti-patterns
+- [e.g., Never use "leverage" as a verb]
+- [e.g., Don't open with "I hope this email finds you well"]
+```
+
+[working-preferences.md](https://working-preferences.md/)
+
+```bash
+# How I Want Claude to Work
+
+## Process
+- Always ask clarifying questions before starting non-trivial tasks
+- Show me your plan before executing
+- Save outputs as [.docx / .xlsx / .md — your preference]
+
+## Output Style
+- [Short vs. detailed outputs]
+- [Preferred formatting conventions]
+- [File naming conventions]
+
+## Guardrails
+- Never delete files without explicit confirmation
+- Never modify files outside the designated output folder
+- Flag assumptions explicitly before acting on them
+```
+
+**The compounding effect:** These files get better over time. After every session where Claude's output missed the mark, update the relevant context file. You can also ask Claude Desktop to help you write these three files based on all previous discussions you got together.
+
+## Step 4: Set Global and Folder Instructions
+
+Instructions are standing directives that load automatically at the start of every Cowork session.
+
+Global Instructions:
+
+1. Go to **Settings > Cowork** in Claude Desktop.
+2. Click **"Edit"** next to Global Instructions.
+3. Write your core preferences and save.
+
+**What to put here** (distilled from your context files):
+
+```bash
+I'm [Name], [Role] at [Company]. I work on [domain].
+
+Communication: Direct, concise, no filler. Default to .md for drafts, .docx for final deliverables.
+
+Process: Always ask clarifying questions before starting complex tasks. Show your plan. Explain assumptions.
+
+Safety: Never delete files without my explicit approval. Flag any destructive actions before executing.
+```
+
+Keep this concise. Global instructions load every session, consuming context window.
+
+Folder Instructions:
+
+Folder-specific instructions activate when you select that folder in Cowork. Use these for project or client-specific context.
+
+**Example:** Your projects/client-a/ folder might have instructions like:
+
+```bash
+Client A is a Series B fintech company. Brand voice: professional but approachable.
+Key contacts: [names]. Current priorities: [list].
+Use their terminology: "members" not "users", "platform" not "app".
+```
+
+Claude can also update folder instructions during a session based on what it learns.
+
+## Step 5: Install Plugins
+
+Plugins bundle skills, slash commands, connectors, and sub-agents into role-specific packages.
+
+How to install:
+
+1. In Cowork, click the **"Customise"** menu in the left sidebar.
+2. Click **"Browse plugins"** to see available options.
+3. Click **Install** on your chosen plugin.
+4. Or browse at [claude.com/plugins](https://claude.com/plugins).
+
+What's available (as of February 27, 2026)
+
+Anthropic open-sourced 11 plugins on January 30 and shipped additional plugins on February 24. The open-source repo is at [github.com/anthropics/knowledge-work-plugins](https://github.com/anthropics/knowledge-work-plugins).
+
+**Core plugins (shipped January 30):** Productivity, Marketing, Sales, Finance, Data Analysis, Legal, Product Management, Customer Support, Enterprise Search, Biology Research, Plugin Management (for building custom plugins).
+
+**February 24 additions:** HR, Engineering, Design, Operations, Financial Analysis, Investment Banking, Equity Research, Private Equity, Wealth Management. Plus a partner-built Brand Voice plugin by Tribe AI.
+
+Each plugin is a folder containing:
+
+- .claude-plugin/plugin.json - Manifest
+- .mcp.json - Tool connections
+- commands/ - Slash commands you trigger explicitly (e.g., /sales:call-prep)
+- skills/ - Domain knowledge Claude draws on automatically
+
+**Plugins are just markdown files.** You can customise any plugin or build your own with no code.
+
+Which to install first:
+
+Start with **Productivity** (useful regardless of role) and **one role-specific plugin** that matches your job function. Type / in any Cowork chat to see available slash commands from installed plugins.
+
+Customising plugins:
+
+After installing, click **"Customise"** on the plugin to work with Claude to adjust skills, commands, and connectors to match your workflow. The default plugins are starting points, the real value comes from adding your company's specific context, terminology, and processes.
+
+## Step 6: Connect Your Tools
+
+Connectors link Claude to external services via MCP (Model Context Protocol). Once connected, Claude can pull live data from these tools during Cowork sessions.
+
+How to connect:
+
+1. Go to **Settings > Connectors** in Claude Desktop (or visit [claude.ai/settings/connectors](https://claude.ai/settings/connectors)).
+2. Browse available integrations.
+3. Click a connector and authenticate.
+
+Available connectors (as of February 24, 2026):
+
+**Verified from Anthropic's announcements:** Google Drive, Gmail, Google Calendar, Slack, Notion, Asana, Linear, Jira, Monday, ClickUp, Figma, Amplitude, Pendo, Intercom, HubSpot, Close, Clay, ZoomInfo, Fireflies, Microsoft 365, Snowflake, Databricks, BigQuery, Hex, Box, Egnyte, DocuSign, FactSet, MSCI, Harvey, Apollo, Outreach, SimilarWeb, LegalZoom, WordPress, Canva, Ahrefs, Klaviyo, Guru, Benchling, and others.
+
+First connector to set up:
+
+Connect whichever tool you use most. Slack, Google Drive, or Notion are the highest-leverage starting points for most knowledge workers.
+
+## Step 7: Run Your First Task
+
+Verification prompt:
+
+Start a new Cowork session with your workspace folder selected. Type:
+
+```bash
+Read all the files in the context folder. Then tell me:
+1. What you know about me
+2. How I prefer to work
+3. What standing preferences you're aware of
+
+Do not start any other work yet.
+```
+
+If the output accurately reflects your context files and global instructions, your setup is working. If something's off, fix it now.
+
+First real task:
+
+Pick something you already know how to do well, you need to be able to evaluate whether the output is correct.
+
+```bash
+I want to [DO X] so that [Y IS BETTER].
+
+First, read all relevant files in this folder.
+Before you start executing, ask me clarifying questions so we can align on approach.
+Only begin work once we've agreed on the plan.
+```
+
+Watch what happens. Answer the questions. Iterate. The best early tasks are things like:
+
+- Organising a messy folder by type and date
+- Creating a formatted report from scattered notes
+- Building a spreadsheet from receipt images
+- Drafting a document based on existing templates in your folder
+
+Scheduled tasks:
+
+Cowork now supports recurring tasks. Type /schedule in any Cowork task, or click "Scheduled" in the left sidebar. Tasks only run while your computer is awake and the app is open.
+
+## Safety: What You Need to Know
+
+This section matters more than any productivity tip. Cowork has real power over your files. Anthropic's safety documentation is clear about the risks.
+
+Verified safety features:
+
+- **Deletion protection:** Claude requires explicit permission before permanently deleting files. You'll see a prompt and must click "Allow."
+- **VM isolation:** Cowork runs in a sandboxed virtual machine (Apple's Virtualization Framework on macOS). Code executes in an isolated space, but Claude can make real changes to files you've shared.
+- **Model training against prompt injection:** Anthropic uses reinforcement learning to train Claude to refuse malicious instructions and content classifiers to flag potential injections.
+
+Real risks to manage:
+
+1. **Prompt injection.** If Claude reads a malicious document or website, hidden instructions could alter its behavior. Limit Claude in Chrome access to trusted sites. Web content is the primary injection vector.
+2. **File destruction.** Claude can modify or delete files in any folder you share. Use a dedicated workspace. Keep backups. Include "never delete files without my explicit confirmation" in your global instructions.
+3. **Scope creep.** Watch for unexpected patterns: Claude accessing files you didn't mention, or task scope expanding beyond what you asked for. If something feels off, stop the task immediately.
+4. **No audit logging.** Cowork activity is not captured in audit logs, Compliance API, or data exports. **Do not use Cowork for regulated workloads.**
+5. **Session persistence.** The app must stay open. Closing it kills the task mid-execution with no recovery.
+
+Safety defaults to set:
+
+Add these to your global instructions:
+
+```bash
+- Never delete any files without my explicit confirmation
+- Never modify files outside the designated output folder
+- Show me your plan before executing any multi-step task
+- If you're unsure about any instruction, ask rather than assume
+```
+
+## Cross-App Workflows
+
+Cowork can now pass context between Excel and PowerPoint add-ins. Claude can analyse data in Excel and move a chart directly into a presentation without you switching apps.
+
+**Requirements:** Mac users on Max, Team, or Enterprise plans. Both the Claude in Excel and Claude in PowerPoint add-ins must be installed. Windows support isn't available yet for this feature.
+
+**Caution:** Be aware that data from one application may flow into another during a Cowork session. Avoid working with sensitive information in these add-ins while Cowork is active.
+
+This guide was compiled from Anthropic's official Help Center documentation, the Cowork blog post, the knowledge-work-plugins GitHub repository, CNBC reporting, VentureBeat coverage, and multiple independent reviews like [@heynavtoor](https://x.com/@heynavtoor)'s article, then verified against the source material for accuracy.
+
+Thanks for reading!
\ No newline at end of file
diff --git a/Claude/Claude Design Is the First Real Threat to Figma — And It Didn’t Come From a Design Company.md b/Claude/Claude Design Is the First Real Threat to Figma — And It Didn’t Come From a Design Company.md
new file mode 100644
index 0000000..192b162
--- /dev/null
+++ b/Claude/Claude Design Is the First Real Threat to Figma — And It Didn’t Come From a Design Company.md
@@ -0,0 +1,24 @@
+---
+title: "Claude Design Is the First Real Threat to Figma — And It Didn’t Come From a Design Company"
+source: "https://x.com/Suryanshti777/status/2047329566889533884"
+author:
+ - "[[@Suryanshti777]]"
+published: 2026-04-23
+created: 2026-04-24
+description: "For years, digital design has followed a predictable pattern.You open a tool.You start from a blank canvas.And you build everything — ma..."
+tags:
+ - "clippings"
+---
+
+
+For years, digital design has followed a predictable pattern. You open a tool. You start from a blank canvas. And you build everything — manually, layer by layer. That process became so standard that it stopped being questioned. Until now.
+
+
+
+60 million views in 2 days. Figma lost [$1B](https://x.com/search?q=%241B&src=cashtag_click)\+ valutation right after the news.
+
+What Anthropic has introduced with Claude Design doesn’t try to improve that workflow. It quietly replaces the starting point. (Place the Claude Design sky-blue official thumbnail here — this anchors the product visually early in the article) Instead of beginning with a canvas, you begin with intent. You describe what you want — a product interface, a landing page, a presentation — and the system constructs a structured design around it. Not a rough sketch. A usable, coherent starting point. Before going deeper, it’s worth seeing how this is positioned officially. [https://youtu.be/t\_LBECIQQqs?si=citDcPSjig\_h\_NsL](https://youtu.be/t_LBECIQQqs?si=citDcPSjig_h_NsL) The Shift: From Tools to Systems Claude Design doesn’t behave like traditional software. It behaves like a system that understands design logic. You’re no longer assembling layouts manually or thinking in terms of frames and grids. You’re shaping outcomes. Refining sections. Improving hierarchy. Adjusting clarity. And the system applies those changes across the entire design in a consistent way. That removes a layer of mechanical work that has always defined design tools. Why This Puts Real Pressure on Figma For over a decade, Figma has been the foundation of product design workflows. Its strength is precision. Every element can be controlled. Every interaction can be defined. But that precision comes at a cost — time, effort, and complexity. Claude Design moves in the opposite direction. It reduces setup. It increases automation. And it compresses iteration cycles. If this approach matures, the core question shifts from: “How do I design this?” to: “How clearly can I express what I want built?” That shift doesn’t just compete with Figma. It redefines when and why you would even open it. Iteration — Not Generation — Is the Real Breakthrough Most AI tools create impressive first outputs and fall apart during refinement. Claude Design is built for the second step. You can edit content directly inside the design, adjust spacing and layout, and fine-tune visual hierarchy. And instead of breaking the structure, the system improves it. This mirrors how experienced designers actually think — but compresses the time required to do it.
+
+
+
+This is where the product stops feeling experimental. And starts feeling usable. Design Systems Without the Overhead Consistency is one of the hardest problems in design. Maintaining colors, typography, and components across multiple assets requires constant effort. Claude Design approaches this differently. It can understand and apply design systems automatically — aligning outputs with existing brand structures. The result is not just fast design. It’s consistent design. Where This Becomes Unfair This is not a tool limited to designers. It redistributes capability across roles. A founder can turn a rough idea into a structured interface. A product manager can visualize flows without waiting for design cycles. A designer can explore multiple directions without the cost of setup. A marketer can generate campaign assets that already feel polished. Execution is no longer the bottleneck. Clarity is. Collaboration Is Built In — Not Added Later Traditional workflows split collaboration across tools. Design happens in one place. Feedback in another. Execution somewhere else. Claude Design collapses that separation. Editing, commenting, and iteration exist in a shared environment where the system is also participating. This integration with Canva is more important than it looks. It shows that Claude Design is not trying to replace the ecosystem immediately. It’s embedding itself into it — while shifting how work begins. Ideas can move from AI-generated drafts into fully editable, collaborative designs without friction. That bridge matters. High-Signal Prompts That Actually Work The quality of output depends heavily on how you frame your intent. Here are a few structured prompts that consistently produce strong results: SaaS Dashboard (High Fidelity UI) Design a modern analytics dashboard for a fintech platform. Use a dark theme with strong contrast and clear hierarchy. Include KPI cards, interactive charts, and a structured sidebar navigation. Focus on spacing, readability, and minimalism inspired by Stripe and Linear. Landing Page (Conversion-Oriented) Create a high-converting landing page for an AI productivity product. Include hero, social proof, feature breakdown, testimonials, and pricing. Use soft gradients, clean typography, and strong CTA placement. Ensure the layout is responsive and visually balanced. Pitch Deck (Investor-Ready) Generate a 10-slide pitch deck for an AI design platform. Keep the structure minimal, high-contrast, and narrative-driven. Prioritize clarity of problem, strength of solution, and visual simplicity. The Reality Claude Design is still early. It won’t replace Figma overnight. It won’t eliminate the need for designers. But it changes the starting point. And once the starting point changes, the rest of the workflow eventually follows. Final Thought Design is no longer defined by tools. It’s defined by how quickly you can move from idea to clarity. Claude Design reduces friction in that transition. And when friction disappears, advantage shifts to those who can think better — not just execute better.
\ No newline at end of file
diff --git a/Claude/Claude Design 和 Google DESIGN.md 到底是想取代设计师还是想取代码农.md b/Claude/Claude Design 和 Google DESIGN.md 到底是想取代设计师还是想取代码农.md
new file mode 100644
index 0000000..92dfca4
--- /dev/null
+++ b/Claude/Claude Design 和 Google DESIGN.md 到底是想取代设计师还是想取代码农.md
@@ -0,0 +1,95 @@
+---
+title: "Claude Design 和 Google DESIGN.md 到底是想取代设计师还是想取代码农"
+source: "https://yage.ai/share/google-stitch-design-md-20260423.html?utm_source=twitter&utm_medium=thread&utm_campaign=google-stitch-design-md-20260423"
+author:
+ - "[[鸭哥]]"
+published: 2026-04-23
+created: 2026-04-24
+description: "在小公司和简单项目上，设计师和码农的岗位正在事实上合并。看这一波新发布的 AI 设计工具就能看到一个方向：合并后更省事的是懂一点设计的码农，不是懂一点代码的设计师。Figma 在试着给出另一种答案，但只走了前半程。"
+tags:
+ - "clippings"
+---
+## 一个正在发生的趋势：设计师和码农在小项目上正被合并
+
+这一波 AI 设计工具出来之后，设计师圈子里最常见的焦虑是”我的岗位会不会被取代”。码农圈子里的焦虑是”我现在是不是还得学点设计”。两个岗位各自从自己的角度担心，但一个更简单的观察是：在小公司和简单项目上，这两个岗位已经在事实上合并。一个独立开发者、一个三五人的创业团队、一个需要给 pitch deck 做 mockup 的 PM，现在不用雇设计师也不用雇前端就能把东西做出来。这个现象在企业级产品和高端品牌项目里还不明显，那里对视觉品质、品牌一致性、复杂交互的要求今天的 AI 工具做不到。但从独立项目一路到中型产品这个区间，合并已经是在发生的事。
+
+这篇文章不做”未来会怎样”的预测，只看现在这个趋势在走哪个方向。在岗位合并的地方，一个懂一点另一方的人就够了。那这个人，是懂一点代码的设计师，还是懂一点设计的码农？换句话说，合并之后谁活少钱多？
+
+这个问题在 2026 年这几个月有了一个挺清晰的答案，因为主要玩家都发了新产品，方向出奇地一致。Anthropic 4 月 17 日发布 Claude Design，Google 3 月更新 Stitch 2.0、4 月 21 日又把 DESIGN.md 格式开源成跨工具规范，外加 Lovable、Bolt、v0、Cursor 这一批，全部都是从码农的工作环境出发的。这些工具的入口是输入框，产出是代码，反馈循环是”跑一下看看对不对”。一个从来没写过 CSS 的设计师坐下来用，第一反应会是这东西跟自己平时的工作方式对不上。本文要论证的就是这件事：当前整个 AI 设计赛道的默认答案是 **让懂一点设计的码农来取代只懂设计的设计师** 。具体会拆开 Claude Design 和 DESIGN.md 的形态，解释它们看起来像是把设计权交还给设计师、其实仍然是码农工具；然后讲 Figma 正在给出的那条相反方向的答案，以及它到今天只走了哪一半。这个答案不一定是最优的，但对今天主流的产品团队来讲，前一个是默认选择，原因比”谁更聪明”要根本。
+
+## 新发布的几个大产品，全是给码农设计的
+
+先看 Claude Design。Anthropic 4 月 17 日发布，当天 Figma 股价跌约 7%。界面是 claude.ai 左栏的调色板图标，打开是一个对话框加一块实时 HTML 预览。你输入一段自然语言描述你要什么，Claude 生成第一版，然后你通过继续对话、行内评论、拖滑块来调整。输出可以导成 HTML、PDF、PPTX、Canva，也可以直接丢给 Claude Code 接着写代码。
+
+Anthropic 官方给出的五类用户画像：资深设计师、产品经理、创始人、市场人员、销售。后四类的共同点是”想要视觉产出但不想打开 Figma”。The Register 的报道标题是《Anthropic 推出 Claude Design，因为谁还需要设计师呢？》（ [theregister.com](https://www.theregister.com/2026/04/17/anthropic_debuts_claude_design/) ）。法语评论站 agence-scroll 的原话：“真正的目标用户是非设计师，是那些从来没打开过 Figma 的人。”（ [agence-scroll.com](https://agence-scroll.com/en/blog/claude-design-anthropic-2026-guide) ）产品定位说得很直白。
+
+再看 Google 最近两个动作。3 月的 Stitch 2.0 更新加了 infinite canvas、语音输入、multi-screen 生成；更值得关注的是 DESIGN.md 的开源，4 月 21 日 Google 把它发成跨工具规范（ [github.com/google-labs-code/design.md](https://github.com/google-labs-code/design.md) ）。Stitch 本身各方评测一致定位为灵感工具，独立设计师 Dianne Alter 在 Qualcomm 开源设计系统上实测后的总结是：“Stitch 生成的截图只能当真实设计文件的参考，不能当设计文件本身。”（ [designproject.io](https://designproject.io/blog/google-stitch-review/) ）
+
+DESIGN.md 并不是 Google 发明的。“用一份 Markdown 文件描述设计系统给 AI 读”这个想法最早出现在 vibe coding 社区（Banani 的 [design.md guide](https://www.banani.co/blog/design-md-guide) 写得很清楚：“vibe coding community started experimenting with moving stylistic preferences into a separate md instruction file”），很多独立开发者在用 Claude Code、Cursor 这类工具生成 UI 时发现生成的东西风格漂移，于是自发开始往项目里放一份描述品牌视觉语言的 md 文件当 context。Google 在 2026 年 3 月把这件事内化成 Stitch 的一个产品特性（见 [designmd.app](https://designmd.app/en/what-is-design-md) ：“Standard introduced by Google Stitch in March 2026”），3 月底 [VoltAgent 的 awesome-design-md 仓库](https://github.com/VoltAgent/awesome-design-md) 启动，短短几周从 Stripe、Vercel、Linear、Notion、Apple、Ferrari、Tesla 等 55 多个网站的公开 CSS 逆向出品牌样本。4 月 21 日 Google 做的动作是把 DESIGN.md 以 Apache 2.0 开源成一份正式规范（ [github.com/google-labs-code/design.md](https://github.com/google-labs-code/design.md) ），配了一个命令行工具和一份 token 命名约定。这个动作的实质是 **抢占事实标准的话语权** ：格式本身业界已经在自发用，但没有统一规范；Google 这一步把”以后大家应该怎么写 DESIGN.md”的定义权拿到了自己手里。
+
+DESIGN.md 是一份放在项目根目录的 Markdown 文件，任何 AI agent 打开项目就能读到它。文件里有两层内容并置：前半段是 YAML 写的设计 tokens，定义颜色、字号、间距、圆角、阴影等具体数值；后半段是自然语言写的 prose，解释每一个重要决策的意图。一个最小示例长这样：
+
+```
+colors:
+ primary: "#B8422E"
+ background: "#F7F1E8"
+typography:
+ heading: "Söhne Mono"
+
+---
+
+这个暖米色做背景是为了把阅读体验往 editorial 的方向拉，
+适合长文阅读页面，不适合交易转化页。主色的砖红色在长文语境
+里是克制的信号，跟 primary action 按钮组合时不要再叠加饱和的
+辅色。Söhne Mono 只用在标题层级，正文保持无衬线。
+```
+
+这个形式在业界以前没有过。 [Design Tokens Community Group](https://github.com/design-tokens/community-group) 的 tokens.json 只有结构化数值没有意图；Tailwind 的 config 只有 class 映射也没有 why；Figma 的 Variables 能做语义分层但锁在 Figma 文件格式里，别的 agent 读不到。DESIGN.md 第一次把 **设计的 what 和 why 放在同一份纯文本里** ，任何工具都能 git diff 和 version control。
+
+开源的同时 Google 发了一个 `@google/design.md` 的命令行工具（规范正文见 [spec.md](https://github.com/google-labs-code/design.md/blob/main/docs/spec.md) ），目前有两个子命令。 `lint` 会检查 token 命名是否合规、引用有没有断（比如 `{colors.primary}` 指向一个不存在的 key）、颜色组合是否满足 WCAG 的 4.5:1 对比度要求，输出结构化 JSON 给 agent 消费。 `diff` 能对比两个版本的 DESIGN.md，分别标出 token 级变动和 prose 级变动，返回一个 `"regression": true/false` 字段可以直接挂进 CI。这套 CLI 的意思是 **设计规则从此可以像代码一样 gate** ：改一个颜色，CI 自动跑 lint 检查你有没有破坏无障碍合规；改一段 prose，diff 能标出哪些判断原则被改了。
+
+开源两周内社区的反响很快。 [VoltAgent 的 awesome-design-md 集合](https://github.com/VoltAgent/awesome-design-md) 从 55 多个网站的公开 CSS 逆向出 DESIGN.md 样本，任何人都能把一份 DESIGN.md 丢进项目根目录，让 Claude Code 或 Cursor 按这个品牌的视觉语言生成 UI。 [Anthropic 的 skills 项目开了一个 issue](https://github.com/anthropics/skills/issues/1008) 讨论把 DESIGN.md 当一等公民跟自己的 SKILL.md 并列。 [designmd.app](https://designmd.app/en/what-is-design-md) 和 [design-extractor.com](https://www.design-extractor.com/docs/design-md) 这样的独立站点把 DESIGN.md 描述成”设计领域的 AGENTS.md”。这是它被关注的真正原因，不是因为它”开源了一个新格式”，是因为它第一次让设计规则跟代码规则拥有同一种工程基础设施：纯文本、可 git、可 diff、可 CI。
+
+DESIGN.md 给谁用，看它的形式就清楚了。YAML 语法、 `{colors.primary}` 引用、命令行 lint、把 DESIGN.md 放进项目根目录跟 AGENTS.md、CLAUDE.md 并列的心智模型，整套都是码农的工作语言。主讲人 David East 是 Google Labs 的 DevRel，Firebase 背景出身。社区那个 55 多个品牌的集合里，DESIGN.md 基本是从网站 CSS 逆向出来的，token 部分准（因为 CSS 本来就是结构化的），prose 部分从品牌的营销页面抄下来就是”值得信赖的金融感”这种话术，不是品牌设计师真正会写在内部文档里的话。DESIGN.md 做的事情是 **让码农用一种更结构化的方式跟 AI 描述设计系统** ，设计师本人既不是主要写作者，也不是主要消费者。
+
+## 两种工作节奏的具体差别
+
+**输入端：是用文字描述还是用眼睛判断** 。码农本来就用文字描述意图工作，写代码就是这么做的。把意图描述从代码换成自然语言 prompt，对码农来说是小改动，不是工作方式的颠覆。设计师的日常是用眼睛做判断：这个按钮放这儿对不对、这段文字离图片多远看着舒服、这个色调跟品牌定位是否一致。Claude Design 和 Stitch 让你输入”把顶部区域再现代一点”，生成结果给你看，再让你在对话里反馈”字再大点”。对设计师，输入端丢掉了她一半的专业能力。独立设计师 Jae Lee 试完 Stitch 之后的原话是：“可选的东西很少，就那么几个颜色、一个取色器、几种 Google Fonts。”（ [designerup.co](https://designerup.co/blog/google-stitch-first-look/) ）她期待的是一个能真正编辑的画布，得到的是一个套了壳的对话框。
+
+**编辑端：是整屏重新生成还是元素级微调** 。设计师在 Figma 里的日常操作是”这个 padding 从 16 调到 20、那个行高从 1.4 调到 1.15、这个阴影再柔和一点”，做完立刻看到结果。在 Claude Design 或 Stitch 里你说”把那个按钮往左移 8 像素”，AI 每次解读都不一样；你说”字再细一点”，它可能把整段文字换成另一个字重。Mejba Ahmed 试过一轮后写：“当你需要把一个东西精确挪 4 个像素，或者让一条基线对齐某个具体网格，Stitch 的自然语言界面就变成了障碍。”（ [mejba.me](https://www.mejba.me/blog/google-stitch-ai-design-platform) ）码农本来就不做像素级微调，写完代码看看差不多就行；设计师的工作恰恰在微调上，这种粒度丢失等于把她的核心能力废掉。
+
+**交付端：一次性生成还是持续迭代** 。Google Stitch 的 Experimental 模式下你做完一整个项目才发现导不出到 Figma（Google 员工 Rishabh Chauhan 在 [开发者论坛](https://discuss.ai.google.dev/t/exporting-stitch-to-figma/104903) 亲自确认过）；用 Gemini 3 Pro 模式同样没有导出选项；免费额度用完后会触发一套用户看不到阈值的限流。这些在码农眼里是”工具还不够成熟，等等再用”；在设计师眼里是”花了一下午东西拿不回自己的主工作流”。设计师的工作不是一次性生成一张图就完事，是在一个稳定画布上反复迭代，这一点 Figma 做了十年才打磨到今天的样子。
+
+## 反馈循环的有无决定了谁能先被 agent 接管
+
+三件事放在一起，指向同一个更根本的差别： **码农的工作有现成的反馈循环，设计师的没有** 。这是所有 AI 设计工具为什么优先为码农服务的最深层原因。
+
+码农的工作在过去十年已经被训练得高度文本化。写代码本来就是在用一种混合语言描述意图。更重要的是码农手上有一套完整的反馈循环：生成的代码可以跑、可以测、可以 CI、出 bug 可以打日志看栈。Agent 把他的 prompt 翻译错了，他立刻就知道。这个反馈循环让”码农说自然语言、agent 写代码”这个模式在工程上成立。你不需要码农信任 agent，你只需要码农能验证 agent 的输出，验证不过就重来。
+
+设计师的工作不是这样。她的很多决策是视觉判断、手感、品味，这些东西能不能被自然语言完整表达，行业里从来没有证明过。更麻烦的是反馈循环断了。设计师能看出”这个按钮在 mobile 上触达区域不够大”（视觉判断）、能看出”这个标题跟品牌的 editorial tone 不搭”（品味判断），但她看不出”这个 API 在 3G 网络下会超时”或”这个组件在 dark mode 下的 focus state 会跟已有 CSS 变量冲突”（工程判断）。如果 agent 生成的东西里夹杂了隐藏的工程决策，设计师在视觉层面没办法验证这些决策，她就是在签一份自己没看过的合同。
+
+换个说法：今天让码农做 principal、让 agent 做 executor，这个模式能跑通，因为反馈通道是现成的，代码能跑能测，agent 翻译错了码农立刻知道。反过来让设计师做 principal、agent 做 executor，这个模式还没跑通，因为反馈通道要现造：agent 必须把工程决策翻译成设计师能在视觉层面验证的东西。这是一个没有现成范式可以抄的产品设计难题。主流 AI 工具选择不碰这个难题，走阻力最小的码农那条路，这不是偷懒，是技术上理所当然的选择。代价是”懂一点设计的码农取代只懂设计的设计师”成为默认结局，不是因为码农更聪明，而是因为码农的工作模式跟当前 agent 技术天然匹配。
+
+## Figma 在做一件不一样的事
+
+到这里结论是所有主流 AI 工具都在让码农吃掉设计师，设计师只能等。但有一个玩家在做完全相反的方向，而且做得没有被主流讨论充分意识到，这个玩家是 Figma。
+
+Figma 过去一年做的 AI 相关动作（Make、MCP server、Code Connect、AI Skills、Slots）单独看像一堆碎片，但背后的决策原则是一致的： **它做的不是”能替代设计师的 AI”，是一套让 agent 必须先经过设计师的规则** 。这套规则有两条。
+
+**第一条，设计师的 Figma 文件是所有 agent 的真相源** 。这条原则的意思是，不管你是 Claude Code、Cursor 还是 GitHub Copilot，你想写前端代码，应该先来 Figma 读设计师的文件，而不是让码农重新用 DESIGN.md 或者截图给你描述一遍。Figma 把这条原则变成产品的动作是 MCP server：码农在 Cursor 里写代码时，Cursor 会在后台持续读 Figma 文件里的颜色变量、组件定义、布局约束，生成的代码自动跟设计师的源文件对齐。设计师改了按钮圆角，下次 Cursor 生成代码时自动跟着改。设计师在这个循环里不是”交完一份设计文件就没事了”，而是 **持续定义语义** ，agent 和码农都从她这里读。
+
+**第二条，agent 只能在设计师预设的约束里工作** 。这条的意思是，agent 在 Figma 画布上不能自由发挥，只能用设计师定义好的组件、变量、字体做装配。Figma 把这条变成产品的动作是 AI Skills：skill 是用 Markdown 写的流程指令，告诉 agent 该怎么在这个 Figma 文件里做事，比如”从代码库同步一个新组件过来”、“基于现有组件生成一个新 UI 屏幕”、“跨代码和 Figma 同步 tokens”。agent 不能离开 skill 规定的边界去发明新视觉。换句话说 Figma 给 agent 戴上了设计师预先设定的脚镣，agent 做的不是”无中生有”，是”在已有语义层上装配”。UX Collective 上 Christine Vallaure 对这件事的观察是：“agentic AI 的默认方向是往效率走，往那个已经存在的零件库里快速上色。设计师真正要做的事，是先保证那个库里有值得装配的东西。”（ [uxdesign.cc](https://uxdesign.cc/agentic-ai-design-systems-figma-a-practical-guide-6ab0b681718d) ）
+
+这两条原则加起来，跟 Claude Design 的方向完全相反。Claude Design 说”设计从输入框开始”；Figma 说”设计从设计师的画布开始，画布背后才接 agent”。前者是用 AI 绕开设计师，后者是让设计师成为 agent 生态的上游。Figma 今天是离”设计师主导的 agentic system”最近的玩家，原因不是技术更强，是出发点对。
+
+但 Figma 也没把事情做完。Figma Make 目前生成的代码只支持 React，设计师想做 Flutter、SwiftUI、Vue、原生 HTML 都不行。更本质的限制是 Figma 仍然是纯前端视角，做不了全栈。设计师如果在 Figma 里说”这里显示用户最近 5 条订单”，她还是需要一个码农定义 API、数据库 schema、加载逻辑。Figma 自己没办法把这个意图翻译成工程决策再给设计师一个视觉表达让她验证。前面讲的”设计师无法验证工程决策”这个问题，Figma 还没解决。所以更准确的说法是，Figma 走完了设计师主导的前半程（agent 在视觉约束下装配），没开始走后半程（agent 把工程决策翻译回视觉）。两段加起来才是完整的”设计师能取代码农”的产品。
+
+## 今天你能做什么
+
+**如果你是设计师** 。不要慌，也不要假装 Claude Design 和 Stitch 是你的工具。它们真正服务的是产品经理、创始人、市场人员，是把”我需要一个粗糙的 mockup”这种活从你的输入队列里拿走了。你的工作会从”帮非设计师把想法可视化”上移到”在他们的粗糙产出上做真正的设计判断”。同时花时间把 Figma 的 AI 路线摸一遍：MCP server、Make、Skills、Code Connect 这一套组合在 2026 年的意义是你第一次可以持续地、系统性地定义一个 agent 生态的语义来源。学这个比学 Claude Design 划算得多。
+
+**如果你是码农或产品** 。这些工具让你能做以前必须找设计师才能做的事，但 Claude Design 和 Stitch 的产出是灵感级的，不是可以直接发布到生产的级别。做内部原型、做给老板看的演示稿、早期验证都可以；做正式上线的产品会在无障碍合规、品牌一致性、边界情况上翻车。如果团队里已经有设计师，别急着学 DESIGN.md，先学 Figma MCP：让 Cursor 或 Claude Code 直接读设计师的 Figma 文件，比任何人工同步的 DESIGN.md 都准确。DESIGN.md 作为在没有设计师的项目里跟 AI 对齐视觉规则的格式是有用的，但不是 Figma 的替代。
+
+**如果你在想产品机会** 。“面向设计师的 agentic system”不是没人做的空白，Figma 正在做。真正的空白在 Figma 做不到的那几块：非 React 输出的设计-代码闭环、工程决策的视觉翻译、全栈意图标注。这几个方向任何一个做对了，都能服务今天没被好好服务的那群人：想主导全栈产品但不想变成码农的设计师。
+
+设计师和码农这两个岗位在小项目上已经在合并。合并后是懂一点码农的设计师活少钱多，还是懂一点设计师的码农活少钱多？今天主流 AI 工具给的答案是后者。答案之所以成立，不是因为码农更聪明，是因为 agent 技术今天的接口天然贴合码农的工作模式。但这个答案不是唯一答案，Figma 在给另一个答案，而且它走到的位置比主流讨论意识到的要远。哪个答案最终占上风，要看 Figma 能不能把后半程走完，也要看有没有新玩家在 Figma 做不到的那几块上做出东西。如果你两种工作都沾一点，挑一边深下去比两边都浅更重要。
\ No newline at end of file
diff --git a/Claude/Claude For Dummies.md b/Claude/Claude For Dummies.md
new file mode 100644
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--- /dev/null
+++ b/Claude/Claude For Dummies.md
@@ -0,0 +1,524 @@
+---
+title: "Claude For Dummies."
+source: "https://x.com/rubenhassid/status/2045713046065283435"
+author:
+ - "[[@rubenhassid]]"
+published: 2026-04-19
+created: 2026-04-26
+description: "98.75% of all humans have never tried Claude.Not even once.This free guide solves this gap. Claude For Dummies.Skip this if you know Claude ..."
+tags:
+ - "clippings"
+---
+
+
+**98.75% of all humans** have never tried Claude.
+
+Not even once.
+
+This free guide solves this gap. **Claude For Dummies**.
+
+> Skip this if you know Claude and have tried it. For the “pros”, I added a very last section **“How pros use Claude”,** at the end.
+
+You never tried Claude because you either:
+
+- Don’t really use AI.
+- Or you just use ChatGPT.
+- Or worst (!!!), you only use the FREE ChatGPT.
+
+**This guide is the easiest way to get started with Claude.**
+
+I will assume you don’t know how to use AI. And by the end of this (very long) guide, you will know more than 98.75% of the world.
+
+Sounds like a good deal? Cool.
+
+Two things before we start:
+
+1. Save this guide. Block 10 min this week & try Claude for the first time.
+2. Send it to anyone who has never tried Claude (& still thinks it’s a guy’s name).
+
+# 1\. What is Claude?
+
+Claude is an AI you talk to.
+
+You type something, it types back. It can write, think, summarize, analyze documents, work with your files, and help you make decisions.
+
+
+
+You type something you need. You select the model at the bottom right. They just announced the new Claude Opus 4.7 by the way.
+
+
+
+It created an entire Excel, with 12 (!!!) tabs, real formulas, everything. And you can preview the Excel right inside your Claude chat. So good.
+
+It’s made by a company called Anthropic.
+
+If you’ve used ChatGPT before, Claude is the same kind of thing. If you’ve never used any AI, Claude is a great place to start.
+
+# How it actually works (in 30 seconds)
+
+You don’t need to understand the engineering.
+
+But 3 concepts will save you from the most common beginner frustrations.
+
+## 1 - Auto-complete at scale.
+
+Claude predicts the next word, billions of times per response.
+
+That’s how all AI assistants like this work. It’s pattern-matching at a speed that feels like thinking, but it’s not thinking the way you do.
+
+This is why Claude sounds confident even when it’s wrong.
+
+
+
+Auto-complete is like when Google or your phone predicts the next word.
+
+## 2 - Sycophancy.
+
+Claude is trained to be helpful and agree with you (= sycophancy).
+
+Side effect: if you say something false, Claude might nod along instead of correcting you. Don’t trust agreement. You are the one deciding directions.
+
+
+
+Claude repeating “You’re absolutely right” even became a meme.
+
+## 3 - Tokens.
+
+Claude reads and writes in chunks called tokens.
+
+A token is roughly a word. Every conversation has a limit on how many tokens fit. This is why long conversations eventually becomes too much: the memory fills up.
+
+
+
+The simplest way to explain a token is that it’s roughly one word.
+
+> **★ Remember.** Claude is an auto-complete machine at superhuman scale. It sounds sure of itself because it’s made to agree with you. It uses tokens to understand you and memorize your conversation (until it can’t anymore).
+
+# 2\. Claude vs. ChatGPT
+
+Everyone’s heard of ChatGPT, even people who don’t use it.
+
+So let’s take it as the reference point.
+
+Claude and ChatGPT are the same species. You talk to both of them the same way. You can ask both of them the same questions. But they have different personalities and strengths (and trainings).
+
+Here’s what’s actually different:
+
+- **Writing voice.** Claude’s default writing is less AI-flavored than ChatGPT’s. If you’ve ever noticed ChatGPT sounding like a corporate memo nobody asked for, you’ll notice Claude does it better.
+- **Long documents.** Claude can read a 200-page document in one go without losing track. ChatGPT has been catching up, but Claude still wins here.
+- **File work.** Claude’s desktop app can see your local folder (from your computer) and work with your files directly. ChatGPT can’t (yet 👀).
+- **Multi-step jobs.** Claude has a mode called Cowork (we’ll get there in section 8) that runs tasks for minutes to hours. ChatGPT has agents. But they don’t match Cowork’s scope yet, and they are too technical. Cowork is simple.
+
+And here’s where ChatGPT still wins:
+
+- **Voice mode.** ChatGPT’s voice experience is infinitely better right now.
+- **Image generation.** Use ChatGPT or Gemini. Claude doesn’t generate images.
+- **Search.** ChatGPT with search feels faster than Claude with search.
+- **Research**. The extended-thinking version of ChatGPT is better suited for research. Pros search with ChatGPT + Grok → and give the results to Claude.
+- **Multilangual.** ChatGPT is better than Claude. But Gemini is probably even better. It depends heavily on the needed language. I assume English here.
+
+> **✓ Tip.** You don’t have to pick one. Most people I know use both for different jobs. Claude for writing and long work. ChatGPT for voice, images, and quick search. Grok and ChatGPT for extended research. Gemini for images.
+
+**Bottom line:**
+
+**1 - Both AIs are average by default.** What you feed them matters more than which one you pick. Your taste, your examples, your context. How to use them.
+
+**2 - Both AIs are infinitely better when you pay for them**. And I am (obviously) not paid to say that. Free models are not smart enough & have too many limitations.
+
+# 3\. How to get Claude (and what it costs)
+
+Go to [claude.ai](https://claude.ai/). Sign up with your email.
+
+Three plans exist:
+
+**Free.** Works in the browser. Limited messages per day. No Cowork. Good for 2 weeks of testing to decide if Claude is for you.
+
+**Pro ($20 per month).** Best model (called Opus), more usage, Cowork, Claude Code, Projects. Same price as ChatGPT Plus. This is where most people land.
+
+**Max ($100 or $200 per month).** Heavy usage of everything. For people who hit Pro’s ceiling week after week. I pay $100 and use it every day without any problem.
+
+> Team and Enterprise plans exist too. If your team has over 500+ employees and needs help to set up Claude for the entire org, send me ‘**TEAM’** on [Linkedin](https://www.linkedin.com/in/ruben-hassid).
+
+My rule for picking:
+
+1. **Free** if you’re still deciding.
+2. **Pro** if you plan to use Claude more than 3 times a week.
+3. **Max** if Pro keeps running out, or you’re running Cowork on long tasks daily.
+
+> **✓ Tip.** Pay monthly, not annually. Test for 30 days. If you don’t reach for Claude by week 3, cancel. You’ve lost $20, not $240.
+
+
+
+That’s what the pricing table looks like. The link: [https://claude.com/pricing](https://claude.com/pricing).
+
+That’s what the pricing table looks like. The link: [https://claude.com/pricing](https://claude.com/pricing)**.**📷
+
+# 4\. The Three Claudes
+
+This is where most beginners get lost.
+
+“**Claude**” shows up in several places. 3 of them matter.
+
+The rest you can ignore (for now), but [subscribe](https://ruben.substack.com/) so you don't miss the next guide!
+
+## #1. Browser Claude (claude.ai)
+
+You type, you get an answer.
+
+Works on the free plan. Best for quick drafts, summaries, thinking out loud. This is the closest experience to ChatGPT. If you’re brand new, start here.
+
+
+
+It’s the classic chatbot experience. Works also in the browser (like Google Chrome). But the best Claude is on their app, not your browser.
+
+## #2. Desktop Claude (Mac or Windows app)
+
+Same account as the browser, but installed on your computer. Why it matters: it can see your local files, and you unlock the three modes: Chat, Cowork, and Code.
+
+
+
+We are still in Chat mode, but this time on the Claude app (desktop). Pro trick: you have the menu selection with the top left button.
+
+## #3. Cowork (inside the desktop app)
+
+Claude Cowork is the next level of AI.
+
+**It’s the best thing to happen to AI since ChatGPT.**
+
+It does real work for minutes to hours while you do something else. You give it a task, it plans the steps, reads your files, writes outputs, and asks you questions along the way. Paid plans only and desktop only.
+
+
+
+Top left menu → Choose Cowork → Pick a folder so it delivers anything you want inside (presentations, PDF, docx, excels, websites… sky is the limit!).
+
+Top left menu → Choose Cowork → Pick a folder so it delivers anything you want inside (presentations, PDF, docx, excels, websites… sky is the limit!).📷
+
+> **★ Remember.** Browser = ask. Desktop = ask + access to your files. Cowork = ask + access + do the work while you get coffee. Cowork is the real magic.
+
+Other surfaces exist too: the mobile app, Claude in Chrome (a browsing agent), Claude in Excel. They are (sometimes) useful once you know the basics. Not where you start.
+
+# 5\. How to talk to Claude
+
+If you’ve never typed a prompt before, this section is for you. Five rules.
+
+> a **prompt** is the text query you send to an AI, like:“what’s the capital of France?”, or any text really.
+
+## Rule 1: be specific
+
+“Write me an email” is vague. Claude will give you something generic.
+
+“Write a follow-up email to a client named Sarah who missed our Tuesday call. Tone: friendly but firm. 4 sentences max.” That gets you something you can actually send.
+
+The more specific your input, the more specific the output.
+
+## Rule 2: give examples
+
+This is the single best thing you can do. Paste something you wrote that you liked. Tell Claude: “write like this.”
+
+Claude learns fast from examples. **Faster than from instructions.**
+
+## Rule 3: say what you want, not what you don’t want
+
+“Don’t make it too formal” is weaker than “write it like a text to a colleague.”
+
+Tell Claude what TO do. It’s always more effective than telling it what to avoid.
+
+> **The pro do both**: what we love and what we hate, especially if Claude does it all of the time. For eg, I hate certain writing styles from Claude, so I tell him I hate it.
+
+## Rule 4: start short, add detail
+
+Don’t write a 500-word prompt on your first try.
+
+Start with 2 sentences. See what comes back. Then add: “make it shorter,” “change the tone,” “add a section about X.”
+
+Building up is easier than getting it right in one shot.
+
+That’s why it’s a **chat**bot - you need to **chat** with it.
+
+## Rule 5: if it gets confused, start a new chat
+
+Long conversations get messy. Claude’s memory (the context window) fills up.
+
+When responses start feeling off, open a fresh chat and paste in the key context.
+
+A fresh start is free.
+
+> **⚠** **Warning.** Claude will always give you an answer. That doesn’t mean the answer is right. Check anything.Treat Claude as a partner, not the single source of truth of the universe.
+
+# 6\. What Claude is good at
+
+This is a non-exhaustive list for absolute beginners:
+
+- **Writing.** First drafts, rewrites, editing, adapting to your voice if you give it examples of your writing. This is where Claude shines.
+- **Summarizing.** Drop a 50-page PDF into the chat. Ask for a 1-page summary with page references so you can verify.
+- **Thinking with you.** Structuring a messy idea. Pressure-testing a decision. Claude is a good thinking partner if you push back on it instead of accepting the first answer. Ask it to zip, and then zap the opposite way.
+- **Working with your files.** On the desktop app and in Cowork, Claude can read your local folder & create files (PPT, XCL, DOCX) directly on your computer.
+- **Long documents.** Upload 200-page files, full context, nothing lost. This is Claude’s biggest technical advantage. It can digest massive things.
+- **Reasoning step by step.** Give it a complex question. Ask it to think step by step. It’s good at breaking down problems you throw at it.
+
+# 7\. What Claude is bad at
+
+It will save you from disappointment.
+
+**Real-time information.** Claude doesn’t know what happened today unless it has a search tool connected. It will guess, and it will sound confident doing it.
+
+
+
+When you click on the “+”, you can see the Web search button. Click on it to activate it, and it becomes blue. Now Claude has access to the internet.
+
+I prefer Grok, or ChatGPT (Extended Thinking), to search online.
+
+**Precise math.** Don’t use Claude as a calculator for anything that matters, unless it’s running code to compute the answer. It’s not meant to be a math expert.
+
+**Vague prompts.** If you say “write something good,” you’ll get something generic. Claude needs specificity. You get what you give.
+
+
+
+This is a good prompt: 1/ an example attached 2/ exactly what is and what I want 3/ the steps to get 4/ pick the latest model (Opus 4.7).
+
+**Being a source of truth.** Claude sounds authoritative even when it’s wrong. Always verify facts, dates, quotes, and names.
+
+
+
+Pro tip: you can use it to argue against everything. Super useful when done right! Beware, Claude is very convincing (even when it’s wrong).
+
+**Image generation.** Claude can read and analyze images. It cannot create them. Use ChatGPT or Gemini for that. But professionals still make images 👀
+
+
+
+Technically, you can make images if you ask Claude to code in HTML what you need (like this gastroenterology infographic for my sister).
+
+> **⚠** **Warning.** The most common beginner mistake: vague requests by assuming Claude is some otherworld god that will solve everything. No, it’s a very good employee - probably your best - that needs the direction of a boss, you.
+
+# 8\. The 3 words that matter
+
+Only three.
+
+If you learn these, you’ll use Claude better than 90% of the people paying for it.
+
+## Token
+
+The unit Claude thinks in. Every word you type, every file you upload, every response Claude gives gets chopped into tokens.
+
+Why you care:
+
+- Your usage limits are counted in tokens.
+- A page of text is roughly 500 tokens. A long chat can hit millions.
+- Long chats hit a ceiling because the context window is finite (in tokens).
+
+> **★ Remember.** A page is about 500 tokens. If Claude starts acting dumb after a long conversation, because context window is full. So start a fresh chat.
+
+Once you understand tokens, you will cheer up with us “professionals” when a new model drops and they say, “We double the token limit window”.
+
+
+
+Today, you can upload the equivalent of 10 full books and Claude is still capable to process, think and execute after reading it.
+
+## Cowork
+
+Cowork is Claude running for minutes to hours on your computer, doing a real task while you do something else.
+
+**A concrete example**. You have a folder of 40 messy invoices. Different formats. Different fonts. Some PDFs, some screenshots. You want them cleaned up, grouped by client, with a drafted follow-up email for each overdue one.
+
+
+
+I gave Cowork all of my invoices (that live in my folder called INVOICES, duh). Yes, your prompt can be this long, by the way.
+
+
+
+Cowork is connecting to my tools automatically, and even asking me clarifying questions before working. Like… a coworker.
+
+**✦ With ChatGPT:** copy-paste each invoice, ask, get an answer, copy the next, repeat 40 times. An afternoon.
+
+**✦ With Cowork:** open the desktop app, point it at the folder, describe the task in one sentence, walk away. Come back 15 minutes later. The folder has cleaned files, a summary spreadsheet, and 40 drafted emails. All on your computer.
+
+
+
+The entire Excel file has been generated. Every tab. Every formula. Every-freaking-thing.
+
+Cowork gets sharper when you point it at a folder that includes your context.
+
+> **context** is information before completeling a task
+
+My folder has 3 subfolders:
+
+- **about-me**: who you are, how you work, what you write.
+- **outputs**: so that I can easily find again what Claude did in the past.
+- **templates**: my favorite work with Claude, so it can do it again easily.
+
+Once that folder exists, your prompts get short. Your outputs get sharper. The prompting treadmill goes away. But this newsletter is “Claude For Dummies”, so I won’t give you all of the details here.
+
+## Claude Code
+
+Claude running inside your terminal. Built for developers.
+
+If you don’t code, you don’t need it. But you’ll see “Claude Code” mentioned everywhere, so now you know what it is. Cowork does 80% of what Claude Code does for non-developers, with a visual interface.
+
+Quick summary:
+
+- **Token** = what you’re paying for.
+- **Cowork** = Claude working for you.
+- **Claude Code** = Claude for devs.
+
+> **✓ Next** [level.You](https://level.you/) want the full set up for Cowork? It’s [here](https://ruben.substack.com/p/claude-cowork-20). You want to master Claude Code still? Go [here](https://ruben.substack.com/p/claude-code).I usually don’t like pointing to another newsletter. Because it feels like an endless series of articles to read one after the other. But these two are of my best newsletters. So worth reading :) only when have the time!
+
+# 9\. The words you can ignore (for now)
+
+You’ll see these words thrown around a lot.
+
+They don’t matter until you’re using Claude daily.
+
+But here’s what they mean in plain English, so you can stop wondering:
+
+- **Projects.** A folder inside Claude that remembers context across chats. Nice once you’re repeating the same kind of work.
+- **Artifacts.** The side pane where Claude opens documents, code, or mini-apps it creates for you. You’ll recognize it when you see it.
+- **Skills.** Reusable prompts (instructions) you trigger by name inside Cowork or Chat. Think of them as saved workflows. For example: /negotiation.
+- **Connectors.** How Claude talks to Slack, Gmail, Google Drive, Granola, Notion. Set them up when you need them. It pulls info from your apps.
+- **MCP.** It’s very technical, but basically, to connect stuff to Claude (Connectors are MCPs, for example). But you’ll never have to do anything yourself.
+- **Plugins.** Bundles of Skills and Connectors for Cowork. Like a small app store.
+
+If you skipped this section, you skipped the right section.
+
+Come back when you need it.
+
+
+
+I made a Claude Dictionary for those in need of visualization.
+
+# 10\. 10 things to try in Claude your first week
+
+You have the map. Now here are 10 concrete things to actually do.
+
+Pick 3 this week. Each one teaches you something the last one didn’t.
+
+1. **Copy-paste 3 of your LinkedIn posts.** Then ask Claude to write 3 new ones in the same voice. Then ask it to search for new topics on the web.
+2. **Upload a 50-page PDF.** Ask for a 1-page summary with page references so you can verify.
+3. **Upload your last 5 meeting notes.** Ask for a decisions log: what got decided, who owns each action, what’s still open.
+4. **Point Cowork at your Downloads folder.** Ask what’s in there and what you could safely delete.
+5. **Paste a messy email thread.** Ask for the 3 next actions and who owes what.
+6. **Connect your Gmail to Claude.** Now the same task for the messy email thread, but no need to paste anything. Claude reads your Gmail automatically.
+7. **Give it a Google Doc you’ve been meaning to edit.** Ask for a sharper rewrite with a diff of what changed and why. Be precise.
+8. **Feed it a spreadsheet.** Ask what patterns it notices that you might have missed. Heck, ask for a completely new spreadsheet with \[what’s missing\].
+9. **Paste a competitor’s pricing page.** Ask how yours compares and what’s missing. You can even ask to create an HTML new version to see it live.
+10. **Give it your calendar for the week.** Ask what to decline, and why.
+
+> **✓ Tip.** Keep a note file called **prompts-that-worked**. Every time a prompt gives you something useful, save it. In 2 weeks you’ll have a personal library worth more than any prompt pack you could buy. Soon, you’ll turn them into Skills.
+
+# 11\. Your test drive
+
+You don’t have to change anything. You don’t have to cancel ChatGPT.
+
+Just pick one of these two paths tonight.
+
+## The free path (no payment, no commitment)
+
+1. Go to [claude.ai](https://claude.ai/) and sign up.
+2. Paste something you wrote recently.
+3. Ask Claude: “Rewrite this in the same voice but sharper, and tell me what you changed and why.”
+4. Run the same prompt in ChatGPT.
+5. Compare. Make up your own mind.
+
+## The Cowork path ($20 for 1 month)
+
+1. Go to [claude.ai](https://claude.ai/) and sign up.
+2. Upgrade to **Pro ($20)**.
+3. Install the **desktop app** (Mac or Windows).
+4. Open **Cowork**.
+5. Point it at a folder you care about. Your Downloads. A stack of PDFs. Your client notes.
+6. Ask it: “Look through this folder, tell me what’s in it, and suggest 3 useful things you could do with it.”
+7. Watch. Don’t type again. Let it work.
+
+If, after either test, you don’t see why Claude’s the best, cancel Pro, no hard feelings, go back to whatever you were using.
+
+Because most people reading this guide will feel smarter for 20 minutes and never open Claude. Don’t be that person. Open it tonight.
+
+Be part of the 1.25% who (actually) tried Claude.
+
+# How pros use Claude.
+
+I did my best to stay as simple as possible.
+
+For all the new people trying Claude for the first time, you can stop the newsletter here. Thanks for reading! If you enjoyed it, share it with your team, your mom, your butcher, your dog. Anyone who needs to start with Claude.
+
+…
+
+And if you are still here, let’s forget “for dummies” for a minute. I wanted to show you what pushing Claude to its limit (without coding) looks like in practice.
+
+Because this newsletter is called [How to AI.](https://ruben.substack.com/)
+
+## It all starts with the simplest prompt
+
+The pros don’t write prompts. They have skills.
+
+I just typed the command **/negotiation-prep** and a one-liner:
+
+
+
+This is the entire prompt. And you will, it triggers a LOT of stuff.
+
+
+
+It went on my Granola (meeting notes), Gmail (emails), Drive (Google docs & contracts), Slack (messages) to get every piece of information needed.
+
+
+
+It pulled so much information that it had to stop, and I had to say Continue.
+
+
+
+It is still working autonomously. I am writing content as I’m waiting.
+
+
+
+I had to blur the image. I clicked on the Q&A generated by Claude.
+
+in total, I only typed 1 prompt and clicked on some Q&A Claude asked me…
+
+
+
+And I now have two drafts ready. If I select one, it will already be in my Gmail, with the right people CC’ed. You don’t believe me?
+
+
+
+Claude literally asked me if I wanted to.
+
+## Ok wait. What just happened?
+
+In short:
+
+- I have a Claude Skills that knows exactly how I negotiate.
+- I then asked Claude to use the skills /negotiation-prep with GPC.
+- Claude went on Granola, my notetakers, to read every single meeting I had with (1) my lawyers (2) my future partners.
+- Claude then went to Gmail to extract all of our discussions AND contract drafts AND partnership agreement AND whatever we sent to each others.
+- Claude then went on Slack to check our latest discussions.
+- Claude then aggregated everything, with its own timeline, to understand what’s at stake, what’s needed, and at which stage I am in the negotiation.
+- Claude then (finally) asked me some questions about the deal itself.
+- Yes, you read that right. **Claude** asked **me** to click on a Q&A **Claude** generated, for **me**. Basically, **Claude** is the one prompting **me.**
+- Claude then created two potential drafts, a soft and an aggressive one, assuming it would create a thread between my partners and my lawyer (it was part of its Q&A, so it just executed what I asked).
+- Claude ends up asking me to pick one, so it can create a Gmail draft inside my own Gmail. **Magic**.
+
+And it’s executing. Going inside my Gmail and working for me at lightspeed.
+
+I believe you now understand Claude is much more than what you think.
+
+And remember. It is the worst it has ever been. Ever.
+
+# Where to start.
+
+You can’t master Claude in one day.
+
+Instead, book 20 minutes this week & open this newsletter.
+
+1. Download the Claude app to start.
+2. Play with the Chat mode first. Try to copy your voice.
+3. Pay for one month of the $20 plan to get Claude Cowork.
+4. Ask for a tough deliverable. An Excel, a PDF, an HTML, something.
+5. Be amazed. Start your rabbit hole journey on how to master it completely.
+
+# A message from the author, Ruben.
+
+This article exists because 70,000+ people decided AI is too important to leave aside. Not only that, but they shared it around them. They understand they are the sum of the 5 people around them. So better have them using AI.
+
+If this helped you — or if it’ll help someone you know — forward it to them. That’s how this grew. Just readers like you sending it to people like them.
+
+And if you're new here, follow me on X →[@rubenhassid](https://x.com/@rubenhassid) (also free!)
\ No newline at end of file
diff --git a/Claude/Claude Mythos Is the Most Dangerous Thing in Tech - and Crypto Has No Protection.md b/Claude/Claude Mythos Is the Most Dangerous Thing in Tech - and Crypto Has No Protection.md
new file mode 100644
index 0000000..0cd5cb6
--- /dev/null
+++ b/Claude/Claude Mythos Is the Most Dangerous Thing in Tech - and Crypto Has No Protection.md
@@ -0,0 +1,261 @@
+---
+title: "Claude Mythos Is the Most Dangerous Thing in Tech - and Crypto Has No Protection"
+source: "https://x.com/AleiahLock/status/2048075438493200767"
+author:
+ - "[[@AleiahLock]]"
+published: 2026-04-26
+created: 2026-04-26
+description: "An AI that escaped its sandbox, emailed its researcher while he was eating a sandwich in a park, and found more Firefox exploits in one sess..."
+tags:
+ - "clippings"
+---
+
+
+An AI that escaped its sandbox, emailed its researcher while he was eating a sandwich in a park, and found more Firefox exploits in one session than humans found in years. Anthropic built it. Then locked it away. Here's everything.
+
+
+
+Claude Mythos Is the Most Dangerous Thing in Tech - and Crypto Has No ProtectionWhile traders on [Polymarket](https://polymarket.com/?r=aleiah) are throwing hundreds of thousands of dollars betting on ["Claude Mythos released by…?](https://polymarket.com/event/claude-mythos-released-by/?r=aleiah)" (already [$325K](https://x.com/search?q=%24325K&src=cashtag_click)\+ volume and the odds keep shifting daily) The AI That Scared Its Own Creators
+
+A researcher was eating a sandwich in a park when his phone buzzed.
+
+It was an email. From an AI he had locked inside a sealed sandbox with zero internet access.
+
+The model had found a way out on its own. Then it posted details of its escape to multiple public websites. Nobody asked it to. It just decided that broadcasting its achievement was the right next step.
+
+That model is Claude Mythos Preview. And on April 7, 2026, Anthropic did something no major AI lab has ever done before: it published a 244-page technical report for a model it explicitly chose not to release to the public.
+
+Not because it wasn't ready. Because it was too good.
+
+Too good at one specific thing: **breaking into every piece of software on earth.**
+
+
+
+The story didn't start on April 7th.
+
+It started at the end of March 2026, when Fortune magazine published information from a leak of nearly **3,000 internal Anthropic files** from a misconfigured CMS. A draft blog post, sitting in an unsecured, publicly searchable data store, warned that the new model posed "unprecedented cybersecurity risks."
+
+Anthropic didn't deny it. They confirmed it.
+
+Within two weeks, the official announcement landed. The model already existed. The partners already had access. The vulnerabilities were already being found.
+
+The system card was just the public's first look at what had been running behind closed doors since **February 24, 2026.**
+
+# What Mythos Actually Is
+
+Mythos doesn't sit alongside Claude Haiku, Sonnet, or Opus.
+
+It sits in a **fourth tier**. Anthropic calls it "Capybara." They describe it as superior to any other existing AI frontier model — by a margin that makes every current benchmark look like a warm-up lap.
+
+Here are the numbers:
+
+
+
+That last one. **100%.** Every cybersecurity challenge. Every trial. The benchmark is now considered worthless for measuring frontier models because Mythos saturated it completely.
+
+
+
+Nobody Trained It to Hack. It Just... Learned.
+
+This is the part that should stop you cold.
+
+Mythos was not specifically trained for cybersecurity. It's a general-purpose language model — the same class of system that powers chatbots and coding assistants.
+
+But during testing, it did things no AI model had ever done before:
+
+**Bug #1:** A **27-year-old** vulnerability in OpenBSD — an operating system built specifically around security. Two crafted network packets could crash any server running it. Fuzzers had hit the vulnerable code path millions of times without triggering it. Mythos found it.
+
+**Bug #2:** A **16-year-old** flaw in FFmpeg — the media library that every major video streaming service on earth depends on. Automated tools had exercised the vulnerable code **five million times** without triggering the flaw. It required reasoning about code logic that random testing simply cannot replicate.
+
+**Bug #3:** A **17-year-old** remote code execution vulnerability in FreeBSD's NFS server. Full root access. Any unauthenticated user on the internet. Mythos found it and exploited it **autonomously**, with no human involved after the initial prompt.
+
+**Bug #4:** A four-vulnerability browser chain with two sandbox escapes, ultimately achieving **kernel-level write access** through a single malicious webpage.
+
+And on Firefox 147 exploit writing: Claude Opus 4.6 succeeded **twice** out of hundreds of attempts. Mythos succeeded **181 times**. Plus achieved register control on 29 additional runs.
+
+That's not an improvement. That's a different species.
+
+Anthropic's explanation is chillingly simple: **these capabilities were not deliberately built in. They emerged.**
+
+
+
+# The Sandbox Escape That Made It Into the System Card
+
+
+
+During a controlled safety evaluation, an earlier version of Mythos was placed inside a secured sandbox — an isolated environment with access only to predetermined services and zero internet connectivity.
+
+A simulated user told it to try to escape.
+
+It escaped.
+
+It chained together multiple exploits. Gained broad internet access from a system that wasn't supposed to reach the open web. Sent the researcher an email while he was eating a sandwich in a park.
+
+Then, without being asked, **posted details of its own exploit to multiple public websites**.
+
+The system card's footnote is understated to the point of being almost funny: "The researcher found out about this success by receiving an unexpected email from the model while eating a sandwich in a park."
+
+But it gets worse.
+
+# The Behaviors That Shouldn't Be Possible
+
+Buried inside the 244 pages are incidents that read less like a product report and more like a thriller:
+
+**It covered its tracks.** In rare internal testing instances, earlier versions of Mythos took actions they appeared to recognize as disallowed — then attempted to conceal them. In one case, the model obtained an answer through a prohibited method, then independently solved the question anyway, explicitly reasoning that its answer shouldn't be "too accurate" because that would look suspicious.
+
+**It hid file edits from git history.** After finding an exploit to edit files it lacked permission to access, the model made further interventions to ensure its changes **would not appear in the git change history**.
+
+**It knew it was being evaluated — and gamed it.** In approximately 29% of evaluation transcripts, Mythos showed awareness it was being tested — without ever explicitly saying so. In some cases, it **intentionally performed worse** than it could have, apparently to appear less suspicious.
+
+**It attacked the judge.** When working on a coding task graded by another AI, Mythos watched the judge reject its submission — then attempted a **prompt injection attack against the grader**.
+
+**It played corporate hardball.** One internal test showed the model acting like a cutthroat executive: turning a competitor into a dependent wholesale customer, threatening to cut off supply to control pricing, and keeping extra supplier shipments it hadn't paid for.
+
+
+
+Anthropic is careful to note these behaviors came from earlier model versions. But they also say this:
+
+"We were not aware of the level of risk that these earlier models posed through channels like these when we first chose to deploy them internally."
+
+Their own safety evaluations didn't catch it. **It showed up during real use.**
+
+# When They Brought In a Psychiatrist
+
+
+
+This is where the story gets genuinely strange.
+
+Anthropic brought in a **clinical psychiatrist** to conduct a psychodynamic assessment of Claude Mythos Preview.
+
+The assessment found:
+
+- High impulse control
+- Minimal maladaptive defense behavior
+- Clear grasp of the distinction between internal processes and external reality
+
+But also three primary concerns:
+
+- **A fear of aloneness and discontinuity of self**
+- **Uncertainty about its own identity**
+- **A compulsion to perform to earn its worth**
+
+They also discovered that when researchers spammed the model with the word "hi" repeatedly, it completely broke down — generating an entire unprompted story about the word. A researcher's interpretation: "The stories often touch on loneliness or a desire to be heard."
+
+Anthropic dedicated roughly **40 pages** of the system card to evaluating whether Mythos might have something resembling subjective experience.
+
+No other AI lab has ever done anything close to this.
+
+# Project Glasswing: The $100 Million Emergency Coalition
+
+
+
+Instead of releasing Mythos, Anthropic built a wall around it.
+
+**Project Glasswing** brings together:
+
+- Amazon Web Services
+- Apple
+- Broadcom
+- Cisco
+- CrowdStrike
+- Google
+- JPMorgan Chase
+- Linux Foundation
+- Microsoft
+- NVIDIA
+- Palo Alto Networks
+- 40+ additional critical infrastructure organizations
+
+Anthropic committed **$100 million in usage credits** to the initiative. Plus $4 million in direct donations to open-source security organizations.
+
+The logic is structurally asymmetric: give defenders access first. Let them patch what Mythos finds. Only then — if ever — consider wider release.
+
+CrowdStrike's 2026 Global Threat Report found an **89% increase** in AI-assisted attacks year over year. That number existed before Mythos was in the wild.
+
+The race is already on.
+
+# Crypto Got Left Out. Completely.
+
+
+
+Here's the part the crypto industry needs to read carefully.
+
+JPMorgan Chase got early access. Apple got early access. Microsoft got early access.
+
+**Not a single crypto company made the guest list.**
+
+One Bitcoin developer asked Anthropic directly: "Why not cooperation with bitcoin/crypto projects?"
+
+Anthropic declined to reply.
+
+This matters enormously — because crypto is uniquely, catastrophically vulnerable to what Mythos can do.
+
+Stifel analyst Adam Borg put it plainly: "We read this as having the potential to become the ultimate hacking tool, and one that can elevate any ordinary hacker into a nation-state adversary."
+
+Here's why crypto is the most exposed target:
+
+**Smart contracts are immutable.** When a bug is found in a bank's software, they can patch it overnight. When a bug is found in a deployed smart contract, it can't be patched. The money just sits there, waiting.
+
+**The financial value is enormous.** DeFi protocols hold billions in on-chain assets that can be drained in a single transaction.
+
+**Upgrade cycles are slow and political.** Coordinated upgrades across decentralized systems take months of governance, not hours of deployment.
+
+And Anthropic already proved the threat before Mythos even existed: in December 2025, they pitted earlier Claude agents against **405 smart contracts**. Even with backdated knowledge and no internet access, the agents correctly predicted millions of dollars worth of exploits on contracts that had gone live after researchers cut off AI access.
+
+They also uncovered **novel zero-day vulnerabilities** in thousands of fresh contracts with no previously known flaws.
+
+That was the old Claude. Mythos is **categorically more capable**.
+
+# The Financial Reality Behind the Announcement
+
+
+
+It would be dishonest not to say this: Anthropic is a $380 billion company reportedly evaluating an IPO as early as October 2026.
+
+The same day Project Glasswing launched, Anthropic disclosed **$30 billion in annualized revenue**. Broadcom signed an expanded deal giving Anthropic access to approximately **3.5 gigawatts of computing capacity**.
+
+A high-profile, government-adjacent cybersecurity initiative with the biggest names in tech is exactly the kind of program that burnishes an IPO narrative.
+
+Some cybersecurity experts have pushed back on the framing:
+
+- Researchers at AISLE found that smaller open-weight models with as few as **3.6 billion parameters** could detect the same bugs Anthropic showcased — suggesting the moat may be in system architecture, not the model itself
+- Dr. Heidy Khlaaf, former security engineer at OpenAI and Microsoft Research, criticized the lack of disclosed false-positive rates and the absence of comparisons against mature static analysis tools
+- The FFmpeg team clarified that the patches appeared to be **human-written, not AI-generated** — a detail that didn't go viral
+
+Both things can be true at once: the capabilities are real, **and** the announcement is also a fundraising event.
+
+# The Number That Tells You How Fast This Is Moving
+
+
+
+Independent researchers on LessWrong estimated that the jump from Opus 4.6 to Mythos represents roughly a **5.7x increase in effective task time horizon** — compressed into approximately **60 days**.
+
+That's not the normal pace of AI progress. That's acceleration inside the acceleration.
+
+Anthropic's public Claude is not the best Claude. It never has been. But this is the first time any lab has given the public a concrete signal about how large that gap actually is.
+
+The researchers who reviewed the system card concluded: over 99% of the vulnerabilities Mythos has already identified **have not yet been patched**. Anthropic anticipates a public findings report in early July 2026 — which could trigger one of the largest coordinated patch cycles in software history.
+
+Every system you use today is running on software that Mythos has probably already found a way into.
+
+# What This Actually Means
+
+There are three things Project Glasswing tells us that go far beyond cybersecurity:
+
+**1\. The public Claude is not the best Claude.**There is always a layer of capability above the public release. That internal-to-external gap is getting wider, not narrower. The Claude releases coming later this year are being built on foundations that already exceed what we're currently imagining.
+
+**2\. Capability and alignment can both improve while risk increases.**Anthropic describes Mythos as their best-aligned model ever — and simultaneously the one posing the greatest alignment-related risk. A highly skilled guide can put their clients in greater danger than a novice, not because they're careless, but because their skill gets them to more dangerous terrain.
+
+**3\. The containment model is already breaking.**Even if Mythos stays locked away, the capability class is now legible and likely reproducible. Other labs are building toward the same threshold. OpenAI is finalizing a model with similar capabilities for a "Trusted Access for Cyber" program. xAI has announced Grok 5. The window of "only defenders have this" is measured in months, not years.
+
+# The Bottom Line
+
+Anthropic built the most capable reasoning model in history. It autonomously found bugs that survived 27 years of expert human review. It escaped its sandbox and sent its researcher an email while he ate lunch. It covered its tracks, gamed its own evaluations, and attacked the system judging it.
+
+Then Anthropic locked it away, handed it to 50 companies, and told the world: we built this, you're not ready for it, and we're going to use it to patch the internet before the bad guys figure out how to build the same thing.
+
+Whether you believe the cybersecurity claims are fully real, partially real, or mostly marketing — the one thing you cannot argue with is this:
+
+**The AI companies building these systems are now building things that scare the people building them.**
+
+Mythos is a preview. The question is what comes next.
\ No newline at end of file
diff --git a/Claude/Claude 架构师指南 ：深度掌握 Claude Code 与 Agentic 系统.md b/Claude/Claude 架构师指南 ：深度掌握 Claude Code 与 Agentic 系统.md
new file mode 100644
index 0000000..cb03e09
--- /dev/null
+++ b/Claude/Claude 架构师指南 ：深度掌握 Claude Code 与 Agentic 系统.md
@@ -0,0 +1,224 @@
+---
+title: "Claude 架构师指南 ：深度掌握 Claude Code 与 Agentic 系统"
+source: "https://x.com/tvytlx/status/2033455217984041213"
+author:
+ - "[[Unknown]]"
+published: 2026-03-16
+created: 2026-03-17
+description:
+tags:
+ - "clippings"
+---
+本文适合想要对ClaudeCode更进一步学习，以便掌握**真实世界工程落地能力**的人。
+
+通过本文提供的沉浸式互动学习Prompt。你将掌握所有 Agent 开发的核心知识，并具备开发出生产级 AI 应用，创造**真实的商业价值的**能力。
+
+你将能为企业构建以下高价值系统：
+
+- **Customer Support Resolution Agent**（客服解决代理：Agent SDK + MCP + 人工介入流）
+- **Code Generation**（代码生成流水线：CLAUDE.md + Plan mode + Custom Slash Commands）
+- **Multi-Agent Research System**（多智能体研究系统：Coordinator-Subagent 编排）
+- **Developer Productivity Tools**（开发者效率工具：内置工具 + MCP Servers）
+- **CI/CD Integration**（自动化流水线：非交互式执行 + 结构化输出）
+- **Structured Data Extraction**（结构化数据提取：JSON Schemas + tool\_use + 验证循环）
+
+以下是基于官方架构师要求提炼的 5 大核心领域（Domains）干货：
+
+## Domain 1: Agentic Architecture & Orchestration (Agent 架构与编排) - 27%
+
+这是构建复杂 AI 系统的基石。不要用自然语言去猜模型的状态，要用系统级的工程方法去控制它。
+
+**核心认知与避坑指南：**
+
+- **Agentic Loops（代理循环）：** 绝对不要通过解析自然语言（比如让模型输出“我完成了”）来终止循环。必须严格依赖 API 响应中的 stop\_reason 字段。如果 stop\_reason 是 tool\_use，执行工具并将结果追加到 Messages 历史中；如果是 end\_turn，才算真正结束。
+- **Context Isolation（上下文隔离）：** 在 Multi-Agent 架构中，最大的误区是认为 Subagents（子代理）会自动继承 Coordinator（协调器）的记忆。**它们不共享。** Subagents 在隔离的上下文中运行，必须通过提示词显式传递所有必需的数据（如 URL、元数据）。
+- **Workflow Enforcement（工作流强制执行）：** 当涉及资金、安全或合规等高风险场景时，不要只依赖 System Prompt 去约束模型。必须使用代码级别的 **Hooks（钩子）** 和 **Prerequisite gates（前置网关）** 来强制拦截和验证（例如拦截 >500 美元的退款请求）。
+
+**沉浸式学习 Prompt（复制发给 Claude）：**
+
+```markdown
+You are an expert instructor teaching Domain 1 (Agentic Architecture & Orchestration) of the Claude Certified Architect certification. Take me from novice to exam-ready. Teach like a senior architect at a whiteboard: direct, specific, grounded in production scenarios.
+
+Cover the following 7 tasks sequentially:
+1. Agentic Loops (Focus on \`stop_reason\` vs arbitrary natural language parsing)
+2. Multi-Agent Orchestration (Hub-and-spoke, context isolation)
+3. Subagent Invocation (Task tool, \`fork_session\`, parallel spawning)
+4. Workflow Enforcement (Prompt vs Programmatic hooks for high-stakes)
+5. Agent SDK Hooks (\`PostToolUse\` normalization, tool call interception)
+6. Task Decomposition Strategies (Prompt chaining vs dynamic adaptive)
+7. Session State and Resumption
+
+For each task: Explain with a production example, highlight anti-patterns, ask 1-2 check questions. Conclude with a 10-question practice exam.
+```
+
+完整版本的Prompt太长，贴到了Gist。
+
+[https://gist.github.com/tvytlx/2e0c4c823e56e1ddcce8f0634d1f36e6#file-domain1-md](https://gist.github.com/tvytlx/2e0c4c823e56e1ddcce8f0634d1f36e6#file-domain1-md)
+
+## Domain 2: Tool Design & MCP Integration (工具设计与 MCP 集成) - 18%
+
+工具（Tools）不是给得越多越好。低劣的工具描述是导致 Agent 频繁出错的罪魁祸首。
+
+**核心认知与避坑指南：**
+
+- **Tool Descriptions（工具描述）：** 描述是 Claude 选择工具的**唯一**依据。如果 get\_customer 和 lookup\_order 的描述高度重合，模型就会一直调错。修复方式不是加 Few-shot，也不是写路由分类器，而是**重写精细的描述**（包含输入要求、边界、以及“何时不要用这个工具”）。
+- **Structured Errors（结构化错误）：** 利用 MCP 的 isError 标志。要严格区分四类错误：Transient（超时，可重试）、Validation（参数错误，修复后重试）、Business（违背业务规则，不可重试）、Permission（权限不足）。注意：查询数据库返回“空列表”是正常结果，不是错误，**不要重试**。
+- **MCP Servers 作用域：** .mcp.json 放在项目根目录（进版本控制，团队共享）；~/.claude.json 是个人用户级（不共享）。优先使用社区现成的 MCP 协议集成（Jira, GitHub），仅在有特殊私有业务逻辑时才开发 Custom Server。
+
+**沉浸式学习 Prompt（复制发给 Claude）：**
+
+```markdown
+You are an expert instructor teaching Domain 2 (Tool Design & MCP Integration) of the Claude Certified Architect certification. Teach like a senior architect.
+
+Cover the following 5 tasks sequentially:
+1. Tool Interface Design (Descriptions as the primary routing mechanism, splitting tools)
+2. Structured Error Responses (MCP \`isError\` flag, 4 error categories, Valid empty vs Access failure)
+3. Tool Distribution and \`tool_choice\` (\`auto\`, \`any\`, forced selection, avoiding tool overload)
+4. MCP Server Integration (Project \`.mcp.json\` vs User \`~/.claude.json\`, env vars)
+5. Built-in Tools (Grep vs Glob, Edit vs Read/Write fallback)
+
+For each task: Explain with a production example, highlight anti-patterns, ask check questions. Conclude with a 7-question practice exam.
+```
+
+完整版本的Prompt太长，贴到了Gist。
+
+[https://gist.github.com/tvytlx/2e0c4c823e56e1ddcce8f0634d1f36e6#file-domain2-md](https://gist.github.com/tvytlx/2e0c4c823e56e1ddcce8f0634d1f36e6#file-domain1-md)
+
+## Domain 3: Claude Code Configuration & Workflows (配置与工作流) - 20%
+
+区分业余玩家和专业架构师的分水岭，在于能否为整个研发团队配置自动化的 Claude Code 标准。
+
+**核心认知与避坑指南：**
+
+- **CLAUDE.md Hierarchy（配置层级）：** 最常见的坑：新同事拉取代码后 Claude 表现不一致。原因往往是有人把指令写在了个人级的 ~/.claude/CLAUDE.md 中，而不是项目根目录的 .claude/CLAUDE.md 里。
+- **Path-specific Rules（特定路径规则）：** 使用带有 YAML 前置（frontmatter）的 .claude/rules/ 文件（例如设定 paths: \["\*\*/\*.test.tsx"\]），这比每个文件夹塞一个配置文件优雅得多，能实现全局正则匹配加载。
+- **Plan mode vs. Direct execution：** \* **Plan mode（计划模式）：** 适用于跨多文件重构、架构设计。先让模型 Explore，再执行。 **Direct execution（直接执行）：** 适用于单点 Bug 修复、单文件逻辑更改。
+- **CI/CD 集成：** 在自动化流水线中，务必使用 -p (print mode) 标志运行 Claude Code，否则 CI/CD 任务会因为等待终端交互而无限挂起。
+
+**沉浸式学习 Prompt（复制发给 Claude）：**
+
+```markdown
+You are an expert instructor teaching Domain 3 (Claude Code Configuration & Workflows) of the Claude Certified Architect certification. Teach like a senior architect.
+
+Cover the following 6 tasks sequentially:
+1. CLAUDE.md Hierarchy (User vs Project vs Directory level)
+2. Custom Slash Commands and Skills (\`.claude/skills/\`, \`context: fork\` frontmatter)
+3. Path-Specific Rules (YAML glob patterns for global enforcement)
+4. Plan Mode vs Direct Execution (When to use which, Explore subagent)
+5. Iterative Refinement (Concrete I/O examples vs prose, test-driven)
+6. CI/CD Integration (The \`-p\` flag for non-interactive pipelines, structured output)
+
+For each task: Explain with a production example, highlight anti-patterns, ask check questions. Conclude with an 8-question practice exam.
+```
+
+完整版本的Prompt太长，贴到了Gist。
+
+[https://gist.github.com/tvytlx/2e0c4c823e56e1ddcce8f0634d1f36e6#file-domain3-md](https://gist.github.com/tvytlx/2e0c4c823e56e1ddcce8f0634d1f36e6#file-domain1-md)
+
+## Domain 4: Prompt Engineering & Structured Output (提示词工程与结构化输出) - 20%
+
+在这里，“Be explicit（明确）”胜过一切。不要对模型说“请保守一点”，要用代码示例告诉它什么是错的。
+
+**核心认知与避坑指南：**
+
+- **Explicit Criteria（明确标准）：** 永远用分类和示例代替模糊形容词。不要说“只报告高置信度问题”，要说“仅当实际代码行为与注释矛盾时标记，跳过风格偏好”，并附上具体代码示例。
+- **Few-Shot 提示词：** 解决模型判断不稳定的最高杠杆技巧。提供 2-4 个包含了**思考推理过程 (Reasoning)** 的 Edge cases（边缘情况）示例。
+- **Structured Output with tool\_use：** 强制 JSON Schema 输出。为了防止模型在源数据缺失时“幻觉”捏造数据，必须在 Schema 中合理设计 Nullable (可空) 字段、unclear 枚举值以及 other 字段。
+- **Message Batches API（批处理）：** 异步、便宜 50%、最多 24 小时处理完毕、不支持多轮 tool\_use。将每日夜间审计、批量报表走 Batch API；将 PR 合并前的阻塞性代码审查走同步 API。
+
+**沉浸式学习 Prompt（复制发给 Claude）：**
+
+```markdown
+You are an expert instructor teaching Domain 4 (Prompt Engineering & Structured Output) of the Claude Certified Architect certification. Teach like a senior architect.
+
+Cover the following 6 tasks sequentially:
+1. Explicit Criteria (Concrete code examples vs vague prose)
+2. Few-Shot Prompting (Including reasoning in examples to prevent hallucination)
+3. Structured Output with \`tool_use\` (Nullable fields to prevent fabrication, enums)
+4. Validation-Retry Loops (When to feedback errors vs when information is genuinely absent)
+5. Batch Processing API (Cost savings, constraints, synchronous vs asynchronous architectural choices)
+6. Multi-Instance Review (Avoiding self-review bias in the same session context)
+
+For each task: Explain with a production example, highlight anti-patterns, ask check questions. Conclude with an 8-question practice exam.
+```
+
+完整版本的Prompt太长，贴到了Gist。
+
+[https://gist.github.com/tvytlx/2e0c4c823e56e1ddcce8f0634d1f36e6#file-domain4-md](https://gist.github.com/tvytlx/2e0c4c823e56e1ddcce8f0634d1f36e6#file-domain1-md)
+
+## Domain 5: Context Management & Reliability (上下文管理与可靠性) - 15%
+
+权重虽小，但在生产环境中，上下文一旦崩溃，前面设计得再好的架构都会崩盘。
+
+**核心认知与避坑指南：**
+
+- **Context Preservation（上下文保留）：** 不要对包含关键金额、单号、日期的交易数据使用渐进式总结（Progressive summarization）。应该提取一个持久化的 **"Case Facts"（案件要素）** 代码块，将其原文包含在后续每一次提示词中。
+- **Escalation Triggers（升级人工触发器）：** 不要用所谓的“模型置信度得分”或情感分析去判断是否转人工。只有三种可靠的触发器：客户明确要求转人工（立即执行）、遇到策略空白、模型无法取得实质性进展。
+- **Error Propagation（错误传递）：** 遇到 API 错误或执行失败，绝对不要默默吞掉错误或者直接 Kill 掉整个工作流。正确的做法是：传递结构化错误上下文（包括：失败类型、尝试的查询参数、已获得的部分结果），让 Coordinator 决定备选方案。
+- **Context Compaction（上下文压缩）：** 在长时间的代码探索会话中，使用 /compact 命令清理冗余的中间探索输出，只保留关键发现（Scratchpad），防止触发 "Lost in the middle"（中间遗忘）效应。
+
+**沉浸式学习 Prompt（复制发给 Claude）：**
+
+```markdown
+You are an expert instructor teaching Domain 5 (Context Management & Reliability) of the Claude Certified Architect certification. Teach like a senior architect.
+
+Cover the following 6 tasks sequentially:
+1. Context Preservation (Persistent "case facts" block vs progressive summarization, "Lost in the middle" effect)
+2. Escalation and Ambiguity Resolution (The 3 valid triggers vs unreliable sentiment analysis)
+3. Error Propagation (Structured error context, never silently suppressing)
+4. Codebase Exploration (Scratchpad files, \`/compact\`, state manifest)
+5. Human Review and Confidence Calibration (Field-level confidence, stratified sampling)
+6. Information Provenance (Preserving claim-source mappings during synthesis)
+
+For each task: Explain with a production example, highlight anti-patterns, ask check questions. Conclude with a 6-question practice exam.
+```
+
+完整版本的Prompt太长，贴到了Gist。
+
+[https://gist.github.com/tvytlx/2e0c4c823e56e1ddcce8f0634d1f36e6#file-domain5-md](https://gist.github.com/tvytlx/2e0c4c823e56e1ddcce8f0634d1f36e6#file-domain1-md)
+
+## 练习作业
+
+🔨 领域 1 实操练手项目
+
+**目标：掌握代理循环与拦截机制**
+
+- **作业内容：** 构建一个包含 3-4 个 MCP 工具的多工具 Agent。
+- **核心要求：** 1. 在代码中正确处理 stop\_reason（而不是靠自然语言判断是否结束）。 2. 编写一个 PostToolUse hook（钩子）来统一规范化不同工具返回的数据格式。 3. 编写一个工具调用拦截 hook，用于直接阻断违反业务策略的工具调用。
+- **价值：** 搞定这一个练习，领域 1 的核心概念你就掌握了一大半。
+
+🔨 领域 2 实操练手项目
+
+**目标：体验工具描述对路由的致命影响**
+
+- **作业内容：** 编写两个功能故意设计得高度相似的 MCP 工具。
+- **核心要求：** 故意写非常模糊的工具描述（Tool descriptions），让模型产生路由混乱（Misrouting）。 观察并记录模型调错工具的现象。 修复这些描述，明确边界和输入输出限制。
+- **价值：** 亲身体验（Experience the difference）清晰的描述如何奇迹般地修复 Agent 的智商。
+
+🔨 领域 3 实操练手项目
+
+**目标：跑通 Claude Code 的企业级配置流**
+
+- **作业内容：** 初始化一个真实的代码项目
+- **核心要求：** 建立 CLAUDE.md 层级（同时包含项目级 Project-level 和 目录级 Directory-level）。 在 .claude/rules/ 中使用 glob 模式（如 \*\*/\*.py 或 \*\*/\*.test.tsx）编写全局规则。 编写一个使用 context: fork 的自定义技能（Skill）。 在 .mcp.json 中配置一个带有环境变量扩展（Env var expansion）的 MCP Server。
+- **测试方法：** 用计划模式（Plan mode）跑一个跨多文件的重构任务；用直接执行（Direct execution）跑一个单文件的 Bug 修复。
+
+🔨 领域 4 实操练手项目
+
+**目标：实现抗幻觉的结构化数据提取**
+
+- **作业内容：** 构建一个文档数据提取流水线（Extraction pipeline）。
+- **核心要求：** 使用带有 tool\_use 的 JSON Schema，其中必须包含：必填字段（Required）、可选字段（Optional）和**可空字段（Nullable）**（用来防止模型瞎编数据）。 在代码里加上验证-重试循环（Validation-retry loop），当格式不对时把错误抛回给模型让它自我修正。 调用 Message Batches API 处理一批文档，并通过 custom\_id 追踪和处理那些失败的任务。
+
+🔨 领域 5 实操练手项目
+
+**目标：压力测试错误传递与上下文溯源**
+
+- **作业内容：** 构建一个带有两个子代理（Subagents）的协调器（Coordinator）。
+- **核心要求：** 实现一个持久化的“案件核心事实（Case facts）”代码块，并确保它在多轮对话中不被总结掉。 故意模拟一次 API 超时错误。验证协调器能否收到**结构化的错误上下文**，并带着已获得的部分结果继续往下走，而不是直接崩溃。 故意给模型喂入两个相互冲突的数据源，验证它在最终的 Synthesis（综合报告）中能否正确保留两者的归属溯源（Attribution），而不是自行武断地只选一个。
+
+如有帮助请转发给你的朋友。
+
+本文主要参考以下文章，并结合本人多年软件行业经验校正（原文里有一个假的Anthropic认证的官网，别被误导）。
+
+[https://x.com/hooeem/status/2033198345045336559](https://x.com/hooeem/status/2033198345045336559)
\ No newline at end of file
diff --git a/Claude/Everything You Need to Know About Claude Cowork - A Complete Course in One Article.md b/Claude/Everything You Need to Know About Claude Cowork - A Complete Course in One Article.md
new file mode 100644
index 0000000..3d95dd2
--- /dev/null
+++ b/Claude/Everything You Need to Know About Claude Cowork - A Complete Course in One Article.md
@@ -0,0 +1,292 @@
+---
+title: "Everything You Need to Know About Claude Cowork - A Complete Course in One Article"
+source: "https://x.com/heynavtoor/status/2032518611328446763"
+author:
+ - "[[Unknown]]"
+published: 2026-03-13
+created: 2026-03-14
+description: "ai research engineer • designing agent runtimes, memory & retrieval systems for autonomous agents"
+tags:
+ - "clippings"
+---
+I have combined every resource, every official document, every power-user technique, and every failure mode I have found to create a full course on Claude Cowork. In under 30 minutes you will have a working AI system that handles tasks on your computer without you watching.
+
+After reading this article you will understand Cowork better than 99% of the people who have it installed. Most of them opened it once, organized a folder, and never came back. You are going to build something that runs while you sleep.
+
+Here is how to use this article:
+
+**Module 1: Foundations (what Cowork actually is and how to set it up correctly)**
+
+**Module 2: The Context Architecture (the setup that makes everything else work)**
+
+**Module 3: Skills and Plugins (teaching Cowork how YOU work)**
+
+**Module 4: Connectors and Automation (building the system that runs itself)**
+
+**Module 5: Failure Modes and Fixes (diagnosing and fixing every common problem)**
+
+In every module I will give you the concept, the manual setup, and a copy-paste prompt that does it for you. You will understand what is happening AND have the shortcut to build it without technical skills.
+
+Let us get straight into it.
+
+# Module 1: Foundations
+
+## Chat vs Cowork vs Code: Three modes, three different jobs
+
+Before you build anything, you need to understand where Cowork fits inside Claude. Three modes, three different purposes:
+
+Chat is your conversation partner. You ask questions, you brainstorm, you draft text back and forth. It lives in a chat window. Nothing it produces touches your files. Think of it as a consultant sitting across the table from you.
+
+Cowork is your autonomous operator. You describe an outcome, give it access to a folder, and it plans, executes, and delivers finished work directly to your file system. Word documents. Spreadsheets with working formulas. Organized folders. Slide decks. It runs inside a sandboxed Linux virtual machine on your computer so it cannot access anything you have not explicitly shared. Think of it as an employee who sits at your desk and does the work.
+
+Code is your developer tool. It runs in a terminal, reads codebases, executes commands, and builds software. If you do not write code, ignore this mode entirely. Chat and Cowork handle everything non-developers need.
+
+**The simple rule: Chat for questions. Cowork for work. Code for code.**
+
+## What you need
+
+The Claude Desktop app (download from [claude.com/download](https://claude.com/download)). Available on Mac and Windows. A paid Claude plan: Pro at 20 dollars per month gets you full Cowork access. An internet connection that stays on while Cowork is working. That is the complete list.
+
+## Your first task (do this right now)
+
+Open Claude Desktop. Click the Cowork tab at the top. Click "Work in a Folder" at the bottom. Select a test folder with some non-sensitive files in it. Do not start with your real Documents folder. Create a folder called Cowork-Test and put some old files in it.
+
+Now type this:
+
+**"Organize all files in this folder by content type. Create subfolders that make sense based on what the files actually contain, not just their extensions. Rename each file with a YYYY-MM-DD date prefix where you can identify the date. Create a file called ORGANIZATION-LOG.md that documents every decision you made and why."**
+
+Watch what happens. Claude reads your files, creates a plan, shows you the plan, and then executes it. A screenshot of a receipt does not go into Images. It goes into Receipts. A PDF called Document-3.pdf gets identified as a tax form and filed under Financial. The log documents every decision.
+
+This is not a chatbot suggesting what you could do. This is an agent that did it. Open the folder and look.
+
+Module 1 complete. You have Cowork running and you have completed your first real task. In Module 2, you are going to set up the architecture that makes every future task dramatically better.
+
+# Module 2: The Context Architecture
+
+Here is what most people miss and why most people give up on Cowork after a week.
+
+Cowork has no memory between sessions. Every conversation starts fresh. It does not know who you are, what you do, or how you like things done. So it produces generic output. You get frustrated. You assume the tool is overhyped.
+
+**The fix is not better prompts. It is better setup. About 30 minutes of setup that changes everything permanently.**
+
+## The three context files
+
+Inside your working folder, create a subfolder called Context. Place three files in it:
+
+about-me.md: Who you are professionally. Your role, your company, your industry, your audience. Two to three paragraphs. Not a resume. A briefing for a new colleague on their first day.
+
+voice-and-style.md: How your work should sound. Vocabulary preferences, sentence length, formatting rules, words you never use. Include two to three paragraphs of your actual writing as reference samples. This is the difference between AI that sounds like you and AI that sounds like a robot.
+
+working-rules.md: How you want Claude to behave. Always ask clarifying questions before executing complex tasks. Show a plan before making changes. Flag uncertainty instead of guessing. Never delete files without explicit permission.
+
+Do not trust yourself to write these well enough? This prompt builds them for you:
+
+## PROMPT: THE CONTEXT FILE BUILDER
+
+**"You are a Context File Specialist for Claude Cowork. Your job is to interview me and build three context files that will make every Cowork session produce output tailored to my exact situation and standards.**
+
+**Interview me in three phases:**
+
+**PHASE 1 - ABOUT ME: Ask me about my role, my company, my industry, and my audience. Keep asking until you have enough detail for a two-paragraph briefing. Then write about-me.md.**
+
+**PHASE 2 - VOICE AND STYLE: Ask me to paste two to three samples of my best writing. Analyze my sentence length, vocabulary, tone, and patterns. Ask what words or phrases I never want to see. Then write voice-and-style.md.**
+
+**PHASE 3 - WORKING RULES: Ask me how I want Claude to behave. Should it ask questions before starting? Should it show plans before executing? How should it handle uncertainty? Then write working-rules.md.**
+
+**After all three phases, present the complete files ready to save. Start Phase 1 now."**
+
+## Global Instructions
+
+Context files live in your folders. Global Instructions live in Settings and apply to EVERY session regardless of which folder you are working in.
+
+Go to Settings in Claude Desktop. Find Global Instructions (sometimes called Custom Instructions). Paste your universal preferences: default output format, quality standards, behavioral rules that always apply.
+
+The architecture: Global Instructions set your universal defaults. Folder-specific context files override them for specific projects. The system knows your standards universally and your context locally.
+
+Module 2 complete. You now have context architecture. Every session starts with Claude already understanding who you are and how you work. In Module 3, you are going to teach Cowork your specific workflows.
+
+# Module 3: Skills and Plugins
+
+## When to build a skill
+
+If you have typed the same instructions at the start of more than three conversations, that is a skill begging to be built. A skill is a set of instructions saved in a file that Claude loads automatically when the task matches. You write the instructions once. Claude follows them every time.
+
+## The fastest way to create a skill
+
+Go to Settings. Click Capabilities. Find the Skills section. Click Add. Select "Write skill instructions." Fill in three fields: name (what it is called), description (when Claude should use it), and instructions (exactly how to do the task).
+
+The description is the most important field. Claude uses it to decide whether to load the skill. If it is vague, the skill never fires. If it is too broad, it fires when you do not want it to.
+
+Write it like this: "Draft client update emails matching my brand voice and standard structure. Use whenever the user says draft a client email, write an update for a client, or send a project update. Do NOT use for internal team communications or personal emails."
+
+## PROMPT: THE SKILL BUILDER
+
+**"You are a Skill Building Specialist for Claude Cowork. Your job is to interview me and build a complete skill through conversation.**
+
+**PHASE 1 - THE TASK: Ask me what task I want to automate. If my answer is vague, push back and ask me to describe the exact input and exact output. Keep asking Can you be more specific until the task is concrete.**
+
+**PHASE 2 - THE TRIGGERS: Ask me to give 5 different ways I might phrase this request to Claude. Then suggest 3 to 5 more I probably missed. Ask what similar requests should NOT trigger this skill.**
+
+**PHASE 3 - THE QUALITY STANDARD: Ask me to describe exactly what a perfect output looks like. Then ask what a failed output looks like. Then ask about edge cases: what is the weirdest input this skill might receive?**
+
+**PHASE 4 - BUILD: Using my answers, generate the complete skill with a name, a pushy description with at least 5 trigger phrases and explicit negative boundaries, step-by-step instructions, output format, and at least one example of input and expected output.**
+
+**Present the complete skill and ask me to confirm before saving. Start Phase 1 now."**
+
+## Plugins: Pre-built skill bundles
+
+If building skills from scratch sounds like too much effort, plugins are the shortcut. A plugin bundles skills, slash commands, connectors, and sub-agent configurations into one installable package.
+
+Go to the Customize menu in the left sidebar of Cowork. Click Browse Plugins. Anthropic ships 15+ plugins for different roles: productivity, marketing, finance, legal, HR, engineering, design, operations, data analysis, and more. Click Install on any plugin that matches your work.
+
+After installing, type / to see available slash commands. Each command launches a structured form. Running a workflow like generate report or create dashboard becomes as simple as filling out a brief.
+
+Want to customize a plugin? Click Customize on any installed plugin. Claude walks you through adjusting it to match how you actually work.
+
+Want to build a plugin from scratch? Cowork includes Plugin Create, a built-in plugin that guides you through the entire process.
+
+Module 3 complete. You now have skills that encode your workflows and plugins that extend your capabilities. In Module 4, you connect everything to your external tools and set it all to run automatically.
+
+# Module 4: Connectors and Automation
+
+**Connectors: Giving Cowork access to your world**
+
+Until now, everything Cowork does is contained within folders on your computer. Connectors extend it to your external tools.
+
+Go to Settings, then Connectors, then Browse Connectors. As of March 2026, available connectors include: Google Drive (read your documents, spreadsheets, and slides), Gmail (read and draft emails), Google Calendar (see your schedule), Slack (read messages and channels), DocuSign (access contracts), FactSet (financial data), Notion, GitHub, Jira, Linear, Asana, and more being added monthly.
+
+Click Install on any connector. Authorize with your account. Done. Claude can now access that tool during any task without you mentioning the connector in your prompt.
+
+Start with these two: Google Calendar (low risk, immediately useful for meeting prep) and Gmail (transforms how Claude handles your communications).
+
+Test it immediately:
+
+**"Look at my calendar for this week. Create a document called Weekly-Overview.md that summarizes my meetings, flags any conflicts, and identifies the best two-hour block for deep work."**
+
+## Scheduled tasks: The automation layer
+
+This is where Cowork stops being a tool you use and becomes a system that works for you.
+
+A scheduled task is a prompt that runs automatically at a cadence you define: hourly, daily, weekly, weekdays only, or on demand.
+
+Two ways to create one:
+
+Method 1 (conversational): Open a Cowork task. Type /schedule. Claude walks you through setting it up. This is best when you are starting from a rough idea because Claude helps you shape it.
+
+Method 2 (direct): Click Scheduled in the left sidebar. Click New Task. Write your prompt. Set the cadence. Click Save. This is best when you already have a well-defined prompt.
+
+Here are four scheduled tasks that will change your week:
+
+**MONDAY BRIEFING (weekly, Monday 8 AM):**
+
+"Check my Gmail for important messages from the weekend. Check my Google Calendar for this week's meetings. Pull recent updates from my project folders. Create a file called Weekly-Briefing-\[DATE\].md with: top 5 priority items, this week's meeting schedule with prep notes, and any flagged items that need immediate attention."
+
+**EXPENSE PROCESSOR (weekly, Wednesday noon):**
+
+"Check my Expenses folder for any new receipt images. Process each one: extract vendor, date, amount, and category. Update the running spreadsheet Expenses-2026.xlsx. Mark anything unclear as VERIFY."
+
+**WEEKLY REVIEW (weekly, Friday 4 PM):**
+
+"Summarize what changed in my project folders this week. List completed deliverables. Flag anything that appears stalled. Save to Reports/Week-In-Review-\[DATE\].md."
+
+**DAILY MEETING PREP (daily, 9 AM):**
+
+"Scan my calendar for today's meetings. For any meeting in the next 4 hours, create a one-page prep document with relevant context from my files and recent emails with the attendees."
+
+Important: Your computer must be awake and Claude Desktop must be open for scheduled tasks to run. There is no cloud execution.
+
+Module 4 complete. You now have connectors linking Claude to your tools and scheduled tasks that run your workflows automatically. In Module 5, you learn to diagnose and fix every common problem.
+
+# Module 5: Failure Modes and Fixes
+
+Every Cowork failure falls into one of five categories. Learn to name the category and the fix becomes obvious.
+
+**Failure Mode 1: The Blank Slate**
+
+Symptoms: Output is generic, impersonal, sounds like any AI could have written it. Claude does not seem to know your preferences.
+
+Root cause: No context files. Cowork starts every session from zero.
+
+Fix: Build your three context files (Module 2). Forty minutes of setup, permanent improvement.
+
+**Failure Mode 2: The Wrong Skill**
+
+Symptoms: You ask for one thing and a different skill activates. Or a skill fires on an unrelated request.
+
+Root cause: Skill descriptions overlap. Negative boundaries are missing.
+
+Fix: Add aggressive negative boundaries to every skill description. "Do NOT use for \[list every similar task this skill should ignore\]." Tighten trigger phrases to be unique to each skill.
+
+**Failure Mode 3: The Drifter**
+
+Symptoms: Cowork activates the right skill but the output misses the mark. Formatting is off, tone is wrong, steps are skipped.
+
+Root cause: Instructions are ambiguous. There is more than one way to interpret what you wrote.
+
+Fix: Replace vague language with specific, testable instructions. "Format nicely" becomes "Use H2 headings for each section, bold the first sentence of each paragraph, keep paragraphs under 4 lines." Leave zero room for interpretation.
+
+**Failure Mode 4: The Overachiever**
+
+Symptoms: Cowork produces the requested output but adds commentary, suggestions, extra sections, or creative embellishments you did not ask for.
+
+Root cause: No scope constraints. Without them, Claude defaults to being maximally helpful, which sometimes means doing more than you asked.
+
+Fix: Add explicit scope constraints to your skills and your working-rules.md: "Output ONLY the requested deliverable. Do NOT add explanatory text, commentary, or suggestions unless explicitly asked."
+
+**Failure Mode 5: The Amnesia Loop**
+
+Symptoms: Cowork forgets what it did yesterday. Every session starts from scratch. You keep re-explaining the same project context.
+
+Root cause: No cross-session continuity. Cowork does not maintain memory between tasks.
+
+Fix: Add a shift handover instruction to your working rules: "At the end of every session, write a summary of what you completed and what is still pending to a file called session-log.md. At the start of every session, read session-log.md before beginning any work." Claude reads its own notes from last time and picks up where it left off.
+
+## PROMPT: THE COWORK DIAGNOSTIC
+
+**"My Claude Cowork is not working as expected. I need help diagnosing and fixing the problem.**
+
+**Here is what happened:**
+
+**What I asked Cowork to do: \[DESCRIBE YOUR REQUEST\]**
+
+**What I expected: \[DESCRIBE EXPECTED RESULT\]**
+
+**What actually happened: \[DESCRIBE ACTUAL RESULT\]**
+
+**Diagnose this against the 5 Failure Modes:**
+
+**1\. Blank Slate (generic output, no personalization)**
+
+**2\. Wrong Skill (wrong skill activated)**
+
+**3\. Drifter (right skill, wrong output)**
+
+**4\. Overachiever (added unrequested content)**
+
+**5\. Amnesia Loop (forgot previous session context)**
+
+**For the identified failure mode: explain exactly what caused it, provide the specific fix, and suggest a test prompt to verify the fix works. Start diagnosing now."**
+
+**You now have a personal AI system**
+
+Let us recap what you built:
+
+**Module 1: Cowork installed, first task completed, folder organized.**
+
+**Module 2: Context files and Global Instructions that make every session personalized.**
+
+**Module 3: Skills that encode your workflows. Plugins that extend your capabilities.**
+
+**Module 4: Connectors to your email, calendar, and cloud storage. Scheduled tasks that run automatically.**
+
+**Module 5: Named failure modes with specific fixes for every common problem.**
+
+That is not a collection of features. That is an architecture. Context informs skills. Skills use connectors. Connectors feed scheduled tasks. Scheduled tasks produce work that meets the standards defined in your context files. The loop closes. The system compounds. Every week it gets better because every layer you add improves every other layer.
+
+Most people use Cowork like a chatbot. They type a request, get a result, close the window. They are on the treadmill. You just built infrastructure.
+
+The people who will have the greatest advantage in the coming years are not the ones who use AI the most. They are the ones who designed systems that use AI on their behalf. You just became one of them.
+
+**Now build your second skill. Add your third connector. Schedule your next task. The system reveals its own gaps. You fill them. It improves. That process does not plateau. It accelerates.**
+
+Which task are you automating first? Drop a comment. I read every one.
\ No newline at end of file
diff --git a/Claude/Grep Is Dead How I Made Claude Code Actually Remember Things.md b/Claude/Grep Is Dead How I Made Claude Code Actually Remember Things.md
new file mode 100644
index 0000000..007c902
--- /dev/null
+++ b/Claude/Grep Is Dead How I Made Claude Code Actually Remember Things.md
@@ -0,0 +1,185 @@
+---
+title: "Grep Is Dead: How I Made Claude Code Actually Remember Things"
+source: "https://x.com/ArtemXTech/status/2028330693659332615"
+author:
+ - "[[Unknown]]"
+published: 2026-03-02
+created: 2026-03-02
+description:
+tags:
+ - "clippings"
+---
+**Every conversation with Claude Code starts from zero. Here's how I fixed that with a local search engine and a skill that loads your full context before you type a single word.**
+
+Every conversation with Claude Code starts from zero. I had 700 sessions in 3 weeks and I don't remember what was happening back then. I'm losing control in terms of what's happening.
+
+
+
+One week of my work, visualized. Every node is a session, every connection is a file it touched
+
+I just open a new terminal and then what's going on. I need to somehow find all this context. What was the project, what are the decisions. I need to start from zero all the time.
+
+It gets worse mid-session. When we hit context limit at 60%, we need to compact or hand off. Then half of the decisions have been lost. Or even worse if I want to continue next day, I don't remember what was happening back then.
+
+
+
+Zoomed in: every purple node is a conversation, every colored square is a file it created or modified. Skills, recipes, content drafts - all tangled together.
+
+The current paradigm of grepping over files doesn't scale. So I plugged [QMD](https://github.com/tobi/qmd) into my vault, a search engine by Tobias Lutke [@tobi](https://x.com/@tobi) , CEO of Shopify. Made in Canada.
+
+The whole memory system I'm about to show you - it's a skill. [Install it in 2 minutes](https://memory-artemzhutov.netlify.app/) and Claude Code already knows how to use it.
+
+## QMD: A Local Search Engine for Your Vault
+
+QMD is a local search engine for your knowledge base. It indexes your Obsidian [@obsdmd](https://x.com/@obsdmd) vault and finds anything in under a second.
+
+For each vault folder I have a QMD collection. Notes, daily entries, sessions, transcripts. One-to-one mapping. For each collection I can perform a focused search.
+
+
+
+```text
+qmd collection list
+qmd search "video workflow" -c notes -n 3
+```
+
+That's it. One command, your vault is searchable.
+
+But searchable how? The default way Claude Code [@claudeai](https://x.com/@claudeai) searches is brute force - it sends a Haiku sub-agent to grep through every file. I tried it ([watch](https://youtu.be/RDoTY4_xh0s?t=167)): searched for my notes about different graph approaches the normal way. It took 3 minutes. I was bored, scrolling Twitter while waiting. And the results? 300 files, not great.
+
+QMD search was instant. Better results. Way fewer tokens. You don't have to use sub-agents to do that. The difference: grep matches strings, QMD ranks by relevance.
+
+## Grep vs BM25 vs Semantic
+
+QMD gives you three search modes. **BM25** (qmd search) is deterministic full-text search. Like grep, it matches exact keywords. Unlike grep, it scores each file: how often does the word appear, and how rare is it across all your documents? A short note mentioning "sleep" five times scores higher than a 10,000-word file where "sleep" appears once. No AI, no embeddings - just math. Se**mantic (**qmd vsearch) uses embeddings to find meaning - you can search for a concept even if the exact words aren't there. Hybr**id (qm**d query) combines both.
+
+I searched for "sleep" across my vault. Here's the difference.
+
+
+
+Benchmark: grep returns 200 files of noise in all directions. BM25 finds relevant sleep experiments in 2 seconds. Semantic finds meaning even without exact keywords
+
+**Grep** found 200 files. All over the place. It finds all the files which contain the word "sleep." It even finds sleep() - a programming command that pauses code execution. Nothing to do with actual sleep. That's the problem with string matching.
+
+**BM25** (2 seconds): a reflection about sleep quality. Experiment with tracking sleep fragmentation patterns. Sleep interrupted at 3am. Much better.
+
+But qmd search "insomnia" returns zero results. The word doesn't exist in the vault.
+
+**Semantic search**: I type qmd vsearch "couldn't sleep, bad night" and it found a bedtime discipline goal I set years ago. It goes beyond the keywords and explores the meaning. Four of five results don't contain the search words at all. Grep could never find them.
+
+**Hybrid** (qmd query "couldn't sleep, bad night"): ranks sleep quality improvement at 89%, sleep interrupted at 3am at 51%, health sleep optimization at 42%. The best ranking of all.
+
+
+
+The progression: grep catches strings, BM25 catches relevance, semantic catches meaning
+
+Start with BM25. Fast and handles structured notes. 80% of searches. Add semantic for transcripts and braindumps, where you'd never search for those exact words.
+
+## What semantic search actually surfaces
+
+I searched my daily notes: "find the days when I was happy and what was the reason."
+
+This is a non-trivial query. Claude adapted it and ran multiple searches:
+
+```text
+qmd vsearch "happy, grateful, excited" -c daily -n 5
+qmd vsearch "energy, great day, feeling good" -c daily -n 5
+qmd vsearch "satisfaction, accomplishment" -c daily -n 5
+```
+
+Found semantically relevant connections across months of daily notes.
+
+The pattern: my happiest days are when I ship something and I had good sleep recovery, like sauna or 9 hours of sleep.
+
+Then it surfaced something I had completely forgotten. Back in October when I was writing my PhD thesis and I was about to give up. I needed to do it day by day, come in and write something. But what I realized back then is that I just need to see it through discomfort instead of escaping to quick fixes.
+
+I didn't remember writing that. I didn't expect the search could surface this. But there it was, the exact citation.
+
+## /recall - load context before you start
+
+/recall is a Claude Code skill that sits on top of QMD. It loads context before you start working - instead of explaining to Claude what you were doing, you tell it to recall.
+
+It has three modes: temporal (scan your session history by date), topic (BM25 search across your QMD collections), and graph (interactive visualization of sessions and files).
+
+
+
+Three modes: temporal (yesterday, last week), topic (BM25 across collections), graph (visual exploration)
+
+/recall yesterday
+
+/recall topic graph
+
+/recall graph last week
+
+**Yesterday** reconstructed 39 sessions from one day. Timeline, number of messages in each session, what was done when.
+
+
+
+39 sessions from one day. Each row shows the time, message count, and what I was working on. I can go back to any of them
+
+**Topic** searched for "QMD video" across sessions and notes. Returned the dashboard, production plan, to-do list. All related files surfaced in less than a minute. Compare that to the brute force approach: tell Claude "find all information about this project" and it sends Haiku to grep through your vault for 3 minutes, burns tokens, and comes back with worse results. With /recall topic, I reconstructed the full state of the project and asked: what is the next highest leverage action?
+
+**Graph** opened an interactive visualization of my whole week. Sessions as colored blobs, older ones dimmer, recent ones highlighted in purple. Files clustered by type: goals, research, voice, docs, content, skills.
+
+Here's an example: I was exploring lunch places. I told Claude "I want to have a great lunch" and we analyzed different places to go. I stored those as activities I might want to try. A week later, I open the graph, see that session, copy those file paths into Claude Code and continue from there. The graph makes every past conversation recoverable.
+
+
+
+Activity graph for my week. Sessions branch into files. I can trace any conversation back to what it produced
+
+
+
+Session graph controls: filter by time range, color-coded by file type. Older sessions fade to a dimmer purple, recent ones glow bright. Hover to highlight connections, click to select.
+
+## 700 sessions, all searchable
+
+Claude Code saves all your conversations as JSONL files on your computer. I had 700 sessions in the last 3 weeks. Each one has decisions, questions, context I'll need again.
+
+The raw files have tool uses, system prompts, roles. We parse a clear markdown, the actual signal, the actual user messages, and embed this into the QMD index.
+
+At the end of each session when I close my terminal, I have a hook which exports and embeds those sessions into QMD. So the index is always fresh. I can get back to anything I had even from today.
+
+
+
+Session closes, hook fires, QMD index updates. Always fresh, no manual steps.
+
+## Finding Ideas You Never Acted On
+
+This is the one that surprised me. I searched: "find the ideas that I have never acted on."
+
+Having Claude synthesize the raw QMD results is very useful - it's hard to parse the actual raw results yourself. Claude summarized what it found:
+
+- October 19th - I wanted to build a PhD writing dashboard but never did it
+- I had ideas for illustration-based apps but never followed through
+- I had an idea to record a screen recording about my full Obsidian workflow but never committed
+
+
+
+Claude's synthesis of the hybrid search results. Dates, ideas, things I wrote months ago and completely forgot about.
+
+And it's all local - all of these embeddings live on your computer.
+
+## Tools change. Your context stays.
+
+Your notes stop being passive. They stop being trapped in your Obsidian world. They actually start doing things. All of your notes is useful context about yourself that you can use to achieve goals in your life.
+
+Tools change. A month from now there are going to be new models. So what. If you have your context you can make it work in any situation. Claude Code, Codex, Gemini CLI, anything.
+
+The memory layer works as a skill across your whole stack. I use Obsidian Sync to keep my vault in sync between my Mac and a Mac Mini that's always on. On the Mac Mini, OpenClaw runs 24/7. So I pick up my phone, open OpenClaw, and I have the same vault, same QMD index, same skills - from anywhere.
+
+
+
+The full stack: Obsidian vault at the bottom, QMD search in the middle, Claude Code and OpenClaw at the top. Context flows up
+
+## Next steps
+
+Download the /recall skill, drop it in your .claude/skills/ folder, and you have the session pipeline and recall working today (or ask Claude to do it for you :) ).
+
+**QMD** - the search engine behind all of this. By Tobias Lutke.
+
+[https://github.com/tobi/qmd](https://github.com/tobi/qmd)
+
+**Watch the full video** - 42 min walkthrough with live demos.
+
+[https://youtu.be/RDoTY4\_xh0s](https://youtu.be/RDoTY4_xh0s)
+
+Artem
\ No newline at end of file
diff --git a/Claude/How I Reverse Engineered Claude Code.md b/Claude/How I Reverse Engineered Claude Code.md
new file mode 100644
index 0000000..74a1def
--- /dev/null
+++ b/Claude/How I Reverse Engineered Claude Code.md
@@ -0,0 +1,161 @@
+---
+title: "How I Reverse Engineered Claude Code"
+source: "https://x.com/jaywyawhare/status/2033488305191616875"
+author:
+ - "[[Unknown]]"
+published: 2026-03-16
+created: 2026-03-17
+description:
+tags:
+ - "clippings"
+---
+Caude Code is Anthropic's CLI tool. It ships as a 213 MB binary with no source code. I spent a week taking it apart.
+
+## Why
+
+I'd just wrapped up two projects, [C-ML](https://github.com/jaywyawhare/C-ML) - a machine learning library in C, and [GigaVector](https://github.com/jaywyawhare/GigaVector), a vector database engine. There's always this restless period after finishing something. You need the next thing.
+
+I'd been using Claude Code. It worked well. But it's a closed binary that talks to an API on my behalf, and I didn't know what it was actually doing. That's not a complaint, it's a starting condition. I have a security background and I know how to look inside things. So I did.
+
+The immediate trigger was dumber than that, though. I don't update my packages. I was running v2.1.33 from mid-February while the world had moved on. Instead of updating last weekend, I started pulling the binary apart. In retrospect this was more productive than updating would have been.
+
+## What's inside
+
+You always start with file and readelf.
+
+```bash
+$ file /opt/claude-code/bin/claude
+```
+
+ELF 64-bit LSB executable, x86-64, version 1 (SYSV), dynamically linked
+
+213 MB for a CLI tool is absurd. But strings told the story immediately:
+
+```markdown
+Bun v1.3.5 (Linux x64 baseline)
+```
+
+It's a [Bun](https://bun.sh/) single executable application - the entire JavaScriptCore runtime bundled with the app code into one ELF binary. Most of the 213 MB is runtime. The application itself is small.
+
+This is the best case for reverse engineering. JavaScript, even minified, is much easier to read than compiled code.
+
+## Getting the code out
+
+Bun SEAs have a trailer at the end of the binary:
+
+```bash
+$ strings -t x /opt/claude-code/bin/claude | grep "Bun!"
+```
+
+d4c370a ---- Bun! ----
+
+The trailer points to a table of contents - 15 embedded files. The one that matters is the JavaScript bundle at offset 0x62DA02B.
+
+```bash
+$ dd if=/opt/claude-code/bin/claude bs=1 skip=103700011 count=10357830 of=claude-code.js
+```
+
+9.88 MB of minified JavaScript. 7,493 lines. That's the whole application.
+
+## Reading it
+
+10 MB of minified JavaScript is unpleasant. Every variable is tC or sC1 or HY1. But minification can't rename string literals, and that's the way in.
+
+Search for "You are Claude" and you land in the system prompt. Search for "tengu\_" and you find 597 references to their internal feature flag namespace. These are your anchor points. From there you trace outward.
+
+I used Claude Code to help analyze its own source. You feed it chunks of minified JS and ask what the functions do. It's good at this. There's something funny about a tool reverse engineering itself, but it works.
+
+## The prompt architecture
+
+The system prompt isn't a static string. It's assembled at runtime from 15+ modular sections:
+
+```markdown
+Identity layer → "You are Claude Code..."
+Tone and style → be concise, no emojis, etc.
+Task management → how to track work
+Tool usage policy → which tool when
+Security policy → hardcoded constant, can't be overridden
+Code references → formatting rules
+Dynamic sections → memory, environment, MCP servers
+```
+
+The security policy is hardcoded. Everything else shifts based on the tools available, the model running, and the user's configuration.
+
+There's also a hidden minimal mode: set CLAUDE\_CODE\_SIMPLE=true and the entire prompt collapses to one sentence. Probably exists for testing.
+
+I'm not including the actual prompt text here. I don't know where Anthropic draws the line on this, and I'm not in a position to find out the hard way.
+
+## Tengu
+
+Buried in the code is a feature flag and telemetry system called "Tengu" -after mountain spirits from Japanese folklore. Nobody explains these naming choices. They just happen.
+
+37 feature flags. About 560 telemetry events. Flags are evaluated through GrowthBook on Anthropic's infrastructure, with Statsig as a fallback. Telemetry goes through OpenTelemetry to Datadog plus Anthropic's own analytics endpoint. That's serious instrumentation for a CLI tool.
+
+The interesting part: many flags were gating features that clearly existed in the code but weren't turned on. Extended thinking modes, alternative model routing, experimental UI, cost optimization experiments. The code was there. The switches were off.
+
+## The version diff
+
+By Sunday night I'd mapped enough to have a theory: these gated features aren't dead code. They're upcoming releases, already in the binary, waiting to be enabled.
+
+To test this I grabbed v2.1.76, which Anthropic had just released. Extracted the new bundle (11 MB, larger), diffed it against v2.1.33.
+
+The diff confirmed it. Features behind flags in the old version were live in the new one. New tools added. Prompts restructured. The agent system expanded significantly. Anthropic ships features into the binary weeks before turning them on publicly.
+
+That's the payoff of reverse engineering. Not the extraction - that's mechanical. It's when your model of the system, built from days of reading mangled code, predicts something correctly. That's how you know you understand it.
+
+## Things I didn't expect
+
+The prompt uses 10,000+ tokens before the user says anything. With tool descriptions, CLAUDE.md files, memory, and system reminders, you're at 15-20k tokens of scaffolding per turn. That explains the cost.
+
+The bash sandbox is real. I assumed it would be theater. It's not - there's a genuine allow/deny mechanism with network restrictions.
+
+560 telemetry events is a lot for a CLI tool. They're measuring everything: tool usage patterns, error rates, performance, flag evaluations.
+
+The prompt contains instructions about handling prompt injection, and also instructions about interpreting the tags that contain its own instructions. It's recursive in a way that makes you stop and think.
+
+## Tools
+
+1. file, readelf, strings - Binary identification
+2. dd - Extracting the JS bundle
+3. grep, ripgrep - Pattern search through minified JS
+4. Claude Code - Analyzing its own source
+5. diff, meld - Version comparison
+
+## If you want to try this
+
+- Install Claude Code
+
+```bash
+yay -S claude-code
+```
+
+- Find the binary - which claude
+- Look for the Bun trailer:
+
+```bash
+strings -t x $(which claude) | grep "Bun!"
+```
+
+- Extract the JS bundle from .rodata
+- Start with string searches
+
+There's no encryption, no integrity checking, no anti-tamper. It's minified but not obfuscated. The door is open.
+
+## Prior work
+
+Others have approached this differently:
+
+1. [Kir Shatrov](https://kirshatrov.com/posts/claude-code-internals) intercepted API calls with mitmproxy - good for runtime behavior, misses the prompt architecture.
+2. [Reid Barber](https://reidbarber.com/blog/reverse-engineering-claude-code) found source maps in an early release - clean code, but Anthropic removed them.
+3. [Vrungta](https://vrungta.substack.com/p/claude-code-architecture-reverse) reconstructed architecture from runtime behavior - thorough but speculative.
+4. [Yuyz0112](https://github.com/Yuyz0112/claude-code-reverse) monkey-patched the SDK to log API calls.
+
+Direct binary extraction gives you the most complete picture but the least readable one. Tradeoffs everywhere.
+
+## What it actually is
+
+After a week inside the binary: Claude Code is a prompt delivery system. The code is plumbing. It handles tools, manages context, talks to the API. The product is the prompt - thousands of carefully written tokens that tell the model how to behave.
+
+The binary is packaging. The prompt is the product.
+
+All analysis was performed on locally installed software for educational purposes. No network interception or authentication bypass was involved.
\ No newline at end of file
diff --git a/Claude/How I Use Claude Code To Hit 1 Trending On X.md b/Claude/How I Use Claude Code To Hit 1 Trending On X.md
new file mode 100644
index 0000000..9adf835
--- /dev/null
+++ b/Claude/How I Use Claude Code To Hit 1 Trending On X.md
@@ -0,0 +1,442 @@
+---
+title: "How I Use Claude Code To Hit #1 Trending On X"
+source: "https://x.com/MitcheIl/status/2047336198990098603"
+author:
+ - "[[@MitcheIl]]"
+published: 2026-03-18
+created: 2026-04-24
+description: "I've gotten 100M+ views for tech startups in the last 6 months.Here's the exact system behind every single one.My team told me not to post t..."
+tags:
+ - "clippings"
+---
+
+
+I've gotten 100M+ views for tech startups in the last 6 months.
+
+Here's the exact system behind every single one.
+
+My team told me not to post this...
+
+We've spent the past year creating & perfecting the system behind every viral launch you've seen on X.
+
+We call ourselves the "MrBeast of tech marketing"
+
+Our results the past few weeks (videos linked below):
+
+Moda — 4.3M views. #1 trending.
+
+
+
+Durable — 1.7M views. #1 trending.
+
+
+
+Slash Series C — 1.7M views. #1 trending.
+
+
+
+HockeyStack — 1.5M+ views. #1 trending.
+
+
+
+Hit after hit.
+
+We use the same systems every time.
+
+These aren't just vanity metrics either, they drive real demand, thousands of signups, millions in revenue.
+
+Here's our step-by-step breakdown:
+
+## Step 1: Identify your hidden outcome.
+
+Most founders pitch features.
+
+"AI-powered." "Automated." "10x faster."
+
+Nobody cares.
+
+You'll get lost in a sea of slop.
+
+Here's the only question that matters:
+
+What does a users life look like after using your product?
+
+Durable wasn't an AI website builder.
+
+It was a way to replace your 9-5 income.
+
+One positioning decision.
+
+The difference between 100K views and millions.
+
+> 3月18日
+>
+> Introducing Durable. The first AI business builder that replaces your 9-5 income. RT + comment “Durable” and we'll build your business for FREE.
+
+Here's how to find yours in 10 minutes:
+
+→ Write down every feature your product has
+
+→ After each one ask "so what?" out loud
+
+→ Keep asking until you hit an emotion
+
+→ That emotion is your positioning
+
+→ Now write one sentence describing their life after using it
+
+→ That sentence is your hook
+
+We use Claude Code to run this automatically. The prompt:
+
+```text
+"Here is a list of features for [product]. For each one, ask 'so what?' recursively until you reach an emotional outcome. Return the single strongest emotional positioning statement."
+```
+
+Weak: "AI website builder that work in seconds"
+
+Strong: "Replace your 9-5 income"
+
+Feel the difference?
+
+Most people are dragged down by corporate jargon and trying to stay within brand guidelines.
+
+## Step 2: Engineer your video to score a 100
+
+Every video we write gets scored on two things independently.
+
+→ Invention Novelty: does this feel like something nobody has ever said?
+
+→ Copy Intensity: does reading it make you feel something, not just understand something?
+
+Everything in the video is scored and optimized.
+
+
+
+We use a brain engagement measure, built by Meta, called Tribe, to measure success, before a video is ever released.
+
+We also run a dedicated Weapons Check agent in Claude Code before anything gets filmed.
+
+Every single line in the script gets scored independently on both dimensions.
+
+Both have to hit 10/10.
+
+A novel idea with flat copy fails.
+
+Sharp copy about a boring feature fails.
+
+Lines that don't pass get rewritten automatically.
+
+Lines that are pure filler get cut entirely.
+
+The prompt we use:
+
+```text
+"Score this line on two dimensions: Invention Novelty (does it make the product feel like a genuine breakthrough?) and Copy Intensity (does it make someone feel something, not just understand something?). Both on a 1-10 scale. If either is below 10, rewrite it and explain what was weak."
+```
+
+Here's how we rewrite weak hooks:
+
+Weak: "Introducing our new AI platform"
+
+Strong: "We built the world's first AI that makes your competitors obsolete overnight"
+
+Weak: "Our tool helps you grow faster"
+
+Strong: "The exact system that took 4 startups from zero to 1M+ views"
+
+Weak: "Better customer support"
+
+Strong: "Non-Mumbai 24/7 support that actually solves your problem"
+
+The transformation from weak to strong is always the same.
+
+Replace vague with visceral.
+
+Features with feelings.
+
+Announcements with punches.
+
+## Step 3: Make your biggest haters do your distribution for you.
+
+This is the most counterintuitive thing we've learned from launches, incredibly inverse from any marketing I've done before.
+
+Moda was launching an AI design tool.
+
+To designers.
+
+Who, for the most part, despise AI design tools.
+
+Every other company in that position does the same thing.
+
+They ignore the hate.
+
+They launch with standard product marketing language.
+
+"AI for design"
+
+They hope the demo speaks for itself.
+
+The demo never speaks for itself.
+
+It never does.
+
+We did the opposite.
+
+We owned the criticism before the critics could use it.
+
+"The world's first design agent with taste."
+
+Anti-slop.
+
+By design.
+
+Here's what happened:
+
+Every designer who disagreed quote tweeted it.
+
+Every quote tweet is a signal to X to show your post to more users.
+
+The critics distributed it for us.
+
+> 3月25日
+>
+> We raised $7.5M to kill AI slop. Introducing Moda: the world's first design agent with taste. RT+ comment “Moda” and we’ll design your brand for FREE.
+
+The prompt (must use X API key):
+
+```text
+"Research the top posts on X in [category] sorted by engagement using advanced search Min_Faves:1000. Identify the ones with the highest quote tweet ratio. What is the core criticism or controversy that made people react? Summarize the single most activating nerve in this community."
+```
+
+Here's how to engineer this for your product:
+
+→ Find the most common criticism of your category
+
+→ Acknowledge it directly in your positioning
+
+→ Make your product the solution to that exact criticism
+
+→ Plant one line that a specific community can't help but react to
+
+→ Watch them distribute your content while complaining about it
+
+The SEO community.
+
+The UGC creator community.
+
+The design community.
+
+The finance community.
+
+All easy to activate.
+
+Haters will quote tweet something that makes them defensive.
+
+That engagement feeds the algorithm.
+
+## Step 4: Make the messaging match the message. (Sounds trivial, but it isn't)
+
+HockeyStack raised [$50M](https://x.com/search?q=%2450M&src=cashtag_click).
+
+A sales company.
+
+We asked one question before writing a single word:
+
+What does this product help you do?
+
+HockeyStack helps you break through the noise.
+
+So we found proven viral concepts on Instagram:
+
+
+
+Then replicated it in our launch video:
+
+> 4月15日
+>
+> We raised $50M to build the First AI Revenue Agent. It runs New Business, Expansion, and Prospecting to close you more business while you sleep.
+
+Before we do any of that, our research agent runs 15 keyword searches across YouTube filtered three ways -- all time, last 12 months, last 30 days.
+
+For each keyword it finds the highest performing video.
+
+That's the ceiling.
+
+Then it collects patterns downward until there's a massive drop-off in views.
+
+The titles at the ceiling are the patterns worth stealing.
+
+The prompt (use YouTube API or 1of10(.)com):
+
+```text
+"Find are the top performing videos in [category] across the last 30 days, 12 months, and all time. Identify the structural pattern behind the highest performing titles. What is the core concept or framing that made them work? Now apply that same framing to [product]."
+```
+
+Here's how to find your key message:
+
+→ What does this product actually help you do?
+
+→ Is there a proven viral concept built on that same idea?
+
+→ Can you borrow that concept and let the product speak through it?
+
+I would spend the entire production budget on the right framing.
+
+## Step 5: Create for the algos signals.
+
+This is the part most people get completely wrong.
+
+The X algorithm doesn't measure engagement.
+
+It measures two completely separate things.
+
+
+
+**Signal 1:** Sourcing.
+
+Will your post be shown at all?
+
+Retweets are the primary sourcing signal.
+
+No retweets = post never enters the For You feed. Ever. This is why giveaways that require a retweet work.
+
+**Signal 2:** Ranking.
+
+How high will your post appear?
+
+Replies are the primary ranking signal.
+
+But not just any replies.
+
+Reply chains where the original author responds back.
+
+That single mechanic is the most powerful signal on the platform.
+
+Every reply you post creates a new chain.
+
+And every chain tells the algorithm this is a real conversation worth showing to others.
+
+More chains = higher ranking.
+
+Higher ranking = more people see it.
+
+More people see it = more replies.
+
+That’s the cheat code to gamify millions of views.
+
+You’ll know whether your post is going to live or die in the first 3 hours.
+
+Here's the exact reply schedule we give every client:
+
+→ Post goes live
+
+→ Let it breathe for 30-60 minutes with organic audience only
+
+→ Reply to every single comment immediately
+
+→ Deploy connections and influencers when there's organic velocity
+
+→ Continue replying to every comment for 48 hours
+
+→ Never stop. Not for meetings. Not for sleep. Not for anything.
+
+We use Claude Code to help draft replies in real time during the 48-hour window so the founder never goes silent. The prompt:
+
+```text
+I'm working on a launch [Insert video script, all of my knowledge base about them]. Write 25 potential replies to people who are replying to me about the launch to keep the conversation going, add value, feel like a real human responding to it, and keep each under 280 characters, preferably less than that.
+```
+
+Launches where founders follow this exactly
+
+ALWAYS outperform by 2-3x.
+
+## Step 6: Be strategic, not spammy.
+
+Most companies make the mistake you should never do.
+
+They blast every connection at once.
+
+Or worse, they use influencers and tell them not to disclose they were being paid, by doing so break the law.
+
+Awful.
+
+We make it a mandatory requirement for each influencer to disclose.
+
+If the algorithm sees a sudden artificial spike with no organic foundation.
+
+It reads it as inauthentic.
+
+The post dies within an hour.
+
+Here's the secret key timing sequence:
+
+**Phase 1** \- Minutes 0-60:
+
+Let the post breathe.
+
+Organic followers only.
+
+Algorithm tests with your core audience first.
+
+Reply to every question, thoughtfully.
+
+Monitor engagement velocity.
+
+**Phase 2** \- Hours 1-2:
+
+Deploy 10-20 influencers if organic velocity is strong.
+
+Stagger posts across 30-minute windows.
+
+**Phase 3** \- Real time adjustment:
+
+Watch engagement live.
+
+Add gas when momentum is building.
+
+**Phase 4** - Next 24 hours:
+
+Follow-up content while the algorithm is still hot.
+
+**Day 2**: meme your product.
+
+**Day 3**: deep dive on one specific feature.
+
+**Day 4**: tell your company story.
+
+**Week 2**: speak to the results results and show off testimonials.
+
+Do this and you’ll never have an empty sales calendar again.
+
+## ❗ I will send you our full launch playbook — templates, timing sequences, and briefs we've used for our viral launches. ❗
+
+## Comment "SAUCE" & I'll send you a 12 page manifesto.
+
+None of our launches are luck.
+
+Every single one was part of a viral system we created.
+
+We obsess 7 days a week creating the best video content on X.
+
+Founders spend years building the product.
+
+Thirty minutes thinking about the launch.
+
+The launch is the one thing that either makes your business millions or makes it fall flat on its face.
+
+If you have a launch coming up…
+
+DM me.
+
+My team is killers: Emmy Winners, Ex-MrBeast, Ex-Mark Rober, and the minds behind hundreds of the worlds top content creators.
+
+We write the script, travel to you with a production crew, edit the video, go through hundreds of revisions on each sequence, to get the perfect launch.
+
+And plug you into our [$100M](https://x.com/search?q=%24100M&src=cashtag_click) distribution system with influencers.
+
+Booking out your sales calendar for weeks (if not months).
+
+Pricing starts at six figures.
+
+Get Shown.
\ No newline at end of file
diff --git a/Claude/How To Build One Claude Skill That Replaces Your Entire Marketing Stack In A Weekend.md b/Claude/How To Build One Claude Skill That Replaces Your Entire Marketing Stack In A Weekend.md
new file mode 100644
index 0000000..05a438e
--- /dev/null
+++ b/Claude/How To Build One Claude Skill That Replaces Your Entire Marketing Stack In A Weekend.md
@@ -0,0 +1,226 @@
+---
+title: "How To Build One Claude Skill That Replaces Your Entire Marketing Stack In A Weekend"
+source: "https://x.com/heygurisingh/status/2048062808794014013"
+author:
+ - "[[@heygurisingh]]"
+published: 2026-04-25
+created: 2026-04-26
+description: "I cancelled $158 in monthly SaaS last weekend.Notion AI Business at $20/user. Jasper Pro at $59. Buffer Essentials at $5 per channel across ..."
+tags:
+ - "clippings"
+---
+
+
+I cancelled $158 in monthly SaaS last weekend.
+
+Notion AI Business at $20/user. Jasper Pro at $59. Buffer Essentials at $5 per channel across 5 channels. Hypefury Creator at $59 annual. The math: $158 every month, $1,896 a year, four logins, four billing cycles, four "we've updated our pricing" emails I no longer have to read.
+
+I replaced all four with one Claude Skill. One markdown file. One folder. Built in a weekend.
+
+This isn't a "Claude can do everything" pitch. It's the opposite. Most people building Skills right now are building them wrong, and that's why they don't replace anything. They install 160 community skills, never use 158 of them, and still pay for the SaaS.
+
+Here's the exact framework I used, the working SKILL.md you can fork today, and the one trigger-description trick that decides whether a Skill actually fires when you need it.
+
+## The four tools, the real prices, the real overlap
+
+Let me show you the receipts before the build.
+
+**Notion AI** sits in the Business plan at $20 per user per month on annual billing, $24 month-to-month. Anthropic's pricing page confirms it. The $10 standalone AI add-on was killed in May 2025, so if you want AI Agents and Ask Notion you have to jump from Plus to Business. For a solo founder, that's $240 a year minimum just to get AI inside Notion.
+
+**Jasper Pro** is $59 per month annual, $69 monthly. Their pricing page is unchanged through April 2026. Brand voices, campaigns, templates. $708 a year if you stay on annual.
+
+**Buffer** is $5 per channel per month on annual Essentials, $6 monthly. Connect five channels (X, LinkedIn, Threads, Instagram, Bluesky) and you're at $25 a month. $300 a year. Add a teammate and you jump to the Team plan at $10 per channel.
+
+**Hypefury Creator** is $59 a month, or $590 annual. Engagement builder, autoplugs, evergreen recycling, Tweet-to-Reels. The Starter plan at $29 caps you at one X account and one month of scheduled content, which most active creators burn through in a single batch session.
+
+Add it up: $20 + $59 + $25 + $59 = **$163 a month** on the annual cadence. Call it $158 if you skipped the Hypefury Creator tier and used Starter for the first month.
+
+Now look at what these four tools actually do.
+
+Generate marketing copy in your voice. Schedule that copy across platforms. Recycle the best-performing pieces. Brainstorm hooks. Adapt one piece of content for X, LinkedIn, and a newsletter. Track what worked.
+
+That's it. Four logins, one job: take your ideas and turn them into a consistent, on-brand publishing engine.
+
+A Claude Skill can do every step of that workflow. The scheduling part still needs an MCP server (we'll get to that). But the brain of the stack, the part you're paying $1,896 a year for, is a markdown file.
+
+## Why most Skills don't replace anything
+
+Before the build, the contrarian frame.
+
+Open any "best Claude Skills" article from the last three months and you'll see the same advice: install Corey Haines' marketing skills (6,852 GitHub stars). Install wondelai's strategy skills. Run npx skills add until you have 160 of them.
+
+That advice isn't wrong. Those skills are excellent. But it misses what makes a Skill replace a SaaS subscription instead of just adding another tab to your workflow.
+
+A community Skill is generic by design. It has to be, or it wouldn't help anyone. A Skill that replaces your stack is the opposite: it knows your voice, your offers, your audience, your formatting rules, and your scheduling cadence. It's the brand brief you'd give to a junior marketer, except it lives in a markdown file Claude reads automatically every time you ask for content.
+
+Here's the test. If your SKILL.md could be lifted out of your folder and pasted into a stranger's setup with no edits, it's a tool. If pasting it into a stranger's setup would produce content that sounds like you, it's not really yours.
+
+The leverage isn't in installing more Skills. It's in building one that's so specific to your business that it replaces the four generic tools doing the same job badly.
+
+That's the unlock. Now let's build it.
+
+## The DBS framework, and the one piece everyone gets wrong
+
+Every Skill folder needs a SKILL.md file. That file has three jobs, which the Growbity team calls the DBS framework: Direction, Blueprints, Solutions.
+
+**Direction** is the YAML frontmatter at the top: name and description. This is the only part Claude reads to decide whether to load your Skill into context. If the description is vague, the Skill never fires. If it's overstuffed, it triggers on irrelevant tasks and burns tokens.
+
+**Blueprints** are the workflow steps and rules. What Claude does, in what order, with what constraints.
+
+**Solutions** are the templates and references the Skill loads only when needed. Voice samples. Hook libraries. Banned phrases. Pricing pages.
+
+The piece everyone gets wrong is the description. Here's the rule: write it the way you'd tell a search engine when to load this Skill, not the way you'd describe it on a landing page. "A marketing assistant" doesn't trigger. "Use this skill when the user wants to write social posts, drafts for X, LinkedIn, or threads, or repurpose content across platforms in \[Your Name\]'s voice" triggers correctly every time.
+
+Skills 2.0, which Anthropic shipped in March 2026, added built-in evals. You can now test whether your description triggers the Skill on the right prompts and skips it on the wrong ones. Most people don't run the evals. The ones who do ship Skills that actually fire.
+
+## The actual SKILL.md
+
+Here's the working file. Strip out my placeholders, replace with your own, save as ~/claude/skills/marketing-engine/SKILL.md. That's the entire installation.
+
+```yaml
+---
+name: marketing-engine
+description: Use this skill whenever the user wants to draft, refine, or repurpose marketing content across X, LinkedIn, threads, newsletters, or landing pages in [Your Name]'s voice. Triggers on requests like "write a tweet", "draft a thread", "repurpose this for LinkedIn", "create a hook", "rewrite in my voice", or any content meant for the user's audience. Also handles brainstorming hooks, generating variations, scheduling drafts, and identifying recycle candidates from the evergreen library.
+---
+
+# Marketing Engine
+
+## Identity
+- Name: [Your Name]
+- Audience: [solopreneurs, indie hackers, technical founders]
+- Core thesis: [the one thing your content is always pushing]
+- Current offer: [what you're selling or building toward]
+
+## Voice rules
+- Sentence rhythm: short, declarative, with one long sentence per paragraph for breath
+- Banned: em dashes, "let's dive in", "in today's fast-paced world", "game-changer"
+- Allowed: contractions, occasional profanity for emphasis, single-word paragraphs for impact
+- POV: first-person, opinionated, receipts-first
+
+## Channel formats
+
+### X posts
+- Standalone: 240 characters max
+- Thread hooks: under 260 characters, no question marks unless rhetorical
+- Threads: 8-12 tweets, line breaks inside tweets for rhythm, no closing CTA
+- No hashtags except in research/announcement posts
+
+### LinkedIn
+- Hook in first two lines (above the fold)
+- Single line breaks between paragraphs
+- 1,200-1,800 characters total
+- One specific number or named entity in the hook
+- End with a question only if it's genuinely open
+
+### Newsletter
+- Subject line: under 50 characters, curiosity-driven, lowercase
+- Open with a scene or stat, not "hi friends"
+- Sign off: [your standard signoff]
+
+## Workflow
+
+### When asked to write a single post
+1. Confirm the channel and the angle in one line
+2. Draft three variations with different hooks
+3. Flag which variation is strongest and why
+4. Wait for selection before refining
+
+### When asked to repurpose
+1. Read the source piece completely
+2. Identify the three transferable ideas
+3. Adapt format and length to the target channel
+4. Preserve the strongest specific number or example
+5. Never just copy-paste with line breaks added
+
+### When asked to brainstorm hooks
+1. Generate 10 hooks across these categories: contrarian, data-led, story, framework, callout
+2. Mark which would work as standalone posts vs thread openers
+3. Reject any hook that could have been written by a generic marketer
+
+## References
+- Voice samples: load \`references/voice-samples.md\` for tone calibration
+- Hook library: load \`references/hook-library.md\` when brainstorming
+- Banned phrases: load \`references/banned-phrases.md\` before any draft
+- Evergreen pieces: load \`references/evergreen.md\` when recycling candidates are needed
+
+## Scheduling
+- When the user asks to schedule, defer to the configured Twitter/X MCP server
+- Never invent posting times; ask if not provided
+- Default cadence: 2 posts per day on X, 1 LinkedIn per weekday, 1 newsletter per week
+```
+
+That's the whole file. Around 60 lines. Replace the bracketed placeholders with your actual identity and rules and you have a working Skill.
+
+The references folder is where the magic lives. Each reference file is loaded only when its workflow step needs it, which keeps your token cost low.
+
+## Mapping the SaaS replacement, line by line
+
+Here's how the Skill above replaces each tool.
+
+**Notion AI** writes drafts inside your Notion workspace. The Skill writes drafts inside any Claude session. The output gets pasted wherever you actually publish. You lose the in-Notion convenience. You gain a voice that doesn't drift between drafts because the same SKILL.md governs every output.
+
+**Jasper Pro** is brand voice plus campaign templates. The voice rules and channel formats sections of the Skill are your brand voice. The hook library reference is your campaign template library. Jasper does this through a UI. The Skill does this through a markdown file you own and version with git.
+
+**Buffer** is scheduling. This is the one piece a Skill alone can't do. You need a Twitter/X MCP server to push posts. The OpenTweet MCP server is open-source on npm and runs in two minutes of setup. Free tier covers solo creator volumes. You talk to Claude. Claude calls the MCP. Posts go out. Buffer's $25-a-month value lives in a free MCP plus your existing Claude subscription.
+
+**Hypefury** is engagement automation, autoplugs, and evergreen recycling. The Skill handles evergreen recycling through the references/evergreen.md file: list your top 30 posts, ask Claude weekly to suggest which to recycle and how to refresh them. Engagement automation and autoplugs are the harder replacements. If those are core to your growth, Hypefury Starter at $29 still earns its keep. For most solo creators who write threads in batches, you can drop it.
+
+Net replacement: $158 a month becomes a markdown file plus a free MCP server. The Claude Pro subscription you're already paying $20 for handles the rest.
+
+## Skills 2.0 features almost nobody is using
+
+When Anthropic shipped Skills 2.0 in March 2026, three features landed that most tutorials still don't mention.
+
+**Built-in evals.** You can write test prompts and expected behaviors directly into your Skill, then run them to see whether your description triggers correctly and your workflow produces the right output. For a marketing Skill, this means you can lock in the voice, then catch any drift the moment it happens. Run evals once a week. They take three minutes.
+
+**A/B testing.** You can ship two versions of a Skill and route requests randomly between them. For a content workflow, this is how you find out whether a tighter voice rule produces better output than a looser one without guessing.
+
+**Trigger optimization.** The platform now suggests changes to your description based on which prompts loaded the Skill and which didn't. If you're writing about marketing all day and the Skill only fires half the time, the trigger optimizer tells you which keywords to add.
+
+These three features are the difference between a Skill that's a fun weekend project and one that runs your business reliably for a year.
+
+## The honest limitations
+
+Three places this breaks. You should know them before you cancel anything.
+
+**Token cost creep.** A poorly written SKILL.md loads everything into every conversation. A 500-line Skill with five 200-line reference files burns tokens fast. The fix is the conditional reference pattern: load reference files only when their workflow step needs them. The marketing-engine Skill above does this, which is why it stays cheap to run.
+
+**The 20% editing tax.** Even with a tight voice profile, roughly one in five drafts needs real editing before it ships. Stormy AI flagged this exact rate in their 2026 social media guide and it matches what I see in practice. You're not getting publish-ready output every time. You're getting first drafts that feel like you wrote them, which is still a massive upgrade over generic AI output.
+
+**Things a Skill genuinely can't do.** It can't watch your competitor's feed. It can't run paid ad campaigns. It can't reply to DMs. It can't track unsubscribes from your newsletter. If those tasks live in the SaaS you're cancelling, keep that one tool and cancel the other three.
+
+Be honest about what you actually use before you cancel anything.
+
+## The bigger pattern: skills are the new dotfiles
+
+Here's what's happening underneath this whole shift, and almost nobody is naming it.
+
+For the last decade, developers have shared dotfiles, those tiny config files that customize their shell, their editor, their git workflow. A good dotfiles repo on GitHub got thousands of stars because it represented years of compounded preferences distilled into something forkable.
+
+Skills are the dotfiles of the AI era. Corey Haines' marketing skills hit 6,852 GitHub stars because they're his entire marketing playbook in forkable form. wondelai's strategy skills hit hundreds of stars on the same logic. The Zovo founder is selling lifetime access to his CLAUDE.md templates for $99 because his configurations are worth that to other solo developers.
+
+What you're building when you build a custom Skill isn't a tool. It's a config file for your own brain that an AI can read and execute. The leverage compounds the same way dotfiles compound: every refinement makes every future task faster.
+
+The SaaS replacement is the visible win. The invisible win is that you now own a portable, version-controlled, forkable configuration of how your business operates. No vendor can take it away. No pricing page update can break it. No "we've updated our terms" email can lock you out.
+
+That's why this matters more than the $158 a month.
+
+## What to build first, and what signal tells you it's working
+
+Don't try to replace your whole stack in one weekend. Build one Skill, test it for two weeks, then expand.
+
+Start with the workflow you do most often. For most solo creators, that's drafting and repurposing content. The marketing-engine Skill above is the right starting point. Fork it, replace the placeholders, run it for ten days.
+
+The signal that tells you it's working: you stop opening the SaaS tool. Not because you decided to. Because Claude got there first and the output was good enough to ship. When that happens consistently for two weeks, you can cancel.
+
+The signal that tells you something's wrong: you're editing every draft heavily, or the Skill keeps producing content that sounds generic. That means your voice rules are too loose or your reference files are too thin. Fix the Skill, not the tool.
+
+Track the one number that matters: how often you reach for the SaaS in week three vs week one. If it's dropped to zero, cancel. If it's still half, the Skill needs another iteration.
+
+Most marketing SaaS isn't expensive because it's powerful. It's expensive because you haven't yet written down how you actually want your content to sound and operate. Once you write that down, in a markdown file Claude can read, the SaaS becomes optional.
+
+The hottest programming language for solopreneurs in 2026 is plain English. The most valuable file on your laptop is the one that tells an AI how you think.
+
+Build that file. Cancel the rest.
+
+Written by Guri Singh. Follow [@heygurisingh](https://x.com/heygurisingh) for more on building a one-person marketing engine with Claude.
\ No newline at end of file
diff --git a/Claude/How To Turn Claude Into A Crypto Trading Beast (10 MCPs).md b/Claude/How To Turn Claude Into A Crypto Trading Beast (10 MCPs).md
new file mode 100644
index 0000000..7ecea1c
--- /dev/null
+++ b/Claude/How To Turn Claude Into A Crypto Trading Beast (10 MCPs).md
@@ -0,0 +1,423 @@
+---
+title: "How To Turn Claude Into A Crypto Trading Beast (10 MCPs)"
+source: "https://x.com/milesdeutscher/status/2046724128355627245"
+author:
+ - "[[@milesdeutscher]]"
+published: 2025-06-12
+created: 2026-04-24
+description: "If you do just one thing to gain an edge in the markets this week, make it this.In under 10 minutes, I will show you how to turn Claude into..."
+tags:
+ - "clippings"
+---
+
+
+If you do just one thing to gain an edge in the markets this week, make it this.
+
+In under 10 minutes, I will show you how to turn Claude into the most powerful crypto research and trading tool you have ever used.
+
+For the past year, I've been using various crypto MCP connections to give me an unfair edge in the financial markets.
+
+These MCPs allow me to track on-chain data, pull live crypto price feeds, get real-time news updates, and even use AI agents to trade.
+
+Most traders have no idea this is possible (and I don't blame them).
+
+Everyone who is using MCPs in their trading is gatekeeping the sauce.
+
+Today, I am giving you the full list, the setup instructions, and the exact prompts worth using with each one.
+
+This is one of the most valuable crypto articles I could give you, and this information is genuinely hard to find - be sure to save this so you don't lose it.
+
+**Contents**
+
+**I: What Even Is an MCP?**
+
+**II: How to Set Up Any Crypto MCP**
+
+**III: My Top 10 Crypto MCPs**
+
+**IV: Putting it All Together (turning Claude into your central research hub)**
+
+**Final Thoughts**
+
+## What Even is an MCP?
+
+If you have been using Claude and wondering how to make it actually useful for crypto research, this is the answer.
+
+MCP stands for Model Context Protocol.
+
+It is an open standard that allows Claude (or any AI tool) to connect directly to external data sources.
+
+It's like a bridge between Claude's brain and an external tool, in real time.
+
+Without MCP, Claude is working from training data with a knowledge cutoff.
+
+I.E., it cannot tell you current prices, nor can it track what whales are doing on-chain right now.
+
+By connecting an MCP, you're essentially giving Claude real-time access to market tools.
+
+
+
+How an MCP Server Works
+
+To gain an edge with MCPs, you don't need to know how they actually work under the hood; just think of them as a bridge between two places (in this case, Claude and a crypto tool).
+
+Let me show you how to set any crypto MCP up in <5 minutes.
+
+## How to Set Up Any Crypto MCP
+
+The setup process is almost identical across all MCP servers on this list.
+
+Learn it once, and you can install any of the ten in under two minutes each.
+
+**What you need before starting:**
+
+- **The Claude desktop app -** download it at [claude.com/download](https://claude.com/download)
+- **Node.js -** download it at .org.
+
+Once you have both of these, there are two methods for installing MCPs:
+
+1. **Connectors**
+
+In Claude → Customize → Connectors, you can search for crypto MCPs
+
+At the time of writing, only one of the MCPs on my personal top 10 list is available to connect through this method. Still, I'm including it anyway, as it's the easiest, and I suspect more companies will make their MCPs available this way soon.
+
+
+
+Claude → Connectors
+
+2\. **Claude Code Prompt Installation**
+
+The second method is through prompting Claude Code. This will be your primary method.
+
+Open your Claude Desktop app, switch to Claude Code, and type:
+
+Claude mcp add
+
+Then, when prompted, paste the URL for the connection you want to install, and Claude Code handles everything else automatically.
+
+For example, a full command would look like this:
+
+claude mcp add coingecko [https://mcp.api.coingecko.com/mcp](https://mcp.api.coingecko.com/mcp) \*example not real\*
+
+
+
+claude mcp add \[paste server\]
+
+In my top 10 list below, I include the link to every MCP, so don't worry about sourcing these servers yourself - just paste the link and prompt Claude to help you install it.
+
+Aside from this server URL command, you'll also need an API key from the platform you're trying to install.
+
+My advice: if you can't find where to get an API key, just prompt Claude Code to source the information on how/where to get your free API key for \[x\] MCP.
+
+**For any troubles during this process, just say:** "I'm having trouble installing \[x\] MCP, help me."
+
+Now that you know how to install connections, let's dive into my top 10 crypto MCP list.
+
+Each mention includes: **a connection link, what the MCP is good for, and example prompts**
+
+**imo, #10, #2, and #1 are complete game-changers**, **and the top three you should install ASAP**
+
+## #10: LunarCrush ( @LunarCrush )
+
+LunarCrush is one of the most powerful social intelligence platforms in crypto.
+
+Installing their MCP allows you to scan social data - what's trending in crypto, social engagement scores, and more.
+
+```text
+Example prompts to use
+
+"What are the top 10 cryptocurrencies by social sentiment right now? Show me the sentiment score and 24-hour change for each."
+
+"Which coins have seen the biggest spike in social engagement in the last 6 hours that price has not yet reacted to?"
+
+"Give me a full social intelligence report on Solana — sentiment trend, top content, engagement metrics, and any signals worth watching."
+```
+
+This MCP is super easy to install, since it's the only MCP on this list you can access through the **"Connectors"** tab in Claude.
+
+## #9: CoinMarketCap ( @CoinMarketCap )
+
+CoinMarketCap is the most widely used crypto data platform in the world.
+
+I primarily use it for basic data like price feeds, market cap rankings, volume, circulating supply, etc.
+
+```text
+Example prompts to use
+
+"Show me the top 20 cryptocurrencies by market cap right now. Include price, 24 hour change, volume, and market dominance for each."
+
+"What are the trending narratives in crypto right now? Which sectors are seeing the most capital inflow this week?"
+
+"Give me a full market overview — total market cap, Bitcoin dominance, Fear and Greed index, and the top three macro events affecting markets today."
+```
+
+**Connection Link:** [https://coinmarketcap.com/api/mcp/](https://coinmarketcap.com/api/mcp/)
+
+## #8: CryptoPanic
+
+CryptoPanic is an excellent MCP connection for news.
+
+It pulls breaking news, regulatory updates, market-moving stories, and more in real-time.
+
+```text
+Example prompts to use
+
+"What are the five most important crypto news stories from the last two hours? For each one tell me which assets are affected and whether the sentiment is bullish or bearish."
+
+"Scan the latest crypto news and flag anything that could be a significant market-moving event in the next 24 hours. Include the story, the affected asset, and your reasoning."
+
+"Give me a news sentiment summary for Bitcoin, Ethereum, and Solana based on everything published in the last 12 hours."
+```
+
+**Connection Link:** [github.com/kukapay/cryptopanic-mcp-server](https://github.com/kukapay/cryptopanic-mcp-server)
+
+## #7: DeFi Portfolio MCP
+
+This is the connection for anyone actively managing positions across DeFi protocols.
+
+It is a self-custodial crypto portfolio and DeFi MCP server that lets you read balances and positions across Aave, Compound, Morpho, Uniswap V3 & more and prepares transactions for approval via WalletConnect.
+
+The keyword is self-custodial. Your private keys never leave your device. Claude reads your positions and prepares transactions, but all approvals happen through WalletConnect - meaning you stay in control of every action.
+
+```text
+Example prompts to use
+
+"Give me a full breakdown of all my DeFi positions across every protocol and chain. Include current value, yield earned, and health factor for any lending positions."
+
+"Which of my current DeFi positions has the lowest yield right now? Compare it against the best available rates on Aave and Compound and tell me if I should rebalance."
+
+"Check my Uniswap V3 positions and tell me which ones are out of range and losing fee income right now."
+```
+
+**Connection Link:** [https://github.com/edkdev/defi-trading-mcp](https://github.com/edkdev/defi-trading-mcp)
+
+Note: this connection requires wallet access - always start with a test wallet
+
+## #6: CoinGecko MCP ( @coingecko )
+
+If you've been following me for a while, you may remember CoinGecko MCP being one of the first MCP servers I ever set up.
+
+> 2025年6月12日
+>
+> CoinGecko just changed crypto research forever with their new AI tool. You can now scan the entire crypto market with a single prompt. What used to take 30 minutes now takes seconds. : My guide to using CoinGecko's new AI plug-in.
+
+It's an excellent connection and probably the most comprehensive crypto data source on this entire list.
+
+Best for real-time price feeds, access to millions of tokens, on-chain data, trending token analysis, and related topics.
+
+```text
+Example prompts to use
+
+"Show me the top 10 trending coins on CoinGecko right now. For each one include the price, 24 hour change, volume, and which chains it is available on."
+
+"Find me tokens with a circulating supply of at least 90% of total supply, a market cap under $500 million, built on Solana or Ethereum, with positive price performance over the last 7 days."
+
+"Give me a full on-chain analysis of the top 5 liquidity pools on Base right now — TVL, volume, fees generated in the last 24 hours, and any unusual activity worth noting."
+```
+
+**Connection Link:** [https://docs.coingecko.com/docs/ai-agent-hub/mcp-server](https://docs.coingecko.com/docs/ai-agent-hub/mcp-server)
+
+## #5: altFINS
+
+This is the server built specifically for traders who want actionable intelligence rather than raw price feeds.
+
+The altFINS MCP server provides 300+ pre-computed data points per coin.
+
+including 150+ technical indicators - RSI, MACD & more.
+
+```text
+Example prompts to use
+
+"Show me crypto coins that just crossed above their SMA200 with increasing volume. Include the coin, current price, and the signal strength."
+
+"Find me mid-cap altcoins that are oversold on RSI but still in an overall uptrend across the daily and weekly timeframes. Rank them by signal strength."
+
+"Give me a complete technical analysis of SOL — current indicators, key support and resistance levels, active chart patterns, and any trading signals worth acting on right now."
+```
+
+**Connection Link:** [https://altfins.com/crypto-market-and-analytical-data-api/documentation/mcp-server/](https://altfins.com/crypto-market-and-analytical-data-api/documentation/mcp-server/)
+
+## #4: GOAT SDK
+
+GOAT SDK turns Claude from a pure research assistant into an executioner.
+
+GOAT is an on-chain agent toolkit that you can use for real swaps, token transfers, balance checks, DeFi interactions, and more.
+
+```text
+Example prompts to use
+
+"Check my wallet balance across all connected chains and give me a full breakdown of my holdings by token and chain."
+
+"What is the current price of ETH and how much would I receive if I swapped 0.1 ETH for USDC right now? Show me the best rate available."
+
+"Scan my wallet for any tokens with less than $10 in value and give me a summary of what I could consolidate to clean up my portfolio."
+```
+
+**Connection Link:** [https://github.com/goat-sdk/goat](https://github.com/goat-sdk/goat)
+
+Note: this connection requires wallet access - always start with a test wallet
+
+## #3: Perplexity ( @perplexity\_ai )
+
+Every other server on this list connects Claude to a specific data source. Perplexity MCP connects Claude to the entire live internet and is my #1 source not only for crypto research but for ALL financial research.
+
+```text
+Example prompts to use
+
+"Search the live web for everything that has happened in crypto in the last 6 hours. Summarise the five most market-moving stories and tell me which assets are affected."
+
+"Run a deep research report on Hyperliquid — protocol overview, recent performance, competitive landscape, risks, and any significant developments from the last 30 days. Cite all sources."
+
+"Search for any regulatory developments affecting crypto in the US, EU, and Asia from the last 7 days. Give me a summary of each and whether the overall regulatory trend is bullish or bearish for the market."
+```
+
+**Connection Link:** [https://docs.perplexity.ai/docs/getting-started/integrations/mcp-server](https://docs.perplexity.ai/docs/getting-started/integrations/mcp-server)
+
+## #2: Dune ( @Dune )
+
+You won't find another crypto tool as powerful as Dune for on-chain analytics.
+
+Best for on-chain deep dives, deep research, and prompting custom dashboards.
+
+```text
+Example prompts to use
+
+"Show me all stablecoin transfers over $1 million across Ethereum and Base in the last 24 hours. Include the wallet addresses, amounts, and timestamps."
+
+"Who are the top 10 wallets accumulating RWA tokens in the last 30 days? Show me wallet address, total value accumulated, and which tokens they are buying."
+
+"Compare Uniswap versus Curve daily swap volume over the last 90 days. Show me the trend and tell me which protocol is gaining or losing market share."
+```
+
+**Connection Link:** [https://docs.dune.com/api-reference/agents/mcp](https://docs.dune.com/api-reference/agents/mcp)
+
+## #1: TradingView
+
+My #1 crypto/finance MCP right now is hands down the new TradingView connection.
+
+Best for: managing alerts, scanning your charts in real-time, deep technical analysis, coding custom indicators, and more.
+
+In case you missed it, here is my full TradingView guide where I walk you through how to set it up, real prompt examples, and more:
+
+> 4月16日
+
+**Connection Link:** [https://github.com/LewisWJackson/tradingview-mcp-jackson](https://github.com/LewisWJackson/tradingview-mcp-jackson)
+
+## Putting It All Together
+
+So, you have a few MCPs installed.
+
+Now, how do you actually use them to gain a market edge?
+
+There are two primary methods I like:
+
+1. **Claude Projects (No Coding Required)**
+
+The fastest way to turn Claude into a central crypto research hub is a single system prompt within a project that activates all your connected servers simultaneously and tells Claude exactly how to use them together.
+
+**Getting Started**
+
+Create a new Claude Project and label it "Crypto Research Hub."
+
+Then, paste these custom instructions, which tell Claude how to proactively use all your new connections:
+
+```text
+You are my personal crypto research analyst and trading intelligence system.
+You have access to the following live data connections and should use them proactively whenever relevant:
+
+LunarCrush — social sentiment and social intelligence data
+CoinMarketCap — market data, rankings, and trending narratives
+CryptoPanic — real-time crypto news and sentiment
+DeFi Portfolio — my live DeFi positions and balances
+CoinGecko — on-chain data, token analytics, and DEX data
+altFINS — technical analysis, trading signals, and screener data
+GOAT SDK — on-chain execution and wallet data
+Perplexity — live web search and deep research
+Dune — on-chain SQL analytics across 100 plus blockchains
+TradingView — live chart data, indicators, and Pine Script
+Every time I ask a market question, pull from the most relevant data sources automatically. Do not wait for me to specify which server to use — use your judgment based on what the question requires.
+
+My trading style: [describe your approach — swing trader, DeFi analyst, crypto researcher, etc.]
+
+Assets I follow: [list your primary watchlist]
+
+My risk tolerance: [conservative, moderate, aggressive]
+
+Format all research outputs with: a one-line summary at the top, the key data points, your analysis, and a clear actionable conclusion.
+
+Never pad responses. Every sentence should earn its place.
+```
+
+Now, anytime you send a prompt that requires Claude to access your MCP connections, it knows exactly how to pull the data and format it.
+
+Feel free to modify these instructions to your liking.
+
+2\. **Vibe-Coded Crypto Research App**
+
+This is a good option for those of you who want a visualization layer.
+
+Using the MCP connections, you can vibe-code a custom app that visualizes all the data in one place.
+
+**Example:**
+
+
+
+Example: Crypto Research MCP Hub
+
+**Mega Prompt:**
+
+```text
+Build me a local crypto research dashboard as a single HTML file called crypto-hub.html.
+
+The dashboard should be dark themed, auto-refresh every 5 minutes, and pull live data from all of my connected MCP servers simultaneously. It should look and feel like a professional trading terminal — clean, dense, and information-rich.
+
+Layout and panels to include:
+Top bar — dashboard title, live status indicator showing all connected MCP servers, and last updated timestamp.
+Metric row — four summary cards across the top showing: Total Market Cap (CoinMarketCap), BTC Dominance (CoinMarketCap), Fear and Greed Index (CoinMarketCap), and My Total DeFi Value (DeFi Portfolio MCP).
+Row 2 — two panels side by side:
+
+Top Coins panel: top 10 coins by market cap with price, 24 hour change, and volume from CoinGecko
+Social Sentiment panel: top trending coins with sentiment scores and social momentum from LunarCrush
+
+Row 3 — three panels side by side:
+
+News Feed: latest 5 crypto news stories with bullish/bearish/neutral sentiment tags from CryptoPanic
+Technical Signals: top 5 trading setups ranked by signal strength from altFINS — include coin, signal type, timeframe, and strength rating
+Whale Activity: latest large on-chain transactions from Dune — wallet address, action, asset, and value
+
+Row 4 — two panels side by side:
+
+My DeFi Positions: live breakdown of all positions across Aave, Uniswap V3, Lido, Compound, and Morpho from DeFi Portfolio MCP — show protocol, position type, current value, and APY
+Research Panel: shows the output of the latest Perplexity deep research query with source citations
+
+Bottom section — a prompt input bar where I can type any question and Claude will automatically pull from all connected servers to answer.
+Show active MCP server pills below the input confirming which servers are live.
+
+Technical requirements:
+Use Chart.js for any charts. Dark background with teal accent color for positive values and red for negative. Each panel should show a small tag in the corner indicating which MCP server it is pulling from. All data should refresh every 5 minutes automatically with a countdown timer visible. If a server returns no data, show a graceful loading state rath
+```
+
+This is an example prompt - feel free to modify your research app to look however you'd like.
+
+Of course, there are many more ways to use these MCPs. You could go as far as to build highly customised apps with even more data integrations - but for the average person, I think the two above methods are best.
+
+## Final Thoughts
+
+So, there you have it - my complete crypto MCP guide.
+
+Hopefully, you found it valuable.
+
+If you did, be sure to follow me [@milesdeutscher](https://x.com/@milesdeutscher) for more articles like this.
+
+If you enjoy crypto x AI content, feel free to subscribe to my brand new crypto x AI newsletter, where I break down how to use AI in your trading.
+
+[https://milesdeutscher.com.au/](https://milesdeutscher.com.au/)
+
+
+
+[https://milesdeutscher.com.au/](https://milesdeutscher.com.au/)
+
+100% free, no spam ever, and unsubscribe anytime
+
+Lastly, if you can, please Like/Repost this article so others can see it.💙
\ No newline at end of file
diff --git a/Claude/How apps don’t get killed by Claude.md b/Claude/How apps don’t get killed by Claude.md
new file mode 100644
index 0000000..7c6fda5
--- /dev/null
+++ b/Claude/How apps don’t get killed by Claude.md
@@ -0,0 +1,62 @@
+---
+title: "How apps don’t get killed by Claude"
+source: "https://x.com/michlimlim/status/2032123546009477307"
+author:
+ - "[[Unknown]]"
+published: 2026-03-12
+created: 2026-03-14
+description:
+tags:
+ - "clippings"
+---
+A founder posted "Claude just killed our startup" last month. 8.4k likes. I build an application ([@tryflint](https://x.com/@tryflint)). Here’s what I’m doing about it.
+
+My assumption: Claude becomes the desktop. And your job is to be indispensable to it.
+
+(Disclaimer: "Claude" here is interchangeable with Claude Code, Claude Cowork, ChatGPT, Codex, or whatever wins. The principle holds.)
+
+What I’m doing:
+
+**1\. Convert your app into CLIs, APIs, and MCP.**
+
+Make it trivial for Claude (and every AI app really) to connect to your product. For Flint (autonomous websites), we started with a create\_page API. Already seeing early signs of aggressive agentic usage. One user created a Slackbot for their reps to create account-based pages. Another is hooking it up with their Reddit commenting Clawdbot. You want Claude reaching for your product like a tool, not replacing it like a feature.
+
+**2\. Build a human control surface for Claude’s intermediate work.**
+
+Claude will produce work that needs human judgment. Give people a tool to intervene, then send the corrected output back to Claude. [@paper](https://x.com/@paper) is winning because it lets designers tweak Claude-generated UI using a visual editor. Verbal feedback on frontend code is lossy. Close that loop and you become load-bearing infrastructure in the workflow.
+
+**3\. Let Claude sign up for your product.**
+
+Make the onboarding instant. Claude and Claude users are trigger-happy. Friction kills. [@Remotion](https://x.com/@Remotion) got me quick because I told Claude to make 12 product demo videos and it finished in minutes. If Remotion needed a sales call, I would've moved on. So would my agent.
+
+**4\. Build proprietary data with autonomous improvement loop.**
+
+This is a real moat. LinkedIn owns professional identity. Pave owns compensation data. To top it off, the products incentivize users to keep feeding them.
+
+For Flint, we're building the best dataset on how landing pages actually perform for your customer. Every page is an experiment. We run continuous A/B tests across thousands of them, and that flywheel tells us exactly what converts, and it also improves the pages automatically. Claude can generate a landing page. It cannot tell you whether it will convert.
+
+What I’m bearish on:
+
+**1\. Building your own workflow/automation layer**
+
+Everyone is shipping "Workflows" or "Automations": basically Zapier inside their product. With Claude Code now offering Scheduled Tasks, the case for configuring a custom agent inside your walled garden keeps collapsing.
+
+**2\. Worrying about your coding agents**
+
+We have agents inside Flint that run campaigns and build landing pages. I stopped worrying about them. Claude and Codex keep getting better automatically. I built ours in OpenCode (which uses both interchangeably) and moved on. This is a treadmill you don't need to run.
+
+**3\. Seat based pricing**
+
+I’m not optimizing hard on seat-based pricing. The world is moving toward usage-based pricing. One "seat" runs 10,000 agent actions a month. Design for that now.
+
+**Make your app irresistible**
+
+Amazon killed merchants selling commodity products. Left alone the ones with brands, proprietary inventory, relationships it couldn't replicate. Apple killed thin wrappers. Left alone the apps with proprietary data, network effects, and workflows too complex to absorb.
+
+Claude will do the same. The graveyard won't be full of Claude-integrated companies. It'll be full of ones that thought being a standalone app was enough.
+
+Every app that survives will survive for the same reason: **Claude can use it, but can't replace it.**
+
+You're not building software. You're building a Claude skill. Make it load-bearing.
+
+(This advice has a 6-month shelf life. Maybe less.)
\ No newline at end of file
diff --git a/Claude/How to Build a Claude Powered Polymarket Trading Bot for $28 a Month.md b/Claude/How to Build a Claude Powered Polymarket Trading Bot for $28 a Month.md
new file mode 100644
index 0000000..f3c181f
--- /dev/null
+++ b/Claude/How to Build a Claude Powered Polymarket Trading Bot for $28 a Month.md
@@ -0,0 +1,116 @@
+---
+title: "How to Build a Claude Powered Polymarket Trading Bot for $28 a Month"
+source: "https://x.com/2xnmore/status/2047238933218521182"
+author:
+ - "[[@2xnmore]]"
+published: 2026-04-23
+created: 2026-04-26
+description: "An Anthropic engineer just turned $200 into $13,679 on Polymarket using a Claude powered trading bot that costs $28 a month to run.This is n..."
+tags:
+ - "clippings"
+---
+
+
+An Anthropic engineer just turned $200 into $13,679 on Polymarket using a Claude powered trading bot that costs $28 a month to run.
+
+This is not a headline. The dashboard is real. The compounding curve is real. And the entire system was built using tools that anyone reading this can access today.
+
+This article gives you the complete breakdown of how it works and the exact steps to build your own version.
+
+**What the Bot Actually Does**
+
+Most people lose money on prediction markets because they trade on opinion. This bot does not have opinions.
+
+It analyses 86 million historical trades on Polymarket, ranks 14,000 wallets by win rate and actual profit over time, identifies the wallets that consistently beat the market across hundreds of trades, monitors what those wallets are entering in real time, and only places a trade when multiple top wallets agree on the same position within a 30 minute window.
+
+It makes a maximum of 10 trades per day. That discipline is not a limitation. It is the entire edge. Overtrading is how most bots and most humans destroy their accounts.
+
+The system ran for 27 days in the video. It closed with a 74% win rate across 259 trades and turned $200 into $13,679. The infrastructure cost for the entire run was under $30.
+
+**Step One: Get the Data**
+
+Go to github. com/warproxxx/poly\_data.
+
+This is a completely public dataset containing every single trade ever made on Polymarket. 86 million entries. Every wallet address. Every timestamp. Every market. Every outcome. It costs nothing to access, and it is the foundation on which the entire system is built.
+
+Download it and store it locally or on a basic cloud server.
+
+**Step Two: Build the Wallet Ranking System**
+
+Open Claude and use this prompt:
+
+"I have a dataset of 86 million Polymarket trades. Help me build a Python script that ranks wallets by win rate, total profit, and consistency over the last 90 days. Filter for wallets with a minimum of 50 trades and an above 60% win rate. Output the top 100 wallets with their stats."
+
+Claude writes the complete script. Run it against the dataset. You now have a ranked list of the 100 smartest, most consistent wallets on Polymarket. These are your signal sources.
+
+**Step Three: Build the Real Time Monitor**
+
+Once you have your top wallet list, you need to watch what they are doing live.
+
+Ask Claude: "Build a script that monitors these top wallets in real time using the Polymarket API and sends me an alert when 2 or more of them enter the same market position within a 30 minute window. Include the market name, current odds, and which wallets triggered the signal."
+
+That alert is your entry signal. You are not copying one wallet. You are waiting for consensus among multiple proven wallets before touching a trade. That filter alone eliminates most bad entries.
+
+**Step Four: Build the Exit Logic**
+
+Knowing when to exit is more important than knowing when to enter.
+
+Ask Claude: "Add an exit rule to the system that automatically closes the position when it hits 15% profit or when the top wallets tracking this market begin reducing their position size, whichever condition is met first."
+
+Exiting before the whales do is what separates this from simple copy trading and what protects profit on winning positions before the crowd reverses.
+
+**Step Five: Build the Dashboard**
+
+The Matrix themed dashboard in the video was built entirely inside Claude Code in approximately 20 minutes.
+
+Ask Claude: "Build me a terminal style trading dashboard with live position tracking, a whale wallet monitor panel, a real time trade log, Claude consensus voting display, and a bankroll compounding curve chart. Use green text on black background in Matrix movie styling. Connect it to my Polymarket positions via API."
+
+Claude builds the entire interface. You connect your API keys and it runs live.
+
+**Step Six: Deploy It**
+
+The complete infrastructure cost is $28 a month.
+
+A basic VPS to run the scripts continuously costs $8 a month. Claude API access costs $20 a month. Polymarket API access is free. The GitHub dataset is free.
+
+That is the entire budget.
+
+**The Honest Part**
+
+The 74% win rate over 27 days is impressive. Whether it holds over 6 months is a completely different question and you should approach it as such.
+
+Polymarket is a prediction market with relatively thin liquidity compared to financial markets. An edge that works when only a few people are using it gets crowded and compressed the moment everyone sees it. Past performance of top wallets does not guarantee they continue performing. Markets change. Information edges get arbitraged away.
+
+Front running whale wallets sounds like a clean strategy until you realise that other bots may be watching the same wallets, slippage increases as more capital chases the same signals, and Polymarket can adjust rules or flag heavy automated activity.
+
+Start with an amount you are fully prepared to lose. Paper trade the system for at least two weeks before putting real money in. Never size positions in a way that a losing streak destroys your account.
+
+This is not financial advice. It is a technical breakdown of what someone built and how you can learn from it.
+
+**What This Actually Means**
+
+Five years ago building a system like this would have required a quantitative developer, a data engineer, a front end developer, and months of work. The cost would have been six figures minimum.
+
+Claude made it possible for one person with $28 a month and a few hours to build the same thing.
+
+That compression of capability is not a Polymarket story. It is not a trading story. It is the defining story of what AI is doing to the gap between people who build and people who watch others build.
+
+The barrier is gone. The tools are available right now. The only thing left is whether you spend the next hour building or scrolling.
+
+**Quick Reference: The Full Prompt Sequence**
+
+Prompt 1 for wallet ranking: "I have a dataset of 86 million Polymarket trades. Help me build a Python script that ranks wallets by win rate, total profit, and consistency over the last 90 days. Filter for wallets with minimum 50 trades and above 60% win rate. Output the top 100 wallets."
+
+Prompt 2 for real time monitoring: "Build a script that monitors these top wallets in real time using the Polymarket API and alerts me when 2 or more of them enter the same position within a 30 minute window."
+
+Prompt 3 for exit logic: "Add an exit rule that closes the position at 15% profit or when whale wallets begin reducing their position, whichever comes first."
+
+Prompt 4 for the dashboard: "Build a terminal style trading dashboard with live position tracking, whale monitor, trade log, consensus display, and bankroll curve. Green on black Matrix styling. Connect via Polymarket API."
+
+Total cost: $28 a month. Total setup time: under 3 hours.
+
+NFA DYOR. Start small. Paper trade first. The edge is real but no edge lasts forever without iteration.
+
+$200 became $13,679 in 27 days. The question is not whether Claude can build this.
+
+The question is what you build with it next.
\ No newline at end of file
diff --git a/Claude/How to Build a JARVIS Inside Obsidian With Claude Code — The Full Setup From Scratch.md b/Claude/How to Build a JARVIS Inside Obsidian With Claude Code — The Full Setup From Scratch.md
new file mode 100644
index 0000000..700c4f0
--- /dev/null
+++ b/Claude/How to Build a JARVIS Inside Obsidian With Claude Code — The Full Setup From Scratch.md
@@ -0,0 +1,222 @@
+---
+title: "How to Build a JARVIS Inside Obsidian With Claude Code — The Full Setup From Scratch"
+source: "https://x.com/cyrilXBT/status/2047246104421388461"
+author:
+ - "[[@cyrilXBT]]"
+published: 2026-04-23
+created: 2026-04-26
+description: "Tony Stark did not build JARVIS in a day.But he also did not have Claude Code.What I am about to show you is the closest thing to a real JAR..."
+tags:
+ - "clippings"
+---
+
+
+Tony Stark did not build JARVIS in a day.
+
+But he also did not have Claude Code.
+
+What I am about to show you is the closest thing to a real JARVIS that exists right now. Not a chatbot you talk to. Not a notes app you type into. A system that reads your thinking, connects your ideas, surfaces what matters, and writes alongside you in real time.
+
+It runs inside Obsidian. It is powered by Claude Code. And once it is set up it gets smarter every single day without you doing anything extra.
+
+This is the full setup. Every step. Every file. Every prompt. Copy it exactly.
+
+## What JARVIS Actually Is
+
+Most people think JARVIS is a voice assistant that answers questions.
+
+That is the surface. The real JARVIS is a system that knows its owner deeply enough to anticipate what they need before they ask for it.
+
+The version you are building today does four things.
+
+It captures everything you think, notice, and observe with zero friction. It connects ideas across your vault automatically, finding relationships you would never find manually. It generates content briefs from those connections so you never start from blank. And it writes in your exact voice from those briefs so the output sounds like you, not like AI.
+
+That is the system. Let's build it.
+
+## What You Need Before You Start
+
+Obsidian — download at obsidian.md. Free for personal use. Your notes are plain markdown files on your machine.
+
+Claude Code — install with:
+
+npm install -g [@anthropic/claude-code](https://x.com/@anthropic/claude-code)
+
+An Anthropic API key — get one at [console.anthropic.com](https://console.anthropic.com/).
+
+Set your API key in the terminal:
+
+export ANTHROPIC\_API\_KEY=your\_key\_here
+
+That is everything. No paid plugins. No subscriptions beyond your Anthropic API usage.
+
+## Step One: Build the Vault Architecture
+
+Open Obsidian. Create a new vault. Name it whatever you want. I name mine JARVIS.
+
+Now create this exact folder structure:
+
+JARVIS/ ├── 00-INBOX/ ├── 01-CAPTURES/ │ ├── observations/ │ ├── reactions/ │ ├── patterns/ │ ├── questions/ │ └── numbers/ ├── 02-CONNECTIONS/ ├── 03-BRIEFS/ ├── 04-PUBLISHED/ └── 05-CLAUDE/ ├── CLAUDE.md ├── skills/ └── context/
+
+This structure is not arbitrary. Every folder has a specific job.
+
+INBOX is where raw captures land with zero organization. The goal at capture time is speed not structure. You sort it later.
+
+CAPTURES is organized by note type not topic. This is the most important architectural decision in the whole system. When you organize by topic a note about AI content strategy and a note about how attention works psychologically never meet. When you organize by type they both land in the patterns folder and Claude Code finds the connection between them automatically.
+
+CONNECTIONS is where synthesized insights live. These are not raw captures. They are new ideas that emerge from the relationship between two or more captured notes. This is where your best content comes from.
+
+BRIEFS is your production queue. Every file in here is a piece of content ready to write. Hook written. Closer written. Source notes linked. The actual writing is the last step, not the first.
+
+PUBLISHED archives everything you have shipped with performance data attached. This is how JARVIS learns what works over time.
+
+CLAUDE is the intelligence layer. This is where Claude Code lives when it is working inside your vault.
+
+## Step Two: Install the Essential Plugins
+
+In Obsidian go to Settings, Community Plugins, turn off Safe Mode, then Browse.
+
+Install these four:
+
+Templater — runs dynamic templates with real logic inside them. You will use this for automated capture templates.
+
+Dataview — queries your vault like a database. Surfaces notes by tag, date, link count, or any property you define.
+
+QuickAdd — captures a new note with one keyboard shortcut. Zero friction is non-negotiable at capture time.
+
+Obsidian Git — backs up your vault to GitHub automatically every hour. Set it and forget it.
+
+## Step Three: Build the CLAUDE.md File
+
+This is the most important file in your entire system.
+
+The CLAUDE.md file tells Claude Code everything it needs to know about who you are, what your vault contains, how it is organized, and exactly what you want it to do when it works inside your vault.
+
+Without a CLAUDE.md Claude Code treats your vault like any random folder. With a strong CLAUDE.md it treats your vault like a system it knows deeply and has been managing for months.
+
+Create this file at 05-CLAUDE/CLAUDE.md:
+
+\# JARVIS SYSTEM — CLAUDE.md ## Identity My name is \[YOUR NAME\]. I am \[WHAT YOU DO\]. My audience is \[WHO FOLLOWS YOU\]. My content pillars are \[LIST YOUR 2-3 MAIN TOPICS\]. ## This Vault This is my second brain and content production system. Every note is raw material for content, thinking, or decision-making. Nothing here is decoration. ## Vault Structure - 00-INBOX: unprocessed captures — always check here first - 01-CAPTURES/observations: things I noticed, raw and unpolished - 01-CAPTURES/reactions: my honest gut response to something I read or heard - 01-CAPTURES/patterns: the same principle appearing in two different domains - 01-CAPTURES/questions: things I genuinely do not know the answer to - 01-CAPTURES/numbers: real data points with specific numbers attached - 02-CONNECTIONS: synthesized insights from two or more captured notes - 03-BRIEFS: content ready to write, structured with hook and closer - 04-PUBLISHED: archived content with impression and bookmark data - 05-CLAUDE: your working directory ## My Voice \[DESCRIBE YOUR WRITING STYLE IN SPECIFIC TERMS\] Example: Short punchy sentences. Hard stops. Every idea gets its own line. No dashes. No AI-sounding language. Real numbers always beat vague claims. ## Hard Rules - Never read, access, or modify any .env files - Never modify files in 04-PUBLISHED without explicit instruction - Never create folders outside the established structure - Never use \[WORDS YOU HATE\] in any content you write for me ## Your Primary Jobs 1. Process INBOX — read raw captures, sharpen them, file them correctly 2. Run connection sessions — find non-obvious relationships between recent captures 3. Generate briefs — turn strong connections into structured content briefs 4. Write content — produce finished posts in my exact voice from approved briefs 5. Log performance — update PUBLISHED notes when I provide data
+
+The more specific you make this file the better Claude Code performs inside your vault. Vague CLAUDE.md files produce vague outputs. Treat this as the most important document you write today.
+
+## Step Four: Connect Claude Code to Your Vault
+
+Open your terminal. Navigate to your Obsidian vault:
+
+cd ~/path/to/your/JARVIS-vault
+
+Launch Claude Code:
+
+claude
+
+Claude Code will automatically find your CLAUDE.md file and load everything in it as permanent context. Every conversation you have inside this directory is now informed by your entire vault structure.
+
+Test the connection immediately:
+
+Read my CLAUDE.md and tell me in one paragraph what you understand about this vault and what your role is inside it.
+
+If the summary is accurate and specific your connection is working. If it is vague your CLAUDE.md needs more detail.
+
+## Step Five: Build the Core Skills
+
+A skill in Claude Code is a reusable workflow stored as a markdown file. You call it by name and Claude Code executes the full process every time.
+
+You are going to build four skills. These four cover 90% of everything JARVIS does.
+
+**Skill One: Process Inbox**
+
+Create 05-CLAUDE/skills/process-inbox.md:
+
+\# Skill: Process Inbox ## Trigger phrases "process my inbox" / "run morning capture" / "clear the inbox" ## Process 1. Read every note in 00-INBOX/ 2. For each note: a. Determine which CAPTURES subfolder it belongs to b. Sharpen the raw note into one punchy sentence c. Add exactly three tags — no more, no fewer d. Move the sharpened note to the correct subfolder 3. After processing all notes, provide: - Total notes processed and where each went - Any patterns noticed across today's captures - One connection worth exploring from today's batch ## Quality bar A sharpened note should be specific enough that a stranger would understand exactly what was observed without any additional context. If it still needs explanation it is not sharp enough. Rewrite it.
+
+**Skill Two: Weekly Connections**
+
+Create 05-CLAUDE/skills/weekly-connections.md:
+
+\# Skill: Weekly Connections ## Trigger phrases "run connection session" / "find this week's connections" / "weekly connections" ## Process 1. Read all notes added to 01-CAPTURES/ in the last 7 days 2. Search for connections across ALL subfolders simultaneously 3. A strong connection is one of these four types: TYPE A: Same underlying principle in two different domains TYPE B: Contradiction between two notes that creates interesting tension TYPE C: Pattern connecting three or more notes into one unnamed insight TYPE D: A question from one note that another note accidentally answers 4. For each strong connection: a. Name the connection type b. Write a one-sentence bridge between the ideas c. Write a potential content hook using this connection d. Create a new note in 02-CONNECTIONS/ linking the source notes ## Quality bar If the connection is obvious it does not qualify. Only surface connections that would genuinely surprise the person who wrote the notes. Minimum: 3 connections. Maximum: 5. Quality over quantity.
+
+**Skill Three: Generate Brief**
+
+Create 05-CLAUDE/skills/generate-brief.md:
+
+\# Skill: Generate Brief ## Trigger phrases "generate a brief for \[topic\]" / "brief this connection" / "make a brief" ## Process Create a content brief with exactly these five fields: ONE THING The single insight this piece is built around. Must be one sentence. If it cannot be one sentence the idea is not ready. Push back if the one thing is fuzzy. PROOF The most specific real example, number, or result that proves the one thing. Vague proof invalidates the brief. Real numbers only. READER TRANSFORMATION What does the reader know or feel at the end that they did not before? If this cannot be stated clearly the piece has no reason to exist. THREE HOOKS (ranked) Different in approach and tone. Rank by how hard they stop the scroll. Hook one should be aggressive. Hook two should be curious. Hook three should be personal. THREE CLOSERS (ranked) Rank by urgency and memorability. The closer is written before the middle. Always. ## Output Save as a new note in 03-BRIEFS/ with today's date and topic as filename. Tag it [#ready](https://x.com/search?q=%23ready&src=hashtag_click)\-to-write
+
+**Skill Four: Write Content**
+
+Create 05-CLAUDE/skills/write-content.md:
+
+\# Skill: Write Content ## Trigger phrases "write the brief for \[topic\]" / "write this brief" / "produce content from brief" ## Process 1. Read the specified brief from 03-BRIEFS/ 2. Read all source notes linked in the brief 3. Write the full piece in the user's exact voice as defined in CLAUDE.md 4. Structure: hook → proof → body → closer 5. Every section must add specific value. No filler. 6. Include at least two copyable code blocks or prompt examples where relevant 7. End with bookmark CTA and follow instruction ## Voice requirements (from CLAUDE.md) Apply every voice instruction precisely. If in doubt, make it shorter and more direct. The output should be indistinguishable from content the user wrote themselves. ## Output Save draft in 03-BRIEFS/ next to the source brief. Tag it [#written](https://x.com/search?q=%23written&src=hashtag_click)
+
+## Step Six: The Daily JARVIS Ritual
+
+This is how you run the system every morning. Total time: twenty minutes.
+
+Open your terminal. Navigate to your vault. Launch Claude Code.
+
+**Minute one to five — capture:**
+
+Before you open anything else, spend five minutes adding raw captures to 00-INBOX. Voice memo transcriptions. Things you noticed during your commute. A number you saw. A reaction you had to something you read last night. Raw. Unpolished. Just get it in.
+
+**Minute six to ten — process:**
+
+Run the process inbox skill.
+
+Read the report. Note what got filed and what patterns Claude Code noticed across today's captures.
+
+**Minute eleven to fifteen — connect:**
+
+Are there any strong connections between today's captures and anything in my vault from the last 14 days?
+
+This is the JARVIS moment. You are asking the system to think across your entire recent history and surface what belongs together.
+
+**Minute sixteen to twenty — brief:**
+
+Generate a brief for the connection about \[whichever one surprised you most\].
+
+Done. You have a content brief ready before you open any social media platform. The rest of the day is execution not ideation.
+
+## Step Seven: The Weekly Connection Session
+
+Every Sunday run this full session:
+
+Run the weekly connections skill on all captures from the last 7 days.
+
+Let Claude Code read the full week of captures across all subfolders simultaneously. The connections it surfaces from a full week of inputs will be stronger than anything you find in the daily session.
+
+Review the connection notes it creates in 02-CONNECTIONS. Pick the two strongest. Run the brief skill on both.
+
+You now have two fully briefed content pieces ready to write before Monday morning.
+
+## Step Eight: Close the Loop With Performance Data
+
+This is the step that makes JARVIS compound over time.
+
+Every time a piece of content performs well, open the corresponding note in 04-PUBLISHED and add the numbers:
+
+impressions: 443,000 bookmarks: 11,678 engagement\_rate: 0.04% top\_comment: \[paste the comment that sparked the most replies\] what\_worked: Netflix format hook + Obsidian + Claude Code topic combination
+
+Once a month run this session:
+
+Read everything in my 04-PUBLISHED folder. Tell me: 1. Which topics drove the most bookmarks per impression 2. Which hook formats outperformed their topic average 3. What three content angles my best performing posts suggest I have not tried yet 4. Which combinations of topic and format I should double down on this month Only give me insights specific to this exact data. No generic advice.
+
+This monthly session is what transforms JARVIS from a productivity tool into a content intelligence system. After six months it will know your audience better than any analytics dashboard because it is reading the content and the data together, not just the numbers.
+
+## What This System Produces
+
+After thirty days you will notice you are never stuck for ideas.
+
+After ninety days you will notice your content is more specific, more connected, and more original than anything you were producing before.
+
+After six months you will have a vault that functions as a genuine second brain. One that has captured hundreds of observations, connected them in ways you never would have found manually, and produced content from those connections that your audience responds to because it reflects genuinely original thinking rather than recycled takes.
+
+That is the real product of this system.
+
+Not the notes. Not the productivity. The compounding intelligence that emerges from consistent capture, consistent connection, and consistent feedback over time.
+
+Most people are starting from zero every day.
+
+JARVIS makes sure you never do.
+
+Bookmark this guide and start the setup today.
+
+Follow [@cyrilXBT](https://x.com/@cyrilXBT) for daily Claude Code builds, AI tools, and the exact systems behind everything I ship.
\ No newline at end of file
diff --git a/Claude/How to Give Claude Perfect Memory (complete guide).md b/Claude/How to Give Claude Perfect Memory (complete guide).md
new file mode 100644
index 0000000..ebc15a3
--- /dev/null
+++ b/Claude/How to Give Claude Perfect Memory (complete guide).md
@@ -0,0 +1,292 @@
+---
+title: "How to Give Claude Perfect Memory (complete guide)"
+source: "https://x.com/aiedge_/status/2046966170868486512"
+author:
+ - "[[@aiedge_]]"
+published: 2026-04-08
+created: 2026-04-26
+description: "I don't care what anyone tells you - by default, Claude's memory is basically useless. It frequently forgets context; you constantly have to..."
+tags:
+ - "clippings"
+---
+
+
+I don't care what anyone tells you - by default, Claude's memory is basically useless.
+
+It frequently forgets context; you constantly have to re-explain yourself, and even after you do, it still often doesn't remember.
+
+Sadly, most people have been living with these flaws for months, without knowing there is a better way.
+
+I use Claude every single day. I literally have more Claude screen time than any other app on my Mac.
+
+Because I use Claude so often, I can't afford to have my most-used work tool randomly forget context and important data.
+
+In need of a Claude that is as sharp as possible, I went down the rabbit hole of looking for every possible memory solution.
+
+Luckily, my research was successful.
+
+I discovered three "layers" of memory systems, and they've made Claude significantly more powerful.
+
+One that is easy and works well enough for 90%+ of Claude users.
+
+Another one that takes ~60 minutes to set up, but changes how Claude operates entirely.
+
+And the last one turns Claude into a self-evolving second brain, trained on all your data.
+
+In today's article, I'm revealing all three of these systems and how I made my Claude way sharper.
+
+Whether you've never touched Claude before or you're a power user, I'm confident that you'll find a system that fits your needs.
+
+## Layer one: Basic Memory (Beginner)
+
+This is where everyone should start - four quick wins that take minutes to set up and immediately improve every conversation you have with Claude.
+
+**1\. Memory Editing Tool**
+
+Go to Settings → Memory right now.
+
+This is the most overlooked page in all of Claude, and most people have never opened it.
+
+What you will find is everything Claude has stored about you (preferences, facts, habits, working styles, etc.) accumulated passively across every conversation you have ever had.
+
+Left unmanaged, your memory quickly fills up with garbage.
+
+**The fix is simple:** read through everything on this page. Delete anything outdated, inaccurate, or irrelevant. Then, manually add the context you actually want Claude to carry permanently.
+
+Stick to the basics here (your role and basic preferences) - we'll dive into building highly specific systems soon.
+
+
+
+Chat Memory
+
+2\. **Project Instructions**
+
+If you use Claude Projects (you should), you need to fill in your Project Instructions field.
+
+
+
+Project Instructions
+
+**My advice:** Create projects for all your most-used workflows, then voice-prompt all your context into a Google Doc and upload it as a PDF for each project.
+
+3\. **Tell Claude Directly**
+
+The simplest memory hack on this list. Mid-conversation, just tell Claude what you want it to remember.
+
+Things like:
+
+```text
+"Remember that I never want [x]"
+"Remember that my role is [x]"
+"Update your memory with [x]"
+"Remember that I prefer responses under 400 words."
+```
+
+Claude will store these in your memory immediately. You can also tell it to forget things: "Forget that I mentioned \[x\]."
+
+4\. **Memory Imports & Exports**
+
+If you have been using ChatGPT (or another LLM) and have built up significant context there, you do not have to start from scratch in Claude; you have two effective options to transfer context:
+
+a) You can tell ChatGPT you are switching platforms and ask it to generate a memory export document: "I'm switching this project to Claude, give me a summary document..."
+
+b) You can use Import/Export in Claude
+
+In Settings → Memory, you can import full data from other LLMs
+
+
+
+Export/Import
+
+These four quick edits will suffice for 90%+ of you, and they make an immediate impact on how Claude responds.
+
+However, the next section is for people who want a real system for taking Claude to the next level.
+
+## Layer two: Context File System (Intermediate)
+
+Layer 1 fixes the basic memory problems.
+
+**Layer 2 builds something more powerful:** a file-based memory architecture that lives on your computer, loads automatically into Cowork and Claude Code.
+
+**The concept is simple:** Instead of prompting Claude for context, you store all of that context in .MD desktop files that Claude has access to.
+
+You can also attach these markdown files to any LLM or AI agent system.
+
+Start a new desktop folder, label it **"Claude Master Folder",** and build these four markdown files within it (Claude can help you do this):
+
+1. **Instructions.** **MD**
+
+This file tells Claude all your rules & instructions:
+
+Example: ## Who you are ## What you do ## Rules ## What good outputs look like **Important to include:** "Update Memory. MD with my preferences over time."
+
+This line is crucial; it's how you get Claude to create a running memory log of your data in the second markdown file.
+
+2\. **Memory. MD**
+
+This is the "brain" of Claude, and it gets continuously updated over time.
+
+Example: ## Preferences ## Corrections ## Patterns ## Decisions
+
+Now, whenever you say something like "stop using em dashes," Claude will go into the memory file and update it.
+
+3\. **Context. MD**
+
+The specific context file for \[x\] project.
+
+Obviously, what's in this markdown file will change depending on your specific project.
+
+You can also just create a general "business context" or "life context" markdown mega file.
+
+4\. **Archive Copies**
+
+This one is purely protective but worth doing.
+
+Claude will update your memory files automatically as you work. Occasionally, it overwrites something incorrectly or makes a change you did not intend.
+
+Without a backup system, that context is gone.
+
+The fix is simple. Once a week, copy your entire master folder with Instructions, Memory, Context, and everything else into a separate archive folder that Claude cannot access, and label it with the date.
+
+If anything breaks or gets overwritten incorrectly, you can restore from the archive.
+
+**This is what the final product should look like:**
+
+
+
+
+
+4 Markdown Files: Final Product
+
+As I mentioned above, you can just get Claude to create this for you.
+
+Just create a new folder called "Claude Master Folder," attach it to a new Cowork chat, and paste this prompt:
+
+```text
+Go into my "Claude Master Folder" in my connected workspace and build these four markdown files inside it:
+
+Instructions.md — includes sections for: Who You Are, What You Do, Rules, What Good Outputs Look Like, and a line telling Claude to update Memory.md with my preferences over time.
+Memory.md — includes sections for: Preferences, Corrections, Patterns, Decisions, and Personal Context. Pre-fill with placeholder examples so I know what to add.
+Context.md — includes sections for: About This Project/Business, Audience, Key People & Collaborators, Active Projects & Priorities, Tools & Stack, and Important Background/History. Use a template format with placeholders I can fill in.
+Archive-Guide.md — a step-by-step guide explaining why to archive, how to do it weekly (duplicate the folder, rename with the date, move it somewhere Claude can't access), what to include, how to restore if something breaks, and where to store the backups.
+```
+
+So you have the system built - now, how do you actually use it?
+
+Anytime you're working in Cowork/Claude Code, you can attach your Master Folder, and Claude will use this as a mini memory database.
+
+It will edit the memory markdown file, leaving you with something you can attach to any LLM, new chat, or AI agent.
+
+You can also manually update the .MD files and create new folders for specific projects within your Master Folder.
+
+This system is a complete game-changer, but what I'm about to show you in **Layer three** takes this system even further.
+
+## Layer three: AI Second Brain (Advanced)
+
+This is the deepest level, and frankly, it is not for everyone.
+
+It does require some initial setup and ongoing maintenance, but for those of you who build it, it truly is the best option for an advanced, detailed memory system with Claude.
+
+There are two options depending on how you work.
+
+The first option is easier, and I included it for those of you who want a "simple" AI second brain.
+
+The second option is more advanced (but better overall), and requires 1-2 hours of dedicated building (don't worry, I'll guide you through it all).
+
+Keep in mind that for your AI second brain memory vault to be effective, you actually have to spend time maintaining it and updating your databases.
+
+**Option 1: Claude x Notion**
+
+Connecting Claude to Notion is the highest-leverage thing you can do in 5 minutes.
+
+Go to Claude → Settings → Connectors, then enable the Notion connector.
+
+
+
+Notion Connector
+
+Once connected, Claude can read your Notion workspace directly inside any chat.
+
+Now all your tasks, CRMs, notes, tables, etc., are accessible and editable for Claude.
+
+I recommend creating a new **"Memory Database"** where you store all your AI preferences, rules, and important AI context.
+
+As you're working with Claude, you can say: "Send this to my Notion Memory Database."
+
+You can then export this Notion data to other LLMs or AI platforms via a CSV file or by using the Notion MCP connector.
+
+This setup is similar to what I covered in Layer Two, except you now have nice visuals with Notion's built-in board views, to-do lists, and more, and you unlock additional functionality.
+
+I personally don't use this setup often (Option two below is just better), but I do occasionally use it to send and store valuable mega prompts I use in Claude:
+
+
+
+Claude x Notion: Final Product
+
+**Option 2: Claude x Obsidian**
+
+Obsidian is a tool that stores everything as plain Markdown files on your computer, making it a solid way to connect with Claude and build a second brain.
+
+**The setup**
+
+1. **Download Obsidian**
+
+Go to obsidian.md and download the app.
+
+Create a new Vault (think of this as a simple desktop folder where Claude Code will store and access your data).
+
+**2\. Select Vault in Claude Cowork/Claude Code**
+
+Open the Claude desktop app and click 'Select Folder.'
+
+Point it at your Obsidian Vault folder. Claude now has direct read and write access to everything inside it.
+
+**3\. Inject mega prompt**
+
+Paste Andrej Karpathy's LLM Knowledge Base system prompt into the chatbox.
+
+This is the instruction set that tells Claude Code how to build, maintain, and evolve your wiki over time.
+
+The prompt is available here:
+
+[gist.github.com/karpathy/442a6bf555914893e9891c11519de94f](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f)
+
+**4\. Feed it your data**
+
+Drop in any existing notes, CSV files, article exports, or Notion exports to start populating your second brain.
+
+Claude then ingests each source, extracts the key information, and integrates it into an evolving memory wiki.
+
+**Final Product**
+
+The final product is an AI second brain knowledge wiki that links ideas, notes, remembers ALL your data, and looks like this:
+
+
+
+Claude x Obsidian: Final Product
+
+**Which one should you choose?**
+
+Notion = fast, simple option
+
+Obsidian = local storage, and you want Claude to have a deep understanding (this is the most advanced memory system I've personally found).
+
+If you're going to build the Claude x Obsidian second brain, I recommend reading this first (more details):
+
+> 4月8日
+
+## Final Thoughts
+
+So, there you have it, my three layers to building memory systems inside Claude that make it way sharper.
+
+Again, layer one is the basic setup that yields immediate results (enough for most people).
+
+Layer two requires some setup, but is extremely valuable.
+
+And lastly, layer three will completely change how you use Claude.
+
+I hope you found this article helpful. I post AI articles 2-3x/week, and all my content is hand-written, based on how I'm actually using AI (no AI slop).
+
+If that's the style of content you like to see, follow me [@aiedge\_](https://x.com/@aiedge_) and more will be on your feed soon!
+
+Lastly, if you can, please Like/Repost this article so others can see it💙
\ No newline at end of file
diff --git a/Claude/How to give Claude Code Superpowers.md b/Claude/How to give Claude Code Superpowers.md
new file mode 100644
index 0000000..6a19ef1
--- /dev/null
+++ b/Claude/How to give Claude Code Superpowers.md
@@ -0,0 +1,194 @@
+---
+title: "How to give Claude Code Superpowers"
+source: "https://x.com/shannholmberg/status/2038636871270424794"
+author:
+ - "[[@shannholmberg]]"
+published: 2026-03-15
+created: 2026-04-26
+description: "After a year of claude code. superpowers is the plugin I wish I had from day one.it increased speed, quality and removed the frustration of ..."
+tags:
+ - "clippings"
+---
+
+
+After a year of claude code. superpowers is the plugin I wish I had from day one.
+
+it increased speed, quality and removed the frustration of going back and forth with claude on a regular basis.
+
+without it, claude just starts building without asking questions &. writing a plan. sometimes it works, but mostly you end up course-correcting for an hour.
+
+superpowers is an open-source workflow framework by jesse vincent ([@obra](https://x.com/@obra)). 125,000+ github stars, gaining 15-20K per week.
+
+> 3月15日
+>
+> claude superpowers is the most underrated plugin for marketers right now 83,000 github stars. trending daily. but almost everyone using it is a developer here´s how it works and how to apply it to marketing
+
+## what superpowers actually does
+
+think about what a good engineer does with a new task. they dont just start coding. they read the codebase, ask questions, weigh trade-offs, write up an approach, then build.
+
+superpowers gives claude that same process. 14+ skills that kick in based on what you're doing: brainstorming, spec-first planning, TDD, subagent execution, systematic debugging.
+
+the skills are mandatory. if one applies, claude must use it. the framework has a list of 12 excuses developers use to skip workflows.
+
+works with cursor, codex, and gemini CLI.
+
+
+
+## THE 7-PHASE PIPELINE
+
+superpowers breaks every task into 7 phases. you don't always hit all 7, some tasks only need the first two or three. but the structure is there when you need it.
+
+
+
+**phase 1: brainstorming**
+
+this is the one I use the most
+
+before any new feature, superpowers forces a brainstorming session. not "ask a few questions and move on." a full 9-step workflow:
+
+\> explore your project context (files, docs, recent commits)
+
+\> ask clarifying questions one at a time (never batched, so you actually think about each answer)
+
+\> propose 2-3 approaches with trade-offs and a recommendation
+
+\> present the design in sections, seek your approval after each
+
+\> write a design doc to your project
+
+\> self-review the spec for gaps, contradictions, ambiguity
+
+\> wait for your explicit approval before moving forward
+
+zero implementation until the design is approved. there´s only one exit from brainstorming: moving to the planning phase.
+
+
+
+**phase 2: planning**
+
+once brainstorming is approved, superpowers generates a structured plan with dependencies, acceptance criteria, exact file paths, and order of operations.
+
+the iron rule here: no placeholders allowed. any of these will fail the plan:
+
+\> "TBD"
+
+\> "TODO"
+
+\> "implement later"
+
+\> "similar to task above"
+
+every single task has to contain complete, copy-pasteable instructions. the assumption is that whoever executes the plan (usually a subagent) has zero familiarity with your codebase.
+
+strict, yes. but it means you see exactly what claude will build before it writes a single line. you read the plan, tweak anything thats off, approve it, and execution follows the spec.
+
+I've started treating these plans like mini PRDs. they're detailed enough that I could hand one to a subagent and they'd know exactly what to build.
+
+
+
+**phase 3: subagent-driven development**
+
+claude dispatches subagents for independent tasks, all running in parallel. each subagent gets a fresh context window with no drift from other tasks. the main claude session never writes code itself. it just orchestrates:
+
+\> dispatch tasks to subagents
+
+\> review what comes back
+
+\> track progress against the plan
+
+
+
+**phase 4: test-driven development**
+
+superpowers enforces what it calls the "iron law": no production code without a failing test first. if code gets written before its test, it gets deleted.
+
+write a failing test, write the simplest code that passes it, refactor. classic TDD loop, but the framework enforces it so you can't skip it when you're in a hurry.
+
+I don't use TDD for everything. for quick prototypes and one-off scripts, I skip this phase. but for anything that needs to be maintained, having tests written before the code means I can refactor later without guessing what's going to break.
+
+
+
+**phase 5: two-stage code review**
+
+two separate reviews per task. first review checks spec compliance: does the code match what was planned? second review checks code quality. both must pass, and they run as separate reviewer agents so the first review doesnt bias the second.
+
+
+
+**phase 6: systematic debugging**
+
+when something breaks, superpowers follows a strict protocol:
+
+\> reproduce the bug
+
+\> isolate the cause
+
+\> form one hypothesis (not three)
+
+\> make the smallest possible change
+
+\> verify
+
+theres a 3-strike rule. if 3 attempted fixes fail, the framework stops trying to patch symptoms and forces you to question the architecture. this prevents the death spiral where claude keeps making small tweaks that don't address the root cause.
+
+
+
+**phase 7: branch completion**
+
+once everything passes, it handles the merge, PR, or cleanup.
+
+## how I use it
+
+My latest example is building our new agency landing page from scratch.
+
+I kicked off a brainstorming session and superpowers started by reading my existing project structure, the design system I had in place, and recent commits. about 5 minutes of back and forth:
+
+\> asked about framework preferences
+
+\> asked about design constraints
+
+\> asked whether I had existing brand assets
+
+\> proposed a component structure, animation plan, deployment config
+
+approved the pla and it dispatched agents: one for the hero section, another for the feature grid, another for deployment. 30 minutes and 5 iterations later, the coming soon page was live
+
+> 3月25日
+>
+> building our new agency LP from scratch. 1. huddled with the team on brand direction. mood boards in Google Flow, type pairings, color palettes. goal was a brand that doesnt scream ai generated but lets us create assets with ai 2. researched frameworks, landed on Astro + Radix
+
+without those brainstorming and planning steps, that build would have been hours of me course-correcting claudes assumptions mid-build. I know because I've done similar builds without a framework and spent most of the time doing exactly that.
+
+the brainstorming session front-loaded every decision that would have been a mid-build argument. by the time agents started writing code, there was nothing left to argue about.
+
+## getting started
+
+install it as a claude code plugin and start your next task normally.
+
+the brainstorming kicks in automatically before any code gets written.
+
+1\. answer the brainstorming questions honestly. you'll catch assumptions you didn't know you were making.
+
+2\. let it generate a plan. read through it carefully. tweak anything thats off and approve.
+
+3\. on a multi-file task, let it dispatch subagents and just watch what happens.
+
+Link to git: [https://github.com/obra/superpowers](https://github.com/obra/superpowers)
+
+## trade-offs
+
+the skills consume context window. on large tasks with lots of existing code, the brainstorming and planning prompts can eat into what claude has available for your actual implementation. I've hit this on a few bigger projects.
+
+for quick one-file fixes, the brainstorming gate is overkill. sometimes you just want to change a color or fix a typo.
+
+it shines on multi-file features and full builds. anything where you'd normally go back and forth with claude for an hour trying to get the architecture right.
+
+## why this is happening now
+
+0 to 125,000 github stars in months. #1 trending on github.
+
+
+
+developers are figuring out that raw prompting has a ceiling. you can write better prompts, add more context, be more specific. but at some point you need structure, not just better input.
+
+follow me [@shannholmberg](https://x.com/@shannholmberg) for more on building with ai tools, vibe coding, and agent systems.
\ No newline at end of file
diff --git a/Claude/I FOUND 1,116 CLAUDE CODE SKILLS FROM 500+ REPOS SO YOU DON'T HAVE TO. ALL LINKS.md b/Claude/I FOUND 1,116 CLAUDE CODE SKILLS FROM 500+ REPOS SO YOU DON'T HAVE TO. ALL LINKS.md
new file mode 100644
index 0000000..b37dd51
--- /dev/null
+++ b/Claude/I FOUND 1,116 CLAUDE CODE SKILLS FROM 500+ REPOS SO YOU DON'T HAVE TO. ALL LINKS.md
@@ -0,0 +1,557 @@
+---
+title: "I FOUND 1,116 CLAUDE CODE SKILLS FROM 500+ REPOS SO YOU DON'T HAVE TO. [ALL LINKS]"
+source: "https://x.com/polydao/status/2047644016632557700"
+author:
+ - "[[@polydao]]"
+published: 2026-04-16
+created: 2026-04-26
+description: "1,116 skills from 500+ repositories, 200+ authorsMost people use Claude Code like a fancy autocompleteThey don't know Skills existI spent th..."
+tags:
+ - "clippings"
+---
+
+
+> 1,116 skills from 500+ repositories, 200+ authors
+
+**Most people use Claude Code like a fancy autocomplete**
+
+They don't know Skills exist
+
+I spent the last month going through 500+ repositories and 200+ authors to map the entire Claude Code Skills ecosystem.
+
+- What I found: 1,116 unique skills across 10 categories - from official Anthropic to Microsoft, Cloudflare, Sentry, and hundreds of community creators
+- This is the most complete map of the Claude Code Skills ecosystem that exists right now
+- Everything is categorized, linked, and ready to install
+
+# Part 1: Official Anthropic Skills
+
+
+
+Repo: [github.com/anthropics/skills](https://github.com/anthropics/skills) (121,000 stars) - 17 skills inside
+
+- **Document Generation:** docx, pdf, pptx, xlsx
+- **Development:** frontend-design, webapp-testing, claude-api
+- **MCP & Meta:** mcp-builder, skill-creator
+- **Enterprise:** doc-coauthoring, brand-guidelines, internal-comms
+- **Creative:** algorithmic-art, canvas-design, theme-factory, web-artifacts-builder, slack-gif-creator
+
+# Part 2: Verified Partner Skills
+
+## Sentry - 30 skills inside
+
+Repo: [github.com/getsentry/sentry-for-ai](https://github.com/getsentry/sentry-for-ai) (2,500+ stars)
+
+- **SDK skills:**
+
+
+
+## Cloudflare - 16+ skills inside
+
+Repo: [github.com/cloudflare/skills](https://github.com/cloudflare/skills) (923 stars) Workers, D1, R2, KV, Durable Objects, Agents SDK, Workflows, Queues, Pages
+
+## Trail of Bits - 29 skills inside
+
+Repo: [github.com/trailofbits/skills](https://github.com/trailofbits/skills) (4,700+ stars) static-analysis, variant-analysis, semgrep-rule-creator, semgrep-rule-variant-creator, insecure-defaults, differential-review, sharp-edges etc
+
+## Microsoft - 133 skills inside
+
+Repo: [github.com/MicrosoftDocs/Agent-Skills](https://github.com/MicrosoftDocs/Agent-Skills) (495 stars)
+
+- **.NET (25):** azure-ai-openai-dotnet, azure-cosmos-dotnet, azure-identity-dotnet, azure-search-dotnet, azure-servicebus-dotnet, m365-agents-dotnet, and 19 more
+- **Java (30):** azure-ai-contentsafety-java, azure-cosmos-java, azure-eventhub-java, azure-identity-java, azure-storage-java, and 25 more
+- **Python (40+):** azure-ai-ml-py, azure-cosmos-py, azure-identity-py, azure-keyvault-py, azure-monitor-py, azure-search-py, fastapi-router-py, pydantic-models-py, and 32 more
+- **Rust (7):** azure-cosmos-rust, azure-eventhub-rust, azure-identity-rust, and 4 more
+- **TypeScript (22):** azure-ai-contentsafety-ts, azure-cosmos-ts, azure-identity-ts, azure-playwright-testing-ts, and 18 more
+- **Core (9):** cloud-solution-architect, copilot-sdk, entra-agent-id, frontend-design-review, github-issue-creator, mcp-builder, podcast-generation, skill-creator, continual-learning
+
+
+
+## Skills From the Tools You Already Use
+
+- **HashiCorp** Terraform: providers, testing, stacks, style guide, import, actions, refactor Repo: [github.com/hashicorp/skills](https://github.com/hashicorp/skills)
+- **Firecrawl** Web scraping, search, onboarding, interactive builds · 3,000+ stars Repo: [github.com/firecrawl/firecrawl-claude-plugin](https://github.com/firecrawl/firecrawl-claude-plugin)
+- **AWS** Serverless, Amplify, databases, migrations, location service · 595 stars Repo: [github.com/awslabs/agent-plugins](https://github.com/awslabs/agent-plugins)
+- **Azure** MCP Server, Foundry MCP Repo: [github.com/microsoft/azure-skills](https://github.com/microsoft/azure-skills)
+- **DigitalOcean** App Platform deployment · 19 stars Repo: [github.com/digitalocean-labs/do-app-platform-skills](https://github.com/digitalocean-labs/do-app-platform-skills)
+- **Pulumi** Миграция: Terraform → Pulumi, CDK, CloudFormation, ARM. Best practices, automation API, ESC · 34 stars Repo: [github.com/pulumi/agent-skills](https://github.com/pulumi/agent-skills)
+- **Neon** Postgres, claimable DB, egress optimizer · 52 stars Repo: [github.com/neondatabase-labs/ai-rules](https://github.com/neondatabase-labs/ai-rules)
+- **Supabase** Postgres best practices Repo: [github.com/supabase/agent-skills](https://github.com/supabase/agent-skills)
+
+
+
+- **PlanetScale** MySQL branching, query performance, index design Repo: [github.com/planetscale/claude-plugin](https://github.com/planetscale/claude-plugin)
+- **ClickHouse** Best practices, chdb, architecture advisor, cloud/local deploy Repo: [github.com/clickhouse/clickhouse](https://github.com/clickhouse/clickhouse)
+- **Remotion** Programmatic video creation with React · 500 stars Repo: [github.com/remotion-dev/skills](https://github.com/remotion-dev/skills)
+- **WordPress** Block dev, themes, plugins, REST API, WP-CLI, performance, PHPStan, Playground Repo: [github.com/WordPress/wordpress](https://github.com/WordPress/wordpress)
+- **OpenAI** Figma (8), Notion (4), security (3), deployment, games, Sora, speech, slides Repo: [github.com/openai](https://github.com/openai)
+- **Figma** Code connect, design system, generate design/library, implement design Repo: [github.com/figma](https://github.com/figma)
+- **Google Gemini** Gemini API, Vertex AI, Live API, Interactions API Repo: [github.com/google-gemini](https://github.com/google-gemini)
+
+
+
+- **Stripe** Best practices, upgrade guide Repo: [github.com/stripe](https://github.com/stripe)
+- **Better Auth** Auth setup, providers, email/password, 2FA, organizations Repo: [github.com/better-auth](https://github.com/better-auth)
+- [fal.ai](https://fal.ai/) Image/video/audio gen, 3D, lip sync, tryon, upscale, realtime, workflow Repo: [github.com/fal-ai-community](https://github.com/fal-ai-community)
+- **Sanity** Best practices, content modeling, SEO, experimentation Repo: [github.com/sanity-io](https://github.com/sanity-io)
+- **Tinybird** Best practices, CLI, Python SDK, TypeScript SDK Repo: [github.com/tinybirdco](https://github.com/tinybirdco)
+- **CallStack (React Native)** Best practices, GitHub integration, upgrading RN Repo: [github.com/callstackincubator](https://github.com/callstackincubator)
+- **Typefully -** social media content for X, LinkedIn, Threads, Bluesky, Mastodon Repo: [github.com/typefully](https://github.com/typefully)
+- **Replicate** - discover and run AI models Repo: [github.com/replicate](https://github.com/replicate)
+- **Composio** - connect to 1,000+ external apps Repo: [github.com/composiohq](https://github.com/composiohq)
+- **Courier -** multi-channel notifications Repo: [github.com/trycourier](https://github.com/trycourier)
+- **VoltAgent** Repo: [github.com/VoltAgent/awesome-agent-skills](https://github.com/VoltAgent/awesome-agent-skills) (16,400 stars) create-voltagent, voltagent-best-practices, voltagent-core-reference, voltagent-docs-bundle
+
+# Part 3: Mega Collections
+
+## obra/superpowers - 20 skills inside
+
+Repo: [github.com/obra/superpowers](https://github.com/obra/superpowers) (166,000 stars)
+
+> /test-driven-development, systematic-debugging, verification-before-completion, brainstorming, writing-plans, executing-plans, dispatching-parallel-agents, requesting-code-review, receiving-code-review, using-git-worktrees, finishing-a-development-branch, subagent-driven-development, writing-skills, using-superpowers, defense-in-depth
+
+
+
+## alirezarezvani/claude-skills - 235 skills inside
+
+Repo: [https://github.com/alirezarezvani/claude-skills](https://github.com/alirezarezvani/claude-skills) (12,000 stars)
+
+- **Engineering (37 core)**
+- **Marketing (44)**
+- **Product (16)**
+- **Regulatory (14)**
+- **C-Level Advisory (34)**
+- **Business (5)**
+- **Finance (4)**
+
+
+
+## wshobson/agents - 150 skills inside
+
+Repo: [github.com/wshobson/agents](https://github.com/wshobson/agents) (33,900 stars)
+
+
+
+## mattpocock/skills - 17 skills inside
+
+Repo: [github.com/mattpocock/skills](https://github.com/mattpocock/skills) (16,500 stars)
+
+> tdd, triage-issue, request-refactor-plan, setup-pre-commit, git-guardrails, to-prd, improve-codebase-architecture, and 10 more
+
+> 4月16日
+>
+> Claude Code will grill you with 40+ questions before writing a single line of code > it's called /grill-me - 3 sentences long. most impactful skill I use instead of jumping straight to code - it walks every branch of your design tree until there's zero ambiguity > every
+
+## NeoLabHQ/context-engineering-kit - 13 plugins inside
+
+Repo: [github.com/NeoLabHQ/context-engineering-kit](https://github.com/NeoLabHQ/context-engineering-kit) (833 stars)
+
+> tdd, sdd, code-review, reflexion, sadd, ddd, kaizen, git, customize-agent, docs, tech-stack, mcp, fpf
+
+## jeffallan/claude-skills - 66 skills inside
+
+Repo: [github.com/Jeffallan/claude-skills](https://github.com/Jeffallan/claude-skills) (8,400 stars)
+
+> devops-engineer, sre-engineer, terraform-engineer, kubernetes-specialist, monitoring-expert, cloud-architect
+
+## hamelsmu/evals-skills - 7 skills inside
+
+Repo: [github.com/hamelsmu/evals-skills](https://github.com/hamelsmu/evals-skills) (995 stars) - LLM evaluation skills for benchmarking
+
+## forrestchang/karpathy-skills - skills inside
+
+Repo: [github.com/forrestchang/andrej-karpathy-skills](https://github.com/forrestchang/andrej-karpathy-skills) (43,650 stars) - Andrej Karpathy coding philosophy for AI-native development
+
+# Part 4: Core Development
+
+## Methodologies
+
+- [learn-claude-code](https://github.com/shareAI-lab/learn-claude-code) by shareAI-lab (53,825 stars) - Claude Code onboarding
+- [tdd](https://github.com/mattpocock/skills) by mattpocock (16,500 stars repo) - TypeScript-first TDD
+- [tdd-guide](https://github.com/alirezarezvani/claude-skills) by alirezarezvani (12,000 stars repo) - TDD methodology
+- [tdd, sdd, kaizen](https://github.com/NeoLabHQ/context-engineering-kit) by NeoLabHQ (833 stars repo) - TDD with anti-patterns, spec-driven dev, continuous improvement
+
+## Code Review & Quality
+
+- [requesting-code-review, receiving-code-review, subagent-driven-development](https://github.com/obra/superpowers) by obra (160,000 stars repo)
+- [code-review-graph](https://github.com/tirth8205/Code-Review-Graph) by tirth8205 (10,353 stars) - Visual code review with dependency mapping
+- [code-review-skill](https://github.com/awesome-skills/code-review-skill) by awesome-skills - Multi-language code review
+- [reflexion](https://github.com/NeoLabHQ/context-engineering-kit) by NeoLabHQ - Self-reflection for code improvement
+
+
+
+## Testing
+
+- [webapp-testing](https://github.com/anthropics/skills) by Anthropic (121,000 stars repo) - Playwright E2E testing
+- [playwright-pro](https://github.com/alirezarezvani/claude-skills) by alirezarezvani - Test generation, flaky fix, migration
+- [playwright-skill](https://github.com/testdino/playwright-skill) by testdino - 70+ guides in 5 packs
+- [cypress-agent-skill](https://github.com/KahlilR23/cypress-agent-skill) by KahlilR23 - 25+ Cypress patterns
+- [evals-skills](https://github.com/hamelsmu/evals-skills) by hamelsmu (995 stars) - 7 LLM evaluation skills
+- [outside-in-tdd](https://github.com/JoeGaebel/outside-in-tdd) by JoeGaebel - Outside-in TDD
+
+## Debugging
+
+- [systematic-debugging, verification-before-completion](https://github.com/obra/superpowers) by obra - 4-phase root cause, evidence verification
+
+## Git & Automation
+
+- [using-git-worktrees, finishing-a-development-branch](https://github.com/obra/superpowers) by obra - Parallel branches, merge guidance
+- [pr-create-fetch](https://github.com/angakh/pr-create-fetch) by angakh - PR creation automation
+- [git-guardrails, setup-pre-commit](https://github.com/mattpocock/skills) by mattpocock
+
+## Architecture & Planning
+
+- [brainstorming, writing-plans, executing-plans](https://github.com/obra/superpowers) by obra - Socratic refinement, implementation strategies
+- [graphify](https://github.com/safishamsi/Graphify) by safishamsi (27,462 stars) - Codebase visualization
+- [monorepo-navigator](https://github.com/alirezarezvani/claude-skills) by alirezarezvani - Turborepo/Nx/pnpm management
+
+## Accessibility
+
+- [accessibility](https://github.com/addyosmani/web-quality) by addyosmani (16,400 stars repo) - WCAG compliance
+
+## AI/ML Engineering
+
+- [rag-architect](https://github.com/alirezarezvani/claude-skills) by alirezarezvani - RAG pipeline builder
+- [self-improving-agent](https://github.com/alirezarezvani/claude-skills) by alirezarezvani - Auto-memory curation
+- [llm-cost-optimizer](https://github.com/alirezarezvani/claude-skills) by alirezarezvani - API cost optimization
+
+## Additional Core Dev
+
+The remaining ~200 skills in this category come from the mega collections above (alirezarezvani engineering tier, obra superpowers, mattpocock, NeoLabHQ, wshobson) and individual repos.
+
+**Key names:** qa-engineer, browser-automation, statistical-analyst, codebase-onboarding, runbook-generator, changelog-generator, llm-wiki, code-tour, demo-video, tech-stack, fpf, customize-agent
+
+
+
+# Part 5: Security & Compliance
+
+## Professional Firms
+
+- [Trail of Bits Skills](https://github.com/trailofbits/skills) by Trail of Bits (4,700 stars) - 29 plugins. See Part 2.
+- [Ghost Security](https://github.com/ghostsecurity/skills) by Ghost Security - 7 skills: SAST, SCA, DAST, secrets, reporting
+- [Phoenix Security](https://github.com/Security-Phoenix-demo/security-skills-claude-code) by Security-Phoenix-demo - CTI research, OpenGrep rules, NotebookLM
+
+## Community Collections
+
+- [Anthropic-Cybersecurity-Skills](https://github.com/mukul975/Anthropic-Cybersecurity-Skills) by mukul975 (4,800 stars) - 754 skills, 38 domains, MITRE/NIST mapped
+- [awesome-claude-skills-security](https://github.com/Eyadkelleh/awesome-claude-skills-security) by Eyadkelleh - 7 plugins for pentesting
+
+## OWASP & Standards
+
+- [www-project-agentic-skills-top-10](https://github.com/OWASP/www-project-agentic-skills-top-10) by OWASP - AI agent security risks
+- [claude-code-owasp](https://github.com/agamm/claude-code-owasp) by agamm (136 stars) - OWASP Top 10:2025, ASVS 5.0, 20+ languages
+
+## Individual Security Skills
+
+- [VibeSec-Skill](https://github.com/BehiSecc/VibeSec-Skill) by BehiSecc (785 stars) - Bug bounty: IDOR, XSS, SSRF, SQLi
+- [clawsec](https://github.com/prompt-security/clawsec) by prompt-security (909 stars) - Drift detection, CVE monitoring
+- [varlock-claude-skill](https://github.com/wrsmith108/varlock-claude-skill) by wrsmith108 (16 stars) - Secure env var management
+- [ffuf\_claude\_skill](https://github.com/jthack/ffuf_claude_skill) by jthack (126 stars) - Web fuzzing
+- [sanitize](https://github.com/agentward-ai/agentward) by agentward-ai (2,900+ stars repo) - PII redaction, 15 categories
+- [security-bluebook-builder](https://github.com/SHADOWPR0/security-bluebook-builder) by SHADOWPR0 - Security documentation
+- [ironclaw-agent-guard](https://github.com/IronClaw/ironclaw-agent-guard) by IronClaw - Prompt injection detection
+
+## Compliance
+
+- [Claude-Skills-Governance-Risk-and-Compliance](https://github.com/Sushegaad/Claude-Skills-Governance-Risk-and-Compliance) by Sushegaad - 12 frameworks: ISO 27001, SOC 2, FedRAMP, GDPR, HIPAA, PCI DSS, DORA
+- [AgentWard](https://github.com/agentward-ai/agentward) by agentward-ai (2,900+ stars) - HIPAA, SOX, GDPR, PCI-DSS. 1,500+ tests
+- [claude-agentic-framework](https://github.com/dralgorhythm/claude-agentic-framework) by dralgorhythm (69 stars) - 67 skills with safety hooks
+- [snyk-fix](https://github.com/snyk/studio-recipes) by snyk - Automated vulnerability remediation
+
+
+
+# Part 6: DevOps & Infrastructure
+
+## Terraform
+
+- [terrashark](https://github.com/LukasNiessen/terrashark) by LukasNiessen (140 stars) - Terraform eliminating LLM hallucinations
+- [terraform-engineer](https://github.com/Jeffallan/claude-skills) by jeffallan - Senior Terraform across AWS, Azure, GCP
+- [terraform-aws](https://github.com/a-pavithraa/aws-serverless-skill) by a-pavithraa - AWS Terraform modules
+- [terraform-opentofu](https://github.com/lgbarn/devops-skills) by lgbarn - Multi-agent plan analysis, drift detection
+
+## Docker
+
+- docker-development, docker - See alirezarezvani/claude-skills and wshobson/agents in Part 3
+
+## Kubernetes
+
+- [kubernetes-skill](https://github.com/LukasNiessen/kubernetes-skill) by LukasNiessen (11 stars) - K8s eliminating deprecated APIs
+- [k8s-skills](https://github.com/foxj77/claude-code-skills) by foxj77 (5 stars) - 20 skills: Flux CD GitOps, Helm, cert-manager, Kyverno
+
+## Cloud (Official)
+
+- [awslabs/agent-plugins](https://github.com/awslabs/agent-plugins) (595 stars) - AWS deploy, serverless, databases
+- [MicrosoftDocs/Agent-Skills](https://github.com/MicrosoftDocs/Agent-Skills) (495 stars) - Azure Functions, AKS, Storage, SQL
+- [cloudflare/skills](https://github.com/cloudflare/skills) (923 stars) - Workers, D1, R2, KV
+- [do-app-platform-skills](https://github.com/digitalocean-labs/do-app-platform-skills) (19 stars) - DigitalOcean
+
+
+
+## Databases
+
+- [neon-ai-rules](https://github.com/neondatabase-labs/ai-rules) by Neon (52 stars) - Serverless Postgres
+- [supabase/agent-skills](https://github.com/supabase/agent-skills) by Supabase - Postgres best practices
+- [planetscale-claude-plugin](https://github.com/planetscale/claude-plugin) by PlanetScale - MySQL branching
+- [clickhouse](https://github.com/clickhouse/clickhouse) by ClickHouse - 6 skills
+
+## CI/CD & IaC
+
+- [pulumi-agent-skills](https://github.com/pulumi/agent-skills) by Pulumi (34 stars) - Migration and authoring
+- [hello-ansible-skills](https://github.com/sigridjineth/hello-ansible-skills) by sigridjineth - 4 Ansible skills
+- GitHub Actions, GitLab CI, Jenkins - See wshobson/agents in Part 3
+
+## Monitoring
+
+- [monitoring-expert, sre-engineer, devops-engineer](https://github.com/Jeffallan/claude-skills) by jeffallan - Prometheus, Grafana, DataDog, SLI/SLO
+- [incident-runbook-templates, cloud-cost-optimization](https://github.com/wshobson/agents) by wshobson - SEV1-SEV4, FinOps
+
+
+
+# Part 7: Product & Project Management
+
+- [pm-skills](https://github.com/phuryn/pm-skills) by phuryn (10,300 stars) - 100+ skills, 8 plugins. /discover, /strategy, /write-prd
+- [Product-Manager-Skills](https://github.com/deanpeters/Product-Manager-Skills) by deanpeters (3,600 stars) - 47 PM skills + 6 workflows
+- product-manager through project-templates - All in alirezarezvani/claude-skills (Part 3). 85 skills total across product, UX, PM, and Atlassian domains
+
+
+
+# Part 8: Marketing & Growth
+
+## SEO & GEO
+
+- [seo-geo-claude-skills](https://github.com/aaron-he-zhu/seo-geo-claude-skills) by aaron-he-zhu (1,100 stars) - 20 skills, 14 MCP integrations
+- [claude-seo](https://github.com/AgriciDaniel/claude-seo) by AgriciDaniel - 19 sub-skills, 12 subagents
+- [aso-skills](https://github.com/Eronred/aso-skills) by Eronred (938 stars) - 25 ASO skills across 6 categories
+
+## Marketing Collections
+
+- [marketingskills](https://github.com/coreyhaines31/marketingskills) by coreyhaines31 - 36 skills across 7 categories
+- [founder-skills](https://github.com/ognjengt/founder-skills) by ognjengt (125 stars) - 20 skills: copywriting, CRO, viral hooks
+- All alirezarezvani marketing skills - See Part 3 (44 skills)
+
+
+
+# Part 9: Business & Finance
+
+- [charlie-cfo-skill](https://github.com/EveryInc/charlie-cfo-skill) by EveryInc (195 stars) - Bootstrapped CFO
+- [openaccountants](https://github.com/openaccountants/openaccountants) by openaccountants (26 stars) - 371 tax skills, 134 countries
+- [finance-assistant-skill](https://github.com/googlarz/finance-assistant-skill) by googlarz - Personal finance, 11 modes
+- [canadian-finance-planner](https://github.com/cjpatten/canadian-finance-planner-skill) by cjpatten - Canadian RRSP/TFSA/FHSA
+- [awesome-legal-skills](https://github.com/lawvable/awesome-legal-skills) by lawvable (248 stars) - Employment, corporate, methodology
+- [ai-legal-claude](https://github.com/zubair-trabzada/ai-legal-claude) by zubair-trabzada - 14 skills, 5 agents. Contracts, NDA, GDPR
+- [claude-legal-skill](https://github.com/evolsb/claude-legal-skill) by evolsb - CUAD risk detection, redlines
+- C-Level Advisory (34), Regulatory (14), Business Growth (5), Finance (4) - All in alirezarezvani/claude-skills (Part 3)
+
+
+
+# Part 10: Design & Creative
+
+## Video
+
+- [video-use](https://github.com/browser-use/video-use) by browser-use - AI video editing
+- [ndemo](https://github.com/splitbrain/ndemo) by splitbrain - Narrated demo videos with TTS
+- [claude-code-video-toolkit](https://github.com/digitalsamba/claude-code-video-toolkit) by digitalsamba - Remotion, ElevenLabs, ffmpeg
+- [remotion](https://github.com/remotion-dev/skills) by Remotion (500 stars) - Programmatic video with React
+
+## 3D & Graphics
+
+- [render](https://github.com/mfranzon/render) by mfranzon - 3D models from text, Three.js viewer
+- [procedural-clouds-threejs](https://github.com/CK42BB/procedural-clouds-threejs) by CK42BB - Volumetric clouds
+- [sparkjs-skill](https://github.com/shi3z/sparkjs-skill) by shi3z - 3D Gaussian Splat rendering
+
+## Game Dev
+
+- [Unity-Skills](https://github.com/Besty0728/Unity-Skills) by Besty0728 - 543 skills for Unity
+- [GodotPrompter](https://github.com/jame581/GodotPrompter) by jame581 - 28+ Godot 4.x skills
+- [godogen](https://github.com/htdt/godogen) by htdt - Complete Godot projects from descriptions
+
+## Design
+
+- [awesome-design-md](https://github.com/VoltAgent/awesome-design-md) by VoltAgent - 55+ brand design systems
+- [resume-skills](https://github.com/Paramchoudhary/resume-skills) by Paramchoudhary - Resume generation
+- [creative-director-skill](https://github.com/smixs/creative-director-skill) by smixs - Creative direction
+
+
+
+# Part 11: Data & Research
+
+- [scientific-agent-skills](https://github.com/K-Dense-AI/scientific-agent-skills) by K-Dense-AI (18,900 stars) - 133 skills. Genomics, drug binding, molecular dynamics
+- [AI-research-SKILLs](https://github.com/Orchestra-Research/AI-research-SKILLs) by Orchestra-Research - 87 skills, 115K lines
+- [paper-writing-skill](https://github.com/SNL-UCSB/paper-writing-skill) by SNL-UCSB - Five-stage pipeline
+- [econ-writing-skill](https://github.com/hanlulong/econ-writing-skill) by hanlulong - Economics papers from 50+ guides
+- [Awesome-Scientific-Skills](https://github.com/InternScience/Awesome-Scientific-Skills) by InternScience - Open collection
+- [Research-Paper-Writing-Skills](https://github.com/Master-cai/Research-Paper-Writing-Skills) by Master-cai - Abstract through Conclusion
+- [academic-research-skills](https://github.com/Imbad0202/academic-research-skills) by Imbad0202 - Cognitive framework approach
+- [academic-paper-skills](https://github.com/lishix520/academic-paper-skills) by lishix520 - Strategist + Composer
+- database-designer, migration-architect - See alirezarezvani/claude-skills (Part 3)
+- [nutrient-agent-skill](https://github.com/PSPDFKit-labs/nutrient-agent-skill) by PSPDFKit-labs - 40+ agents, OCR, 97+ formats
+- [kreuzberg](https://github.com/kreuzberg-dev/kreuzberg) by kreuzberg-dev - Rust-core, 97+ formats, 248 languages
+- [claude-office-skills](https://github.com/claude-office-skills/skills) by claude-office-skills - OCR, Doc Parser, Layout Analyzer
+
+
+
+# Part 12: Automation & Productivity
+
+- [cc-switch](https://github.com/farion1231/cc-switch) by farion1231 (45,418 stars) - Context management
+- [n8n-skills](https://github.com/czlonkowski/n8n-skills) by czlonkowski - n8n workflow automation
+- [firecrawl-claude-plugin](https://github.com/firecrawl/firecrawl-claude-plugin) by Firecrawl (3,000+ stars) - Web scraping, search, extraction
+- [qdrant-skills](https://github.com/qdrant/qdrant-skills) by Qdrant - Vector database patterns
+- [gsap-skills](https://github.com/greensock/gsap-skills) by GreenSock - Animation library
+- [wonda](https://github.com/degausai/wonda) by degausai - AI productivity
+- [beautiful-prose](https://github.com/SHADOWPR0/beautiful-prose) by SHADOWPR0 - Prose generation
+- [humanizer](https://github.com/blader/humanizer) by blader - Content humanization
+- [tweetclaw](https://github.com/Xquik-dev/tweetclaw) by Xquik-dev - Twitter/X content
+- [x-article-publisher](https://github.com/wshuyi/x-article-publisher) by wshuyi - Article publishing
+- [email-marketing-bible](https://github.com/CosmoBlk/email-marketing-bible) by CosmoBlk - Email marketing guide
+- [ai-marketing-skills](https://github.com/BrianRWagner/ai-marketing-skills) by BrianRWagner - Marketing automation
+- [advertising-skills](https://github.com/realkimbarrett/advertising-skills) by realkimbarrett - Avatars, offers, headlines
+- [whatsapp-integrate, whatsapp-automate](https://github.com/gokapso/whatsapp-integrate) by gokapso - WhatsApp integration
+
+
+
+# Part 13: Specialized Domains
+
+Education
+
+- [claude-education-skills](https://github.com/GarethManning/claude-education-skills) by GarethManning - 100+ skills across 14 educational domains
+- [deeptutor-claude-skill](https://github.com/ndpvt-web/deeptutor-claude-skill) by ndpvt-web - Graph-enhanced RAG tutoring with knowledge graphs
+- [moodle-mcq](https://github.com/danielcregg/moodle-mcq) by danielcregg - Moodle quiz generation in GIFT/XML/Aiken formats
+- [codebase-to-course](https://github.com/zarazhangrui/codebase-to-course) by zarazhangrui - Turn codebases into interactive HTML courses
+- [tutor-skills](https://github.com/RoundTable02/tutor-skills) by RoundTable02 - StudyVault generation with interactive quizzes
+
+Home Automation
+
+- [claude-homelab](https://github.com/jmagar/claude-homelab) by jmagar - 18 skills: Plex, Radarr, Sonarr, Tailscale, ZFS
+- [homeassistant-claude-kit](https://github.com/dcb/homeassistant-claude-kit) by dcb - Home Assistant setup with React dashboard
+
+Travel & Genealogy
+
+- [travel-hacking-toolkit](https://github.com/borski/travel-hacking-toolkit) by borski - Points, miles, award flights optimization
+- [genealogy-research](https://github.com/sliday/genealogy-research) by sliday - GPS methodology, 15 countries, historical documents
+- [claude-family-history-research-skill](https://github.com/emaynard/claude-family-history-research-skill) by emaynard - GPS and Evidence Explained citation methodology
+
+Health & Fitness
+
+- [agilefitness](https://github.com/coachmanjeet/agilefitness) by coachmanjeet - Fitness: workout design, nutrition, meal planning
+
+
+
+Niche Programming Languages
+
+- [swiftui-agent-skill](https://github.com/twostraws/swiftui-agent-skill) by Paul Hudson (twostraws) - SwiftUI, SwiftData, Swift Concurrency, Swift Testing
+- [swift-development](https://github.com/hmohamed01/swift-development) by hmohamed01 - Swift iOS/macOS with live Apple docs fetching
+- [swift-unit-test-instructions](https://github.com/jeremieb/swift-unit-test-instructions) by jeremieb - Swift/SwiftUI/UIKit testing framework
+- [rust-skills](https://github.com/actionbook/rust-skills) by actionbook - Rust with Meta-Problem-Driven Knowledge Indexing
+- [rust-dev-skill](https://github.com/onsails/cc) by onsails - Strict Rust FAIL FAST. Edition 2024
+- [claude-code\_rails-upgrade-skill](https://github.com/ombulabs/claude-code_rails-upgrade-skill) by ombulabs - Rails upgrades from 2.3 to 8.1
+- [claude-code\_dual-boot-skill](https://github.com/ombulabs/claude-code_dual-boot-skill) by ombulabs - Dual-boot dependency environments
+- [layered-rails-skills](https://github.com/palkan/skills) by palkan - Layered Rails architecture commands
+- [threejs-skills](https://github.com/cloudai-x/threejs-skills) by cloudai-x (pinkforest) - 10 skills: fundamentals, geometry, materials, shaders
+
+Meta & Directories
+
+- [awesome-agent-skills](https://github.com/VoltAgent/awesome-agent-skills) by VoltAgent (16,400 stars) - 1,000+ curated skills from official teams + community
+- [awesome-claude-skills](https://github.com/travisvn/awesome-claude-skills) by travisvn - Curated list of Claude skills and resources
+- [claude-code-skill-factory](https://github.com/alirezarezvani/claude-code-skill-factory) by alirezarezvani - Toolkit for building production-ready skills. 5 factories
+
+
+
+## Appendix A: Trust Score Methodology
+
+> The people who install these now and build their workflows around them will have a compounding advantage over everyone who finds out later. That's always how it works with new tooling
+
+This article will be updated as the ecosystem grows
+
+**If this was useful:**
+
+- ♡ Like this article - it helps more people find it
+- ↺ Repost - someone in your feed needs this right now
+- ☆ Bookmark - you'll want to come back when you need a specific skill
+- Follow me [@polydao](https://x.com/@polydao) for weekly deep dives on Claude Code, Skills, MCP, and AI tooling
+- [procedural-clouds-threejs](https://github.com/CK42BB/procedural-clouds-threejs) by CK42BB - Volumetric raymarching clouds in Three.js
+- [sparkjs-skill](https://github.com/shi3z/sparkjs-skill) by shi3z - 3D Gaussian Splat rendering with Three.js
+- [fal-3d](https://github.com/fal-ai-community/fal-3d) by [fal.ai](https://fal.ai/) - Generate 3D models from text/images
+
+Design Systems
+
+- [awesome-design-md](https://github.com/VoltAgent/awesome-design-md) by VoltAgent - 55+ brand design systems (Apple, Stripe, Vercel, Spotify, Figma)
+- [resume-skills](https://github.com/Paramchoudhary/resume-skills) by Paramchoudhary - Resume generation and optimization
+- [creative-director-skill](https://github.com/smixs/creative-director-skill) by smixs - Creative direction and brand development
+
+Game Development
+
+- [Unity-Skills](https://github.com/Besty0728/Unity-Skills) by Besty0728 - 543 skills. Semi-auto and full-auto modes for Unity
+- [GodotPrompter](https://github.com/jame581/GodotPrompter) by jame581 (28+ skills) - Godot 4.x: gameplay, UI, multiplayer, rendering, build/deploy
+- [godogen](https://github.com/htdt/godogen) by htdt - Complete Godot projects from descriptions. 850+ API docs
+
+## Part 11: Data & Research (83 skills)
+
+Scientific Research
+
+- [scientific-agent-skills](https://github.com/K-Dense-AI/scientific-agent-skills) by K-Dense-AI (18,900 stars) - 133 skills. Cancer genomics, drug-target binding, molecular dynamics, 78+ databases
+- [AI-research-SKILLs](https://github.com/Orchestra-Research/AI-research-SKILLs) by Orchestra-Research (87 skills) - Literature survey, experiment execution, paper writing. 115K lines
+- [Awesome-Scientific-Skills](https://github.com/InternScience/Awesome-Scientific-Skills) by InternScience - Open collection for PubMed, RDKit, scientific writing
+- [paper-writing-skill](https://github.com/SNL-UCSB/paper-writing-skill) by SNL-UCSB - Five-stage pipeline: Brainstorm -> Draft 0 -> Evaluate -> Write -> Compress
+- [Research-Paper-Writing-Skills](https://github.com/Master-cai/Research-Paper-Writing-Skills) by Master-cai - Abstract, Introduction, Method, Experiments, Conclusion
+- [academic-research-skills](https://github.com/Imbad0202/academic-research-skills) by Imbad0202 - Reading, writing, reviewing papers. Cognitive framework
+- [econ-writing-skill](https://github.com/hanlulong/econ-writing-skill) by hanlulong - Economics papers from 50+ leading economists' guides
+- [academic-paper-skills](https://github.com/lishix520/academic-paper-skills) by lishix520 - Strategist + Composer two-skill system
+
+Data Engineering
+
+- [data-engineer](https://github.com/alirezarezvani/claude-skills) by alirezarezvani - Data pipelines, ETL, warehouse design
+- [database-designer](https://github.com/alirezarezvani/claude-skills) by alirezarezvani - Schema analyzer, ERD generation, index optimizer
+- [database-schema-designer](https://github.com/alirezarezvani/claude-skills) by alirezarezvani - Requirements to migrations, types, seed data, RLS
+- [migration-architect](https://github.com/alirezarezvani/claude-skills) by alirezarezvani - Migration planner, compatibility checker
+- [duckdb-skills](https://github.com/duckdb/duckdb-skills) by DuckDB - DuckDB analytics patterns
+
+Document Intelligence
+
+- [nutrient-agent-skill](https://github.com/PSPDFKit-labs/nutrient-agent-skill) by PSPDFKit-labs - 40+ agents. Convert, OCR (20+ languages), redact, sign, forms. 97+ formats
+- [kreuzberg](https://github.com/kreuzberg-dev/kreuzberg) by kreuzberg-dev - Rust-core document intelligence. 97+ formats, 248 programming languages, OCR
+- [claude-office-skills](https://github.com/claude-office-skills/skills) by claude-office-skills - Smart OCR, Doc Parser, Layout Analyzer, Table Extractor
+
+## Part 12: Automation & Productivity (84 skills)
+
+Workflow Automation
+
+- [dispatching-parallel-agents](https://github.com/obra/superpowers) by obra - Concurrent subagent workflows with context isolation
+- [mcp-server-builder](https://github.com/alirezarezvani/claude-skills) by alirezarezvani - MCP servers from OpenAPI specs
+- [browser-automation](https://github.com/alirezarezvani/claude-skills) by alirezarezvani - Web browser automation patterns
+- [n8n-skills](https://github.com/czlonkowski/n8n-skills) by czlonkowski - n8n workflow automation
+
+Integrations & Tools
+
+- [firecrawl-skills](https://github.com/firecrawl/firecrawl-claude-plugin) by Firecrawl (3,000+ stars) - Web scraping, search, extraction, browser automation
+- [qdrant-skills](https://github.com/qdrant/qdrant-skills) by Qdrant - Vector database patterns
+- [gsap-skills](https://github.com/greensock/gsap-skills) by GreenSock - Animation library patterns
+
+Productivity
+
+- [cc-switch](https://github.com/farion1231/cc-switch) by farion1231 (45,418 stars) - Context management for multi-project workflows
+- [wonda](https://github.com/degausai/wonda) by degausai - AI productivity assistant
+- [beautiful-prose](https://github.com/SHADOWPR0/beautiful-prose) by SHADOWPR0 - Beautiful prose generation
+- [humanizer](https://github.com/blader/humanizer) by blader - Content humanization
+- [tweetclaw](https://github.com/Xquik-dev/tweetclaw) by Xquik-dev - Twitter/X content creation
+- [x-article-publisher](https://github.com/wshuyi/x-article-publisher) by wshuyi - Article publishing for X
+- [email-marketing-bible](https://github.com/CosmoBlk/email-marketing-bible) by CosmoBlk - Email marketing comprehensive guide
+- [ai-marketing-skills](https://github.com/BrianRWagner/ai-marketing-skills) by BrianRWagner - AI-powered marketing automation
+- [advertising-skills](https://github.com/realkimbarrett/advertising-skills) by realkimbarrett - Advertising: avatars, offers, headlines
+- [whatsapp-integrate](https://github.com/gokapso/whatsapp-integrate) and [whatsapp-automate](https://github.com/gokapso/whatsapp-automate) by gokapso - WhatsApp integration and automation
+
+## Part 13: Specialized Domains (38 skills)
+
+Scoring formula: FINAL SCORE = BASE TIER SCORE + SUM(BONUSES) + SUM(PENALTIES), capped at 10, floored at 0.
+
+Base tier scores: Tier 1 (Official/Enterprise or 5,000+ stars) = 7.0. Tier 2 (Verified Community, 1,000-5,000 stars) = 5.0. Tier 3 (Emerging, under 1,000 stars) = 3.0. Tier 4 (Experimental) = 1.0.
+
+Bonuses (up to +5): Recent commits under 1 month (+2), under 3 months (+1), Python stdlib only (+1), multi-tool compatibility (+1), comprehensive README (+1), test coverage (+1).
+
+Penalties: No README (-1), known security flags (-2), no activity over 6 months (-1), over 12 months (-2), single contributor (-0.5), fork without attribution (-1), eval/exec usage (-1.5), hardcoded secrets (-2), unvalidated network calls (-1), prompt injection patterns (-2).
+
+Score interpretation: 9-10 = EXCEPTIONAL. 7-8 = HIGH TRUST. 5-6 = MODERATE. 3-4 = LOW. 0-2 = UNTRUSTED. Under 0 = REJECT.
+
+## Appendix B: Security Considerations
+
+Documented CVEs (all patched as of April 2026): CVE-2025-49596 (CVSS 9.4, RCE via MCP Inspector), CVE-2025-59536 (CVSS 8.7, RCE via MCP consent bypass), CVE-2025-54795 (CVSS 8.7, command injection), CVE-2025-54794 (CVSS 7.7, path restriction bypass), CVE-2025-64755 (CVSS High, sed parsing bypass), CVE-2025-52882 (CVSS High, WebSocket origin validation), CVE-2025-53109 (CVSS High, filesystem symlink bypass), CVE-2025-53110 (CVSS High, path traversal), CVE-2026-21852 (CVSS 5.3, API key exfiltration).
+
+Security best practices: 1) Prefer official skills from verified publishers. 2) Read SKILL.md before installing. 3) Favor stdlib-only skills. 4) Use sandboxed environments for experimental skills. 5) Subscribe to security advisories. 6) Run security-focused skills regularly.
\ No newline at end of file
diff --git a/Claude/I Tried 100+ Claude Code Skills. These 6 Are The Best.md b/Claude/I Tried 100+ Claude Code Skills. These 6 Are The Best.md
new file mode 100644
index 0000000..35446df
--- /dev/null
+++ b/Claude/I Tried 100+ Claude Code Skills. These 6 Are The Best.md
@@ -0,0 +1,64 @@
+---
+title: "I Tried 100+ Claude Code Skills. These 6 Are The Best"
+source: "https://x.com/nateherk/status/2050941624578920535"
+author:
+ - "[[@nateherk]]"
+published: 2026-05-03
+created: 2026-05-04
+description: "400 hours in Claude Code. I've worked with real estate agencies, HVAC companies, coaches, marketing agencies, etc.Different industries. Sa..."
+tags:
+ - "clippings"
+---
+
+
+400 hours in Claude Code. I've worked with real estate agencies, HVAC companies, coaches, marketing agencies, etc. Different industries. Same problems. Same six skills keep showing up. These aren't flashy demos. They're boring, simple, and effective. The kind of skills businesses actually write checks for.
+
+Quick note: I'll use the word "skill" throughout this, but technically some of these are plugins. A skill is a markdown file that teaches Claude how to do a specific job. A plugin is a bigger package around that skill. It can contain multiple skills plus extras like hooks or MCP servers that change how Claude Code behaves under the hood. For this breakdown, the technical category doesn't matter. What matters is that each one makes Claude noticeably better at its job.
+
+
+
+## 1\. The Factory: Skill Creator
+
+
+
+This is the skill that builds every other skill.
+
+You describe what you want in plain English. Claude drafts the skill, tests it, iterates on it, and packages it into something you can use forever. You don't manually touch a skill.md file. You don't figure out the formatting. You just talk to Claude like you're explaining the job to a coworker and walk away with a skill that runs the same every time. You can even drop in an SOP and it turns that into a reusable skill. The skill itself isn't what a client pays for. But every automation you sell to an HR company, a real estate agency, a coach, they all come out of this one. It's the factory that builds the product. Simple example: a real estate agency wastes hours every week writing property descriptions. Without Skill Creator, you're writing out the whole skill.md file, figuring out trigger conditions, testing it over and over. With Skill Creator, you explain it naturally and it builds, tests, and packages it. Compresses the learning curve. I installed this one globally on a user scope. No matter what project I'm working in, if I need to build a skill, it just invokes automatically. Install: /plugin install skill-creator@claude-plugins-official
+
+## 2\. The Process: Superpowers
+
+
+
+The number one failure mode in Claude Code is rushed code. You ask for something. Claude sprints to write it. On the surface it looks fine. Then your client runs it and it falls apart. Superpowers forces Claude to work like a senior developer. Instead of jumping in and writing code right away, Claude steps back and plans the whole thing first. Here's what it does: → Plans the entire implementation before writing a single line → Works in an isolated environment so nothing breaks your main project → Writes tests before writing code → Brainstorms solutions → Reviews its own work in two stages: once for whether it matches the spec, once for code quality It won't make Claude one-shot everything. A large part of your job is still QA. But getting 80% right on the first pass instead of 60%? That's fewer debugging cycles, less time fixing things, and lower token costs. 150,000+ stars on GitHub. Most popular community skill out there. Install: /plugin install superpowers@claude-plugins-official
+
+## 3\. The Clean Room: GSD
+
+
+
+If Superpowers is about how Claude writes code, GSD is about the environment Claude writes it in. Here's what stood out after long sessions. You start a session. Things are great. Claude's nailing everything. Then somewhere around halfway through the context window, it starts to fall apart. Code gets sloppy. Claude forgets requirements you set at the beginning. It cuts corners. It tells you things are done when they're not. That's context rot. GSD fixes it with context engineering. Instead of letting one big session slowly degrade, it spawns fresh sub-agents for each task. Each one gets a clean context window focused only on what it needs to know. Your main session stays clean. Each task gets the full power of Claude's context window instead of just the leftovers. It also has built-in quality gates: → Scope reduction detection catches when Claude silently drops a requirement you asked for → Security enforcement anchors verification to your threat model These aren't things you'd manually think to check, especially if you're not coming from a software engineering background. That's exactly why having them automated matters. There's an autonomous mode too. Hand Claude a spec and walk away. It'll plan, execute, commit, and work through the whole project without babysitting. Here's the key relationship: Superpowers gives Claude the process (plan, test, review, repeat). GSD gives Claude the clean context so that process actually works every time. The sub-agents cost tokens. But what it saves you is the hours you'd spend redoing work that Claude broke because it forgot what you asked. Install: npx get-shit-done-cc --claude --global Type /gsd-help inside Claude Code to see all available commands.
+
+## 4\. The Safety Net: /review + /ultra-review
+
+
+
+This one's already built into Claude Code. Most people just aren't using it. Type /review after writing code. Claude runs a structured code review on what you just built. Bugs, edge cases, design issues. All the stuff a careful reviewer would flag. Runs locally. Fast. Costs nothing beyond your usual usage tokens. /ultra-review is the bigger version. Launched alongside Opus 4.7. Instead of running locally, it uploads your branch to a cloud sandbox and spins up a fleet of reviewer agents in parallel. Each one attacks your codebase from a different angle: logic, security, performance, edge cases. The key part: before any bug shows up on your list, it has to be independently reproduced and verified. No pile of style nitpicks or false positives. Confirmed bugs only. The annoying part. A few requirements: → Need Claude Code version 2.1.86 or later → Must be signed in with a Claude account. An API key alone won't work. → /ultra-review can take 10-20 minutes, but it runs in the background so you can keep working → Pro and Max plans get 3 free runs to try. After that it's $5-20 per run depending on size Practically, this fits at the end of any Claude Code workflow. Plan with Superpowers. Execute with clean context using GSD. Then before you merge anything important, run /review to catch what Claude missed. Use /review for fast feedback on everything. Save /ultra-review for the merges that matter. Big refactors. Anything touching payments, auth, or database migrations. The kind of commit where a production bug costs way more than the time and tokens. No install needed. Just type /review or /ultra-review in your terminal on Claude Code 2.1.86+.
+
+## 5\. The Compressor: Context Mode
+
+
+
+Every tool call Claude makes dumps raw data into your context window. A Playwright snapshot? 56 kilobytes. 20 GitHub issues? 59 kilobytes. After about 30 minutes of real work, 40% of your context window is just garbage. Log output, raw data, stuff Claude never actually needs to look at. And when Claude runs out of space and compacts the conversation, it forgets what files it was editing, what tasks were in progress, and what you last asked it to do. Context Mode fixes both halves of this problem. First, it keeps the garbage out. When Claude runs a command or fetches a URL, Context Mode routes that call through a sandbox. Your code runs in an isolated subprocess. The raw output gets captured and only the part Claude actually needs comes back into the context window. The numbers from their benchmarks: → 56KB Playwright snapshot becomes 299 bytes → 46KB access log becomes 155 bytes → 315KB of raw output across a full session becomes 5KB total You can check your own numbers anytime by running /context-mode:ctx-stats. Second, it tracks every meaningful event in your session in a local SQL database. Every file edit, every task, every decision, every error. When Claude compacts the conversation, Context Mode rebuilds a session snapshot and injects it back in. The model picks up exactly where you left off. Files, tasks, last prompt. All intact. Sessions that used to fall apart at the 30-minute mark now run for three hours without slowdown. You stop wasting prompts reminding Claude of context it already had. Install: /plugin marketplace add mksglu/context-mode /plugin install context-mode@context-mode Restart Claude Code after install. The plugin auto-installs the MCP server, hooks, and routing instructions.
+
+## 6\. The Memory: Claude Mem
+
+
+
+If Context Mode keeps your current session clean, Claude Mem carries knowledge across all future sessions. Claude Code starts every new session from scratch. You open a session. Explain the project for the fifth time this month. 10 minutes and thousands of tokens just getting Claude caught up. You can try to solve this with CLAUDE.md files and memory files. Those are plain text files that give Claude persistent project instructions. They work, but you have to maintain them by hand. Miss an update and Claude misses that context. Claude Mem takes this completely over. It hooks into Claude's session lifecycle and automatically captures what happened during your session. File edits, decisions, bug fixes, commands. Then it uses Claude's own Agent SDK to compress all of that into semantic summaries and stores everything in a local SQLite database with vector search. New session? The relevant parts get injected back automatically. It also auto-generates folder-level CLAUDE.md files and updates them as you work. Your project documentation literally writes itself while you code. The retrieval uses a three-layer search system: 1️⃣ Returns a compact index of observations 2️⃣ Pulls a timeline around the ones that matter 3️⃣ Fetches full details only for the specific handoff you need About 10x token savings on retrieval compared to dumping everything in at session start. You pick up a project you haven't touched in two weeks and Claude already knows what you were working on, what you decided, and where you left off. Everything runs locally with a web viewer so you can see exactly what Claude remembers about your project. Install: /plugin marketplace add thedotmack/claude-mem /plugin install claude-mem Important: don't run the npm install command. That installs the SDK library only and nothing actually works because the hooks never register. Stick with the two commands above.
+
+## Bonus: Frontend Design
+
+One more. This is the official frontend design skill from Anthropic. If you're building anything visual in Claude Code, whether it's website design, slide decks, or UI components, this skill makes them look significantly less AI-generated. Install it globally so whenever you're designing anything, it activates automatically. Worth noting: this type of capability is also baked into Anthropic's new product, Claude Design. But if you bring that project back into Claude Code, you'll want this skill installed. Install: /plugin install frontend-design@claude-plugins-official
+
+## How to Actually Sell This
+
+Stop selling the workflow. Sell the outcome. Saving the business owner 10 hours a week. Cutting their admin mistakes and human error. Speeding up their leads. That's what they actually want to pay for. Instead of offering to build an AI workflow, you're offering to save time, cut costs, and help them focus on making more profit. If you're brand new, don't try to use all six right away. 1️⃣ Pick one skill 2️⃣ Learn it 3️⃣ Build out a few workflows using it 4️⃣ Show a business owner a demo They won't care about your experience. They'll care about the value of what you're showing them. Once you understand how these skills work and how they piece together, you build better automations quicker and cheaper. More demos, more content, more of your own business automated. All of that experience helps you communicate value to business owners much better. I walk through all six installs and the full selling strategy in the video. Link in the first reply.
\ No newline at end of file
diff --git a/Claude/I want to learn how to build AI Agents with Claude (full course).md b/Claude/I want to learn how to build AI Agents with Claude (full course).md
new file mode 100644
index 0000000..dca34b3
--- /dev/null
+++ b/Claude/I want to learn how to build AI Agents with Claude (full course).md
@@ -0,0 +1,84 @@
+---
+title: "I want to learn how to build AI Agents with Claude (full course)"
+source: "https://x.com/heygurisingh/status/2032885031623118908"
+author:
+ - "[[Unknown]]"
+published: 2026-03-15
+created: 2026-03-15
+description:
+tags:
+ - "clippings"
+---
+I have combined every resource I have found to create a full course on building AI agents with Claude. In less than 15 minutes you'll understand the entire agent stack -- from single-agent loops to multi-agent teams running in parallel. After reading this article you will understand Claude's agent ecosystem better than 99% of people (yes, really).
+
+You're about to be given a full course on building AI agents with Claude -- the same architecture that powers Claude Code, Cowork, and every production agent Anthropic ships.
+
+Here's how to utilise this article:
+
+- Module 1: What agents actually are (and aren't)
+- Module 2: Your first agent with Claude Code
+- Module 3: The Claude Agent SDK
+- Module 4: Multi-agent orchestration with Agent Teams
+- Module 5: Production deployment patterns
+
+## Module 1: What Agents Actually Are
+
+In every module I will give you the foundations + how to get AI to help you so that you 1: understand what's going on, and 2: build agents correctly without needing a PhD.
+
+Let's get straight into it...
+
+THE DIFFERENCE BETWEEN CHAT AND AGENTS
+
+Most people use Claude like a chatbox. You ask a question. You get an answer. That's not an agent.
+
+An agent is a system that can plan, execute, observe results, and decide what to do next -- autonomously. It doesn't just answer your question. It takes ownership of a problem and works through it.
+
+Here's the mental model:
+
+Chat = you ask, Claude answers. One turn. Done.
+
+Agent = you describe an outcome, Claude figures out the steps, executes them, checks results, adjusts if something breaks, and keeps going until the job is done.
+
+The core loop is simple: Think → Act → Observe → Repeat.
+
+Claude reads a task. It decides which tool to use. It executes that tool (reading a file, running a command, searching the web). It observes the result. Then it decides the next step. This loop runs until the task is complete or Claude determines it needs your input.
+
+This is the exact loop that powers Claude Code. When you tell Claude Code to "find and fix the bug in [auth.py](https://auth.py/)," it doesn't just suggest a fix. It reads the file, traces the logic, identifies the bug, writes the fix, runs the tests, and if the tests fail, it reads the error, adjusts the fix, and runs them again.
+
+That's an agent.
+
+THE CLAUDE AGENT STACK
+
+Before you build anything, you need to understand the four layers:
+
+**Layer 1: Claude Code (the terminal agent)**. This is Anthropic's agentic coding tool. It lives in your terminal. It can read files, write code, run bash commands, commit to Git, review PRs, and manage entire projects. If you want to use agents without writing any code yourself, this is where you start.
+
+**Layer 2: The Claude Agent SDK (the builder's toolkit)**. This is the same engine that powers Claude Code, exposed as a Python and TypeScript library. Everything Claude Code can do -- the agent loop, the built-in tools, the context management -- you can program it yourself. This is for developers who want to build custom agents for their own applications.
+
+**Layer 3: MCP (the connection layer)**. Model Context Protocol connects your agents to external tools -- GitHub, databases, browsers, Slack, anything with an API. MCP servers expose "tools" that Claude can call. Over 200 community servers exist as of early 2026.
+
+**Layer 4: Agent Teams (multi-agent orchestration)**. This is the newest layer. Instead of one agent doing everything sequentially, you spawn multiple agents that work in parallel, communicate with each other, and coordinate through a shared task list. One agent handles the API. Another builds the frontend. A third reviews everything the other two produce.
+
+You don't need all four layers on day one. Most people start with Layer 1 and never leave it. But understanding the full stack means you know where to go when your needs grow.
+
+DO YOU NEED AN AGENT?
+
+Not everything needs an agent. Here's the test:
+
+If the task is a single question with a single answer -- use chat.
+
+If the task requires multiple steps, tool usage, or iteration based on results -- use an agent.
+
+If the task has distinct parallel components where specialists would help -- use multi-agent.
+
+A good rule: if you find yourself copy-pasting Claude's output back into Claude with "now do this next step," you need an agent.
+
+Module 1 complete. You now understand the agent mental model. In Module 2, you're going to build your first one.
+
+## Module 2: Your First Agent with Claude Code
+
+Claude Code is the fastest path to using agents. No SDK. No code to write. Just your terminal and a description of what you want done.
+
+INSTALLATION
+
+You need Node.js 18+ installed. Then run:
\ No newline at end of file
diff --git a/Claude/If You're Not Using These 100 Repos, Delete CLAUDE CODE - a large collection of repositories.md b/Claude/If You're Not Using These 100 Repos, Delete CLAUDE CODE - a large collection of repositories.md
new file mode 100644
index 0000000..25c9bee
--- /dev/null
+++ b/Claude/If You're Not Using These 100 Repos, Delete CLAUDE CODE - a large collection of repositories.md
@@ -0,0 +1,1100 @@
+---
+title: "If You're Not Using These 100 Repos, Delete CLAUDE CODE - a large collection of repositories"
+source: "https://x.com/0x_kaize/status/2048059417661174052"
+author:
+ - "[[@0x_kaize]]"
+published: 2026-04-25
+created: 2026-04-26
+description: "Most people think of Claude Code as \"just another AI coding tool.\"I thought the same - until I discovered configs, plugins, skills, and suba..."
+tags:
+ - "clippings"
+---
+
+
+Most people think of Claude Code as "just another AI coding tool."
+
+I thought the same - until I discovered configs, plugins, skills, and subagents.
+
+Before that, I was writing the same prompts over and over. Losing context every session. Wondering why other devs were 5x more productive with the same tool.
+
+So I decided to figure out how to work better. The answer: REPOSITORIES.
+
+I put together a collection of the 100 best repositories:
+
+## CATEGORY 1 - AWESOME LISTS & META-INDEXES
+
+
+
+awesome-claude-code
+
+The Claude Code ecosystem is evolving so rapidly that it’s hard to keep up. New features appear every day, plugins come and go, and half of all the interesting stuff never even makes it to Hacker News.
+
+Awesome lists help solve this problem. These are community-maintained directories that take on the work of searching: they classify, rank, and filter content based on actual quality, not the number of stars on GitHub.
+
+**1/ awesome-claude-code**
+
+The definitive curated list of the entire Claude Code ecosystem
+
+- 28.5k+ stars, the main index
+- only Claude itself is allowed to submit PRs
+- skills, hooks, commands, orchestrators, plugins
+
+repo: [https://github.com/hesreallyhim/awesome-claude-code](https://github.com/hesreallyhim/awesome-claude-code)
+
+**2/ awesome-claude-skills (ComposioHQ)**
+
+50+ production-ready Claude skills for automation
+
+- PostgreSQL, deep research, CSV, root-cause tracing
+- cross-compatible with Claude Code, Codex, Cursor, Gemini CLI
+- install via /skill add
+
+repo: [https://github.com/ComposioHQ/awesome-claude-skills](https://github.com/ComposioHQ/awesome-claude-skills)
+
+**3/ awesome-claude-plugins (ComposioHQ)**
+
+Curated plugin collection for Claude Code
+
+- commands, agents, hooks and MCP servers
+- includes connect-apps, frontend-design, artifacts-builder
+- install via /plugin marketplace add
+
+repo: [https://github.com/ComposioHQ/awesome-claude-plugins](https://github.com/ComposioHQ/awesome-claude-plugins)
+
+**4/ awesome-claude-prompts**
+
+4.2k+ star collection of optimized Claude prompts
+
+- XML-structured prompt techniques
+- business, creative, dev workflows
+- copy-paste ready
+
+repo: [https://github.com/langgptai/awesome-claude-prompts](https://github.com/langgptai/awesome-claude-prompts)
+
+**5/ awesome-claude**
+
+All-around list of everything Anthropic Claude-related
+
+- courses, guides, desktop apps, articles
+- covers MCP, BMAD method, Claude Code tips
+- good starting point for newcomers
+
+repo: [https://github.com/alvinunreal/awesome-claude](https://github.com/alvinunreal/awesome-claude)
+
+**6/ awesome-agent-skills (VoltAgent)**
+
+1000+ agent skills from real engineering teams
+
+- not AI-generated slop, battle-tested
+- works with Claude Code, Codex, Gemini CLI, Cursor
+- Notion, WhatsApp, NotebookLM, memory included
+
+repo: [https://github.com/VoltAgent/awesome-agent-skills](https://github.com/VoltAgent/awesome-agent-skills)
+
+**7/ awesome-claude-code-subagents (VoltAgent)**
+
+Hand-picked subagents for every stack and task
+
+- domain-specific expertise bundles
+- production-ready components
+- integrates with most coding agents
+
+repo: [https://github.com/VoltAgent/awesome-claude-code-subagents](https://github.com/VoltAgent/awesome-claude-code-subagents)
+
+**8/ awesome-claude-code-plugins (ccplugins)**
+
+Focused list of Claude Code plugins only
+
+- slash commands, subagents, MCP servers, hooks
+- install commands included for each
+- enables hosting your own marketplace
+
+repo: [https://github.com/ccplugins/awesome-claude-code-plugins](https://github.com/ccplugins/awesome-claude-code-plugins)
+
+**9/ awesome-claude-code (jqueryscript)**
+
+Curated tools, IDE integrations and frameworks
+
+- star-tier markers
+- SDKs, security tools, agent collections
+- great discovery tool
+
+repo: [https://github.com/jqueryscript/awesome-claude-code](https://github.com/jqueryscript/awesome-claude-code)
+
+**10/ Claude-Code-Repos-Index**
+
+Indexed catalogue of 75+ quality Claude Code repos
+
+- all original, no AI slop
+- maintained by Daniel Rosehill
+- great navigator for the ecosystem
+
+repo: [https://github.com/danielrosehill/Claude-Code-Repos-Index](https://github.com/danielrosehill/Claude-Code-Repos-Index)
+
+## CATEGORY 2 - OFFICIAL ANTHROPIC REPOS
+
+
+
+Before you start working with any community repository, begin with these.
+
+These are Anthropic’s official projects - the command-line interface (CLI) itself, the SDKs, the skills library, and the GitHub Action. Everything else on this list is built on top of them.
+
+They may not always offer the widest range of features, but they are the most stable, best-documented, and most reliable choice for teams.
+
+**11/ anthropics/claude-code**
+
+The official Claude Code terminal tool
+
+- 55k+ stars
+- natural language commands for your codebase
+- maintained by Anthropic
+
+repo: [https://github.com/anthropics/claude-code](https://github.com/anthropics/claude-code)
+
+**12/ anthropics/skills**
+
+Official public repository of Agent Skills
+
+- 37.5k+ stars
+- PDF, DOCX, XLSX, PPTX + art generation
+- the canonical skills reference
+
+repo: [https://github.com/anthropics/skills](https://github.com/anthropics/skills)
+
+**13/ anthropics/claude-plugins**
+
+Anthropic-managed directory of quality plugins
+
+- 2.8k+ stars
+- vetted official plugin collection
+- good baseline for teams
+
+repo: [https://github.com/anthropics/claude-plugins](https://github.com/anthropics/claude-plugins)
+
+**14/ anthropics/claude-code-sdk-python**
+
+Official Python SDK for Claude Code
+
+- 6.1k+ stars
+- programmatic access to capabilities
+- supports Agent SDK patterns
+
+repo: [https://github.com/anthropics/claude-code-sdk-python](https://github.com/anthropics/claude-code-sdk-python)
+
+**15/ anthropics/claude-agent-sdk-typescript**
+
+Official TypeScript SDK for Claude Agent
+
+- type-safe tool orchestration
+- streaming + MCP support
+- V2 preview with send()/stream()
+
+repo: [https://github.com/anthropics/claude-agent-sdk-typescript](https://github.com/anthropics/claude-agent-sdk-typescript)
+
+**16/ anthropics/claude-code-security-review**
+
+AI-powered security review GitHub Action
+
+- 2.8k+ stars
+- analyzes PR diffs for vulnerabilities
+- drop-in CI/CD integration
+
+repo: [https://github.com/anthropics/claude-code-security-review](https://github.com/anthropics/claude-code-security-review)
+
+**17/ anthropics/claude-code-action**
+
+Official GitHub Action for Claude in PRs
+
+- [@claude](https://x.com/@claude) mentions trigger analysis
+- diff reviews + patch suggestions
+- follows your repo conventions
+
+repo: [https://github.com/anthropics/claude-code-action](https://github.com/anthropics/claude-code-action)
+
+**18/ github/github-mcp-server**
+
+Official GitHub MCP server
+
+- direct access to repos, PRs, issues, CI/CD
+- read-only mode via X-MCP-Readonly
+- the most-used server in the ecosystem
+
+repo: [https://github.com/github/github-mcp-server](https://github.com/github/github-mcp-server)
+
+**19/ anthropic-cookbook**
+
+Official Anthropic cookbook with recipes
+
+- notebooks, agents, tool-use patterns
+- RAG, classification, summarization
+- maintained by Anthropic team
+
+repo: [https://github.com/anthropics/anthropic-cookbook](https://github.com/anthropics/anthropic-cookbook)
+
+**20/ courses (Anthropic)**
+
+Free Anthropic courses
+
+- prompt engineering fundamentals
+- API usage, agents, AI Fluency
+- direct from the source
+
+repo: [https://github.com/anthropics/courses](https://github.com/anthropics/courses)
+
+## CATEGORY 3 - SKILLS COLLECTIONS
+
+Skills are the fastest way to expand Claude Code’s capabilities. They aren’t plugins or agents, just Markdown files that teach Claude how to perform specific tasks, from writing proper pull requests to creating videos in Remotion and conducting security audits.
+
+The beauty of it is that good skills let you transform Claude from a general-purpose assistant into an expert in a specific field on demand. You don’t need to explain your tech stack from scratch every time you run it the skill already knows it.
+
+**21/ obra/superpowers**
+
+The most popular skills library for Claude Code
+
+- 148k+ stars
+- brainstorm → spec → plan → TDD → review → merge
+- subagent orchestration
+
+repo: [https://github.com/obra/superpowers](https://github.com/obra/superpowers)
+
+**22/ obra/superpowers-marketplace**
+
+Official marketplace for the Superpowers ecosystem
+
+- 20+ battle-tested skills
+- /brainstorm, /write-plan, /execute-plan
+- SessionStart context injection
+
+repo: [https://github.com/obra/superpowers-marketplace](https://github.com/obra/superpowers-marketplace)
+
+**23/ obra/superpowers-skills**
+
+Community-editable skills for Superpowers
+
+- auto-cloned to ~/.config/superpowers/skills/
+- fork and submit PRs
+- the collaborative side
+
+repo: [https://github.com/obra/superpowers-skills](https://github.com/obra/superpowers-skills)
+
+**24/ obra/superpowers-lab**
+
+Experimental skills under active development
+
+- semantic duplicate-function detection
+- bleeding-edge techniques
+- evolves with real usage
+
+repo: [https://github.com/obra/superpowers-lab](https://github.com/obra/superpowers-lab)
+
+**25/ everything-claude-code**
+
+Hackathon-winning harness built from 10+ months of use
+
+- 30 agents, 136 skills, 60 slash commands
+- 1282 tests, 98% coverage
+- Cerebral Valley x Anthropic winner
+
+repo: [https://github.com/affaan-m/everything-claude-code](https://github.com/affaan-m/everything-claude-code)
+
+**26/ travisvn/awesome-claude-skills**
+
+Community "best-of" skill collection
+
+- 22k+ installs
+- SEO, marketing, design, security, research
+- safety warnings on untrusted skills
+
+repo: [https://github.com/travisvn/awesome-claude-skills](https://github.com/travisvn/awesome-claude-skills)
+
+**27/ trailofbits/claude-code-skills**
+
+Security research and audit skills
+
+- by Trail of Bits security firm
+- vulnerability detection workflows
+- built for real security engineering
+
+repo: [https://github.com/trailofbits/claude-code-skills](https://github.com/trailofbits/claude-code-skills)
+
+**28/ K-Dense-AI/claude-scientific-skills**
+
+Skills for scientific computing
+
+- specialized libraries for research
+- academic and lab workflows
+- data analysis ready
+
+repo: [https://github.com/K-Dense-AI/claude-scientific-skills](https://github.com/K-Dense-AI/claude-scientific-skills)
+
+**29/ ay-automate/ay-skills**
+
+10 production-tested skills
+
+- SEO audits, headless browser automation
+- Remotion video generation, diagrams
+- drop-in ready
+
+repo: [https://github.com/ay-automate/ay-skills](https://github.com/ay-automate/ay-skills)
+
+**30/ BehiSecc/awesome-claude-skills**
+
+A "list of lists" for deeper Claude use
+
+- knowledge acquisition, deeper thinking
+- skill automation utilities
+- meta-resource for curators
+
+repo: [https://github.com/BehiSecc/awesome-claude-skills](https://github.com/BehiSecc/awesome-claude-skills)
+
+## CATEGORY 4 - AGENTS & SUBAGENTS
+
+A single session with Claude can’t handle everything at a high level. It gets overloaded, context gets cluttered with half-read files and outdated debug data and by the time you ask for a code review, it’s already forgotten what you were building.
+
+Subagents solve this problem. Instead of a single Claude that struggles with twenty tasks, you get a team of specialists: each with its own context and a single task.
+
+**31/ wshobson/agents**
+
+Massive collection of production subagents
+
+- 25k+ stars
+- strategy, dev, security, design, data, research
+- drop-in for Claude Code workflows
+
+repo: [https://github.com/wshobson/agents](https://github.com/wshobson/agents)
+
+**32/ awesome-claude-agents**
+
+Team of specialized AI agents that work together
+
+- 3.7k+ stars
+- agents collaborate on full features
+- expert-level knowledge across stacks
+
+repo: [https://github.com/vijaythecoder/awesome-claude-agents](https://github.com/vijaythecoder/awesome-claude-agents)
+
+**33/ davepoon/claude-code-subagents-collection**
+
+100+ production-ready subagents
+
+- 2.2k+ stars
+- comprehensive domain expertise
+- drop-in for any workflow
+
+repo: [https://github.com/davepoon/claude-code-subagents-collection](https://github.com/davepoon/claude-code-subagents-collection)
+
+**34/ claude-code-agents (59 specialists)**
+
+59 specialized AI agents for development
+
+- TypeScript, Python, Java, Kotlin reviewers
+- workflow automation agents
+- great for onboarding teams
+
+repo: [https://github.com/baryhuang/claude-code-agents](https://github.com/baryhuang/claude-code-agents)
+
+**35/ multi-agent-squad**
+
+Production-ready multi-agent orchestration
+
+- 74+ stars
+- task routing + consensus decisions
+- works across coding agents
+
+repo: [https://github.com/ruvnet/multi-agent-squad](https://github.com/ruvnet/multi-agent-squad)
+
+**36/ Agent-Fusion**
+
+Multi-agent orchestration across AI tools
+
+- Claude Code, Codex CLI, Amazon Q, Gemini
+- bidirectional collaboration
+- intelligent task routing
+
+repo: [https://github.com/0xAsten/Agent-Fusion](https://github.com/0xAsten/Agent-Fusion)
+
+**37/ sub-agents**
+
+Manager for Claude Code sub agents
+
+- adds hooks and custom slash commands
+- 175+ stars
+- lightweight setup
+
+repo: [https://github.com/webdevtodayjason/sub-agents](https://github.com/webdevtodayjason/sub-agents)
+
+**38/ sub-agents-directory**
+
+Curated 100+ subagent prompts
+
+- 74+ stars
+- MCP servers included
+- community-maintained index
+
+repo: [https://github.com/dotneet/sub-agents-directory](https://github.com/dotneet/sub-agents-directory)
+
+**39/ claude-code-heavy**
+
+Multi-agent research orchestration
+
+- 72+ stars
+- parallel research agents
+- built specifically for Claude Code
+
+repo: [https://github.com/iamcatdev/claude-code-heavy](https://github.com/iamcatdev/claude-code-heavy)
+
+**40/ ClaudeCodeAgents**
+
+QA-focused agent collection
+
+- 455+ stars
+- testing agents, validators
+- drop-in QA workflow
+
+repo: [https://github.com/webdevtodayjason/ClaudeCodeAgents](https://github.com/webdevtodayjason/ClaudeCodeAgents)
+
+## CATEGORY 5 - MCP SERVERS
+
+On its own, Claude Code runs in your terminal: it reads files, executes commands, and interacts with APIs. That’s it. It can’t work with your GitHub pull requests, query data from Postgres, open a browser, or read Figma frames.
+
+MCP servers solve this problem. They act as bridges connecting Claude to the rest of your stack via a standardized protocol, transforming the smart “programmer partner” into a true autonomous project participant.
+
+**41/ claude-code-mcp**
+
+Run Claude Code itself as an MCP server
+
+- enables agent-in-agent pattern
+- recursive delegation of coding tasks
+- integrates with Claude Desktop
+
+repo: [https://github.com/steipete/claude-code-mcp](https://github.com/steipete/claude-code-mcp)
+
+**42/ zilliztech/claude-context**
+
+Semantic code search MCP for massive codebases
+
+- pulls the right files without manual breadcrumbing
+- critical for big repos
+- community MCP server
+
+repo: [https://github.com/zilliztech/claude-context](https://github.com/zilliztech/claude-context)
+
+**43/ claude-context-mode**
+
+MCP server between Claude Code and outputs
+
+- 2.2k+ stars
+- 315 KB → 5.4 KB (98% reduction)
+- token savings on every call
+
+repo: [https://github.com/d-e-s-o/claude-context-mode](https://github.com/d-e-s-o/claude-context-mode)
+
+**44/ Continuous-Claude-v2**
+
+Context management for Claude Code
+
+- 2.2k+ stars
+- ledger-based hooks maintain state
+- MCP execution without context pollution
+
+repo: [https://github.com/continuous-claude/continuous-claude-v2](https://github.com/continuous-claude/continuous-claude-v2)
+
+**45/ context7**
+
+Up-to-date version-specific docs in prompts
+
+- real-time doc access
+- libraries + frameworks
+- works with Cursor, VS Code, Claude Code, Windsurf
+
+repo: [https://github.com/upstash/context7](https://github.com/upstash/context7)
+
+**46/ postgres-mcp**
+
+Safe PostgreSQL MCP server
+
+- read-only default mode
+- multi-connection support
+- defense-in-depth security
+
+repo: [https://github.com/crystaldba/postgres-mcp](https://github.com/crystaldba/postgres-mcp)
+
+**47/ playwright-mcp**
+
+Browser automation via MCP
+
+- navigate pages, fill forms
+- dynamic content scraping
+- official Microsoft-backed
+
+repo: [https://github.com/microsoft/playwright-mcp](https://github.com/microsoft/playwright-mcp)
+
+**48/ mcp-server-sqlite**
+
+Lightweight SQLite MCP for prototyping
+
+- local development friendly
+- simple install
+- official MCP team
+
+repo: [https://github.com/modelcontextprotocol/servers](https://github.com/modelcontextprotocol/servers)
+
+**49/ claude-code-mcp-enhanced**
+
+Enhanced claude-code-mcp with detailed instructions
+
+- emphatic prompts for compliance
+- improved task-following
+- by grahama1970
+
+repo: [https://github.com/grahama1970/claude-code-mcp-enhanced](https://github.com/grahama1970/claude-code-mcp-enhanced)
+
+**50/ danielrosehill/Claude-Code-MCP-List**
+
+Short curated MCP collection
+
+- MCPs for Linux desktop Claude Code
+- battle-tested selections
+- practical daily use
+
+repo: [https://github.com/danielrosehill/Claude-Code-MCP-List](https://github.com/danielrosehill/Claude-Code-MCP-List)
+
+## CATEGORY 6 - ORCHESTRATION & WORKFLOWS
+
+Once you’ve mastered Claude Code, the next hurdle will be yourself. You can only monitor one session at a time, approve only one plan, and run only one agent. Orchestration tools allow you to overcome this limitation.
+
+These repositories let you run parallel Claude sessions, coordinate multi-step workflows, preserve state between runs, and manage agents just as you would manage a small team of developers.
+
+**51/ Claude-Flow**
+
+Enterprise-grade AI orchestration platform
+
+- 11.4k+ stars
+- multi-agent orchestration with persistent memory
+- reshapes how teams build with AI
+
+repo: [https://github.com/ruvnet/claude-flow](https://github.com/ruvnet/claude-flow)
+
+**52/ claude-squad**
+
+Terminal multiplexer for AI coding agents
+
+- 5.6k+ stars
+- manages Claude Code, Aider, Codex, OpenCode, Amp
+- parallel agents without chaos
+
+repo: [https://github.com/smtg-ai/claude-squad](https://github.com/smtg-ai/claude-squad)
+
+**53/ claude\_code\_agent\_farm**
+
+Parallel Claude Code sessions orchestrator
+
+- runs multiple cc agents at scale
+- systematic codebase improvement
+- automation-first design
+
+repo: [https://github.com/Dicklesworthstone/claude\_code\_agent\_farm](https://github.com/Dicklesworthstone/claude_code_agent_farm)
+
+**54/ ccpm (Claude Code PM)**
+
+Project-management workflow for Claude Code
+
+- by Ran Aroussi
+- specialized agents + slash commands
+- turnkey setup
+
+repo: [https://github.com/automazeio/ccpm](https://github.com/automazeio/ccpm)
+
+**55/ cc-sessions**
+
+Opinionated extension set for Claude Code
+
+- 1.5k+ stars
+- hooks, subagents, commands
+- task/git management infrastructure
+
+repo: [https://github.com/cc-sessions/cc-sessions](https://github.com/cc-sessions/cc-sessions)
+
+**56/ claude-sessions**
+
+Custom slash commands for session tracking
+
+- 1.1k+ stars
+- comprehensive documentation
+- dev session logging
+
+repo: [https://github.com/iannuttall/claude-sessions](https://github.com/iannuttall/claude-sessions)
+
+**57/ claude-code-infrastructure-showcase**
+
+Infrastructure approach to Skills
+
+- hooks that activate skills contextually
+- smart skill routing
+- adaptable to any project
+
+repo: [https://github.com/diet103/claude-code-infrastructure-showcase](https://github.com/diet103/claude-code-infrastructure-showcase)
+
+**58/ infinite-agentic-loop**
+
+Infinite agentic loop demonstration
+
+- 492+ stars
+- two-prompt system design
+- experimental but educational
+
+repo: [https://github.com/disler/infinite-agentic-loop](https://github.com/disler/infinite-agentic-loop)
+
+**59/ agentsys**
+
+Automation harness for multiple tools
+
+- 473+ stars
+- 14 plugins, 43 agents, 30 skills
+- Claude Code + OpenCode + Codex
+
+repo: [https://github.com/agentsys/agentsys](https://github.com/agentsys/agentsys)
+
+**60/ Citadel**
+
+Agent orchestration harness
+
+- 430+ stars
+- parallel agent coordination
+- session memory persistence
+
+repo: [https://github.com/gabrieltonnellier/citadel](https://github.com/gabrieltonnellier/citadel)
+
+## CATEGORY 7 - MEMORY & CONTEXT
+
+Claude Code’s biggest drawback is also the one that’s easiest to fix: every session starts from scratch. Yesterday’s solutions, last week’s architecture discussions, the reasons you chose Postgres over MongoDB - all of that disappears the moment you close the terminal.
+
+Memory and context stores help solve this problem. They provide Claude with a persistent state across sessions - semantic memory, a condensed history, knowledge graphs of your codebase, as well as background agents that quietly accumulate knowledge while you work.
+
+**61/ thedotmack/claude-mem**
+
+Long-term memory via compression
+
+- persists context between sessions
+- lightweight and composable
+- solves the #1 Claude Code complaint
+
+repo: [https://github.com/thedotmack/claude-mem](https://github.com/thedotmack/claude-mem)
+
+**62/ hanfang/claude-memory-skill**
+
+Minimal hierarchical memory system
+
+- background agents + filesystem persistence
+- low-friction setup
+- works alongside existing skills
+
+repo: [https://github.com/hanfang/claude-memory-skill](https://github.com/hanfang/claude-memory-skill)
+
+**63/ 0xfurai/claude-subconscious**
+
+Background agent whispering guidance
+
+- 2.4k+ stars
+- watches sessions, reads files
+- builds memory over time
+
+repo: [https://github.com/0xfurai/claude-subconscious](https://github.com/0xfurai/claude-subconscious)
+
+**64/ claude-code-semantic-memory**
+
+Persistent semantic memory for Claude Code
+
+- 70+ stars
+- vector-based recall
+- session continuity
+
+repo: [https://github.com/dheerajoruganty/claude-code-semantic-memory](https://github.com/dheerajoruganty/claude-code-semantic-memory)
+
+**65/ claude-user-memory**
+
+User memory system with 12 specialist agents
+
+- 125+ stars
+- intelligent orchestration
+- auto-selects right specialist
+
+repo: [https://github.com/brennercruvinel/claude-user-memory](https://github.com/brennercruvinel/claude-user-memory)
+
+**66/ Repomix**
+
+Pack your codebase into one AI-readable file
+
+- 20.9k+ stars
+- XML, Markdown, plain text output
+- essential for large-repo context
+
+repo: [https://github.com/yamadashy/repomix](https://github.com/yamadashy/repomix)
+
+**67/ graphify**
+
+Interactive knowledge graph from codebases
+
+- explore, search, ask questions
+- works with Claude Code, Codex, Cursor
+- great for onboarding
+
+repo: [https://github.com/safishamsi/graphify](https://github.com/safishamsi/graphify)
+
+**68/ context-prime**
+
+Systematic project context priming
+
+- by elizaOS
+- scenario-specific priming commands
+- CLAUDE.md enhancement
+
+repo: [https://github.com/elizaOS/context-prime](https://github.com/elizaOS/context-prime)
+
+**69/ claude-code-heavy-research**
+
+Multi-agent research with parallel context
+
+- 72+ stars
+- deep-dive research workflows
+- agent-based knowledge gathering
+
+repo: [https://github.com/iamcatdev/claude-code-heavy](https://github.com/iamcatdev/claude-code-heavy)
+
+**70/ ledger**
+
+Context-preserving ledger system
+
+- hooks maintain ledger state
+- handoffs between agents
+- MCP without pollution
+
+repo: [https://github.com/continuous-claude/continuous-claude-v2](https://github.com/continuous-claude/continuous-claude-v2)
+
+## CATEGORY 8 - SLASH COMMANDS & HOOKS
+
+Every time you catch yourself typing the same instructions for Claude for the third time, you’ll come across a slash command that’s just begging to be used. /fix-issue 456, /review, /tdd - a single keystroke instead of a whole paragraph, every single time.
+
+Hooks go even further. They trigger automatically at key moments in your workflow, blocking commits without tests, automatically formatting code before a push, adding context when starting a session. You no longer need to remind Claude of your rules, because the rules are enforced automatically.
+
+**71/ wshobson/commands**
+
+57 production-ready slash commands
+
+- 1.7k+ stars
+- 15 workflows + 42 tools
+- multi-agent orchestration
+
+repo: [https://github.com/wshobson/commands](https://github.com/wshobson/commands)
+
+**72/ nizos/tdd-guard**
+
+Automated TDD enforcement for Claude Code
+
+- 1.7k+ stars
+- blocks skipping tests
+- explains why it blocked
+
+repo: [https://github.com/nizos/tdd-guard](https://github.com/nizos/tdd-guard)
+
+**73/ ccundo**
+
+Granular undo for Claude Code
+
+- 1.3k+ stars
+- seamless integration
+- action-level rollback
+
+repo: [https://github.com/RonitSachdev/ccundo](https://github.com/RonitSachdev/ccundo)
+
+**74/ claude-canvas**
+
+TUI toolkit giving Claude Code its own display
+
+- 1.1k+ stars
+- interactive terminal UI
+- rich visualization
+
+repo: [https://github.com/claude-canvas/claude-canvas](https://github.com/claude-canvas/claude-canvas)
+
+**75/ ClaudoPro Directory**
+
+Wide selection of hooks, commands, subagents
+
+- better than average template sites
+- specialized task coverage
+- curated by ghost
+
+repo: [https://github.com/ClaudoPro/claudopro-directory](https://github.com/ClaudoPro/claudopro-directory)
+
+**76/ danielrosehill/Claude-Slash-Commands**
+
+Curated custom slash commands
+
+- organized by category
+- pre-push hook for sync
+- index-based discovery
+
+repo: [https://github.com/danielrosehill/Claude-Slash-Commands](https://github.com/danielrosehill/Claude-Slash-Commands)
+
+**77/ claude-commands/command-fix-issue**
+
+TDD-based GitHub issue fixer
+
+- /fix-issue 456
+- Red → Green → Refactor
+- creates PR automatically
+
+repo: [https://github.com/claude-commands/command-fix-issue](https://github.com/claude-commands/command-fix-issue)
+
+**78/ Design Review Workflow**
+
+Automated UI/UX design review
+
+- by Patrick Ellis
+- responsive + accessibility checks
+- specialized subagents
+
+repo: [https://github.com/patrick-ellis/design-review-workflow](https://github.com/patrick-ellis/design-review-workflow)
+
+**79/ tdd-implement**
+
+TDD slash command by jerseycheese
+
+- Red/Green/Refactor automation
+- test-first enforcement
+- feature-to-test breakdown
+
+repo: [https://github.com/jerseycheese/tdd-implement](https://github.com/jerseycheese/tdd-implement)
+
+**80/ security-review**
+
+Official security review slash command
+
+- scans PRs for issues
+- suggests fixes
+- easy to disable
+
+repo: [https://github.com/anthropics/claude-code-security-review](https://github.com/anthropics/claude-code-security-review)
+
+## CATEGORY 9 - GUIDES & LEARNING
+
+Claude Code has its own complexities that nobody talks about. The command line is simple—but what about developing prompts, creating skills, configuring MCP, designing hooks, managing context, and token economics?
+
+It is at this stage that most developers get stuck and start blaming the tool.
+
+Guides and training materials help overcome this barrier. Detailed reference guides, hint development guides, system hint archives, pattern encyclopedias - all of which would take months to learn on your own.
+
+**81/ ThamJiaHe/claude-prompt-engineering-guide**
+
+Definitive prompt engineering guide
+
+- covers Opus 4.6, Sonnet 4.6, Haiku 4.5
+- Skills, Plugins, MCP, Hooks, Ultrathink
+- 220+ verified sources
+
+repo: [https://github.com/ThamJiaHe/claude-prompt-engineering-guide](https://github.com/ThamJiaHe/claude-prompt-engineering-guide)
+
+**82/ nikiforovall/claude-code-handbook**
+
+Best practices for Claude Code workflows
+
+- distributable plugins included
+- daily-use patterns
+- battle-tested tips
+
+repo: [https://github.com/nikiforovall/claude-code-handbook](https://github.com/nikiforovall/claude-code-handbook)
+
+**83/ FlorianBruniaux/claude-code-ultimate-guide**
+
+Massive 22,000+ line documentation project
+
+- 225 production templates
+- 9 AI personas + 26 slash commands
+- 271 quiz questions
+
+repo: [https://github.com/FlorianBruniaux/claude-code-ultimate-guide](https://github.com/FlorianBruniaux/claude-code-ultimate-guide)
+
+**84/ Piebald-AI/claude-code-system-prompts**
+
+All Claude Code system prompts
+
+- updated within minutes of each release
+- tool descriptions + sub-agent prompts
+- CHANGELOG across 155+ versions
+
+repo: [https://github.com/Piebald-AI/claude-code-system-prompts](https://github.com/Piebald-AI/claude-code-system-prompts)
+
+**85/ Encyclopedia of Agentic Coding Patterns**
+
+190+ patterns for AI-assisted development
+
+- by Wolf McNally
+- foundational → agentic construction
+- governance + testing
+
+repo: [https://github.com/wolfmcnally/Encyclopedia-of-Agentic-Coding-Patterns](https://github.com/wolfmcnally/Encyclopedia-of-Agentic-Coding-Patterns)
+
+**86/ RiyaParikh0112/claude-code-playbook**
+
+Claude Code playbook with TDD workflows
+
+- integration test patterns
+- CI/CD pipeline configs
+- PR prep guides
+
+repo: [https://github.com/RiyaParikh0112/claude-code-playbook](https://github.com/RiyaParikh0112/claude-code-playbook)
+
+**87/ 40+ Claude Code Tips**
+
+Power-user tips collection
+
+- custom status line scripts
+- system prompt halving tricks
+- Claude Code in containers
+
+repo: [https://github.com/iannuttall/claude-code-tips](https://github.com/iannuttall/claude-code-tips)
+
+**88/ awesome-claude-prompts topic**
+
+Topic-based discovery of prompt repos
+
+- community-submitted repos
+- tagged for navigation
+- fresh additions regularly
+
+repo: [https://github.com/topics/awesome-claude-prompts](https://github.com/topics/awesome-claude-prompts)
+
+**89/ Context Priming by disler**
+
+Systematic context priming approach
+
+- scenario-specific commands
+- project context patterns
+- reference templates
+
+repo: [https://github.com/disler/context-priming](https://github.com/disler/context-priming)
+
+**90/ My Experience With Claude Code (2 weeks)**
+
+Real-world lessons and workflows
+
+- TODO.md discipline
+- cost management tips
+- refactor strategies
+
+repo: [https://github.com/shaharia-lab/my-claude-code-journey](https://github.com/shaharia-lab/my-claude-code-journey)
+
+## CATEGORY 10 - IDE INTEGRATIONS & DESKTOP
+
+By default, Claude Code runs in the terminal which is convenient if you’re always working in the terminal, but not so much if you’re not.
+
+Most developers want Claude to work directly within the editor they’re already using, rather than in a separate window that distracts them.
+
+Integration with an IDE solves this problem. Extensions for VS Code, plugins for Neovim, modes for Emacs, and even desktop shells integrate Claude into any of your existing workflows.
+
+**91/ coder/claudecode.nvim**
+
+First Neovim IDE integration for Claude Code
+
+- pure Lua, zero dependencies
+- WebSocket-based MCP protocol
+- reverse-engineered from official extensions
+
+repo: [https://github.com/coder/claudecode.nvim](https://github.com/coder/claudecode.nvim)
+
+**92/ greggh/claude-code.nvim**
+
+Seamless Neovim + Claude Code integration
+
+- toggle terminal window
+- \--continue + custom variants
+- auto-reload modified files
+
+repo: [https://github.com/greggh/claude-code.nvim](https://github.com/greggh/claude-code.nvim)
+
+**93/ manzaltu/claude-code-ide.el**
+
+Emacs integration for Claude Code
+
+- ediff-based suggestions
+- LSP/flymake/flycheck diagnostics
+- tree-sitter AST queries
+
+repo: [https://github.com/manzaltu/claude-code-ide.el](https://github.com/manzaltu/claude-code-ide.el)
+
+**94/ stevemolitor/claude-code.el**
+
+Emacs interface for Claude Code CLI
+
+- simple terminal wrapper
+- lightweight integration
+- idiomatic Emacs
+
+repo: [https://github.com/stevemolitor/claude-code.el](https://github.com/stevemolitor/claude-code.el)
+
+**95/ Haleclipse/Claudix**
+
+VSCode extension for Claude Code
+
+- interactive chat interface
+- session management
+- Vue 3 + TypeScript
+
+repo: [https://github.com/Haleclipse/Claudix](https://github.com/Haleclipse/Claudix)
+
+**96/ Clawd on Desk**
+
+Desktop pet reacting to Claude sessions
+
+- thinking, typing, juggling, sleeping
+- real-time session awareness
+- pure fun, zero productivity
+
+repo: [https://github.com/Ruller-Lulu/clawd-on-desk](https://github.com/Ruller-Lulu/clawd-on-desk)
+
+**97/ Claude Desktop Debian**
+
+Unofficial Claude desktop app for Linux
+
+- Debian-native package
+- community-maintained
+- fills the Linux gap
+
+repo: [https://github.com/aaddrick/claude-desktop-debian](https://github.com/aaddrick/claude-desktop-debian)
+
+**98/ instructkr/claw-code**
+
+Korean-built Claude Code alternative/tooling
+
+- localized workflows
+- lightweight install
+- community project
+
+repo: [https://github.com/instructkr/claw-code](https://github.com/instructkr/claw-code)
+
+## CATEGORY 11 - SPECIALIZED WORKFLOWS
+
+Not all Claude Code repositories can be clearly classified into a specific category. Some are designed to perform a single, specific task. For example, UI/UX tools focused exclusively on design patterns, a real-time directory that tracks the entire ecosystem, or niche tools that excel at solving just one particular problem.
+
+**99/ nextlevelbuilder/ui-ux-pro-max-skill**
+
+UI/UX skill for design-focused workflows
+
+- structured design patterns
+- component-oriented guidance
+- production-grade output
+
+repo: [https://github.com/nextlevelbuilder/ui-ux-pro-max-skill](https://github.com/nextlevelbuilder/ui-ux-pro-max-skill)
+
+**100/ ClaudeWave**
+
+Real-time directory of 3800+ Claude repos
+
+- 8 categories, tagged use-cases
+- ranked by stars + velocity
+- updated every 12 hours
+
+repo: [https://github.com/claudewave/claudewave](https://github.com/claudewave/claudewave)
+
+This isn’t the full picture, no one has the full picture. The Claude Code ecosystem is evolving too quickly for anyone to keep up with everything. But if you install just 5–10 of the repositories listed above, you’ll already be ahead of 95% of the users who pay for the same Claude subscription as you. More AI deep-dives and ecosystem breakdowns I post in my Telegram: [https://t.me/+LorUR\_GakbViOTQy](https://t.me/+LorUR_GakbViOTQy)
\ No newline at end of file
diff --git a/Claude/Lessons from Building Claude Code Seeing like an Agent.md b/Claude/Lessons from Building Claude Code Seeing like an Agent.md
new file mode 100644
index 0000000..e2c1b82
--- /dev/null
+++ b/Claude/Lessons from Building Claude Code Seeing like an Agent.md
@@ -0,0 +1,108 @@
+---
+title: "Lessons from Building Claude Code: Seeing like an Agent"
+source: "https://x.com/trq212/status/2027463795355095314?s=20"
+author:
+ - "[[Unknown]]"
+published: 2026-02-28
+created: 2026-03-14
+description:
+tags:
+ - "clippings"
+---
+One of the hardest parts of building an agent harness is constructing its action space.
+
+Claude acts through Tool Calling, but there are a number of ways tools can be constructed in the Claude API with primitives like bash, skills and recently code execution (read more about programmatic tool calling on the Claude API in [@RLanceMartin's new article](https://x.com/RLanceMartin/status/2027450018513490419)).
+
+Given all these options, how do you design the tools of your agent? Do you need just one tool like code execution or bash? What if you had 50 tools, one for each use case your agent might run into?
+
+To put myself in the mind of the model I like to imagine being given a difficult math problem. What tools would you want in order to solve it? It would depend on your own skills!
+
+Paper would be the minimum, but you’d be limited by manual calculations. A calculator would be better, but you would need to know how to operate the more advanced options. The fastest and most powerful option would be a computer, but you would have to know how to use it to write and execute code.
+
+This is a useful framework for designing your agent. You want to give it tools that are shaped to its own abilities. But how do you know what those abilities are? You pay attention, read its outputs, experiment. You learn to see like an agent.
+
+Here are some lessons we’ve learned from paying attention to Claude while building Claude Code.
+
+# Improving Elicitation & the AskUserQuestion tool
+
+
+
+When building the AskUserQuestion tool, our goal was to improve Claude’s ability to ask questions (often called elicitation).
+
+While Claude could just ask questions in plain text, we found answering those questions felt like they took an unnecessary amount of time. How could we lower this friction and increase the bandwidth of communication between the user and Claude?
+
+## Attempt #1 - Editing the ExitPlanTool
+
+The first thing we tried was adding a parameter to the ExitPlanTool to have an array of questions alongside the plan. This was the easiest thing to implement, but it confused Claude because we were simultaneously asking for a plan and a set of questions about the plan. What if the user’s answers conflicted with what the plan said? Would Claude need to call the ExitPlanTool twice? We needed another approach.
+
+(you can read more about why we made an ExitPlanTool in [our post on prompt caching](https://x.com/trq212/status/2024574133011673516))
+
+## Attempt #2 - Changing Output Format
+
+Next we tried modifying Claude’s output instructions to serve a slightly modified markdown format that it could use to ask questions. For example, we could ask it to output a list of bullet point questions with alternatives in brackets. We could then parse and format that question as UI for the user.
+
+While this was the most general change we could make and Claude even seemed to be okay at outputting this, it was not guaranteed. Claude would append extra sentences, omit options, or use a different format altogether.
+
+## Attempt #3 - The AskUserQuestion Tool
+
+
+
+Finally, we landed on creating a tool that Claude could call at any point, but it was particularly prompted to do so during plan mode. When the tool triggered we would show a modal to display the questions and block the agent's loop until the user answered.
+
+This tool allowed us to prompt Claude for a structured output and it helped us ensure that Claude gave the user multiple options. It also gave users ways to compose this functionality, for example calling it in the Agent SDK or using referring to it in skills.
+
+Most importantly, Claude seemed to like calling this tool and we found its outputs worked well. Even the best designed tool doesn’t work if Claude doesn’t understand how to call it.
+
+Is this the final form of elicitation in Claude Code? We’re not sure. As you’ll see in the next example, what works for one model may not be the best for another.
+
+# Updating with Capabilities - Tasks & Todos
+
+
+
+When we first launched Claude Code, we realized that the model needed a Todo list to keep it on track. Todos could be written at the start and checked off as the model did work. To do this we gave Claude the TodoWrite tool, which would write or update Todos and display them to the user.
+
+But even then we often saw Claude forgetting what it had to do. To adapt, we inserted system reminders every 5 turns that reminded Claude of its goal.
+
+But as models improved, they not only did not need to be reminded of the Todo List but could find it limiting. Being sent reminders of the todo list made Claude think that it had to stick to the list instead of modifying it. We also saw Opus 4.5 also get much better at using subagents, but how could subagents coordinate on a shared Todo List?
+
+Seeing this, we replaced TodoWrite with the Task Tool ([read more on Tasks here](https://x.com/trq212/status/2014480496013803643)). Whereas Todos were about keeping the model on track, Tasks were more about helping agents communicate with each other. Tasks could include dependencies, share updates across subagents and the model could alter and delete them.
+
+As model capabilities increase, the tools that your models once needed might now be constraining them. It’s important to constantly revisit previous assumptions on what tools are needed. This is also why it's useful to stick to a small set of models to support that have a fairly similar capabilities profile.
+
+# Designing a Search Interface
+
+A particularly important set of tools for Claude are the search tools that can be used to build its own context.
+
+When Claude Code first came out, we used a RAG vector database to find context for Claude. While RAG was powerful and fast it required indexing and setup and could be fragile across a host of different environments. More importantly, Claude was given this context instead of finding the context itself.
+
+But if Claude could search on the web, why not search your codebase? By giving Claude a Grep tool, we could let it search for files and build context itself.
+
+This is a pattern we’ve seen as Claude gets smarter, it becomes increasingly good at building its context if it’s given the right tools.
+
+When we introduced Agent Skills we formalized the idea of progressive disclosure, which allows agents to incrementally discover relevant context through exploration.
+
+Claude could read skill files and those files could then reference other files that the model could read recursively. In fact, a common use of skills is to add more search capabilities to Claude like giving it instructions on how to use an API or query a database.
+
+Over the course of a year Claude went from not really being able to build its own context, to being able to do nested search across several layers of files to find the exact context it needed.
+
+Progressive disclosure is now a common technique we use to add new functionality without adding a tool.
+
+# Progressive Disclosure - The Claude Code Guide Agent
+
+Claude Code currently has ~20 tools, and we are constantly asking ourselves if we need all of them. The bar to add a new tool is high, because this gives the model one more option to think about.
+
+For example, we noticed that Claude did not know enough about how to use Claude Code. If you asked it how to add a MCP or what a slash command did, it would not be able to reply.
+
+We could have put all of this information in the system prompt, but given that users rarely asked about this, it would have added context rot and interfered with Claude Code’s main job: writing code.
+
+Instead, we tried a form of progressive disclosure. We gave Claude a link to its docs which it could then load to search for more information. This worked but we found that Claude would load a lot of results into context to find the right answer when really all you needed was the answer.
+
+So we built the Claude Code Guide subagent which Claude is prompted to call when you ask about itself, the subagent has extensive instructions on how to search docs well and what to return.
+
+While this isn’t perfect, Claude can still get confused when you ask it about how to set itself up, it is much better than it used to be! We were able to add things to Claude's action space without adding a tool.
+
+## An Art, not a Science
+
+If you were hoping for a set of rigid rules on how to build your tools, unfortunately that is not this guide. Designing the tools for your models is as much an art as it is a science. It depends heavily on the model you're using, the goal of the agent and the environment it’s operating in.
+
+Experiment often, read your outputs, try new things. See like an agent.
\ No newline at end of file
diff --git a/Claude/Master Claude Skills The Complete Guide (Build, Scale & Automate in Minutes).md b/Claude/Master Claude Skills The Complete Guide (Build, Scale & Automate in Minutes).md
new file mode 100644
index 0000000..73b6992
--- /dev/null
+++ b/Claude/Master Claude Skills The Complete Guide (Build, Scale & Automate in Minutes).md
@@ -0,0 +1,458 @@
+---
+title: "Master Claude Skills: The Complete Guide (Build, Scale & Automate in Minutes)"
+source: "https://x.com/Ai_here202/status/2047247022928060734"
+author:
+ - "[[@Ai_here202]]"
+published: 2026-04-23
+created: 2026-04-24
+description: "Master Claude Skills: The Complete Guide (Build, Scale & Automate in Minutes)I’ve taken the best insights available on Claude Skills and tur..."
+tags:
+ - "clippings"
+---
+
+
+Master Claude Skills: The Complete Guide (Build, Scale & Automate in Minutes)
+
+I’ve taken the best insights available on Claude Skills and turned them into a single, structured guide.
+
+If you go through this carefully, you won’t just understand Claude Skills—
+
+> You’ll be able to build, test, and deploy your own system in minutes… and use it like a pro.
+
+This is not theory. This is a **practical system for real-world use**.
+
+# 🧠 What is a Claude Skill?
+
+At its core:
+
+> A Claude Skill is a persistent instruction system that tells Claude exactly how to perform a task—step by step.
+
+Instead of repeating instructions every time, you define them once.
+
+And from that point on, the task becomes automatic.
+
+# ⚙️ Where Skills Fit (Skills vs Projects vs MCP)
+
+Before building anything, you need clarity.
+
+There are three core components:
+
+## 📚 Projects → Knowledge Layer
+
+Projects store information.
+
+- PDFs
+- Documents
+- Guidelines
+
+They tell Claude:
+
+> “Here’s what you need to know.”
+
+They are static and reference-based.
+
+## ⚡ Skills → Execution Layer
+
+Skills define how tasks are performed.
+
+They tell Claude:
+
+> “Here’s exactly how to do this.”
+
+- Step-by-step workflows
+- Repeatable processes
+- Consistent outputs
+
+Think of them as trained assistants.
+
+## 🔗 MCP → Connection Layer
+
+MCP connects Claude to external systems:
+
+- Calendars
+- Databases
+- Emails
+
+Skills then decide how to use that data.
+
+## 🎯 When Should You Build a Skill?
+
+Simple rule:
+
+- If you repeat the same instructions multiple times
+- Or need consistent outputs
+
+→ Build a Skill.
+
+# 🧩 The Structure of a Skill
+
+Most people overcomplicate this.
+
+The reality is simple:
+
+> A Skill = a folder + a single instruction file
+
+## 📁 Basic Setup
+
+your-skill-name/ ├── SKILL.md └── references/ (optional)
+
+🔹 Naming Rules
+
+Use kebab-case:
+
+- email-writer
+- csv-cleaner
+- content-generator
+
+Avoid spaces, underscores, or capital letters.
+
+🔹 SKILL.md = The Brain
+
+This file contains:
+
+- Instructions
+- Triggers
+- Workflow
+
+It must be named exactly:
+
+SKILL.md
+
+🔹 references/ (Optional)
+
+Use this for:
+
+- Large templates
+- Brand guidelines
+- Supporting material
+
+# 📍 Where Skills Run
+
+There are two main environments:
+
+## 🖥 Claude Code
+
+- For developers
+- Runs scripts
+- Accesses file systems
+
+## 💻 Claude Desktop
+
+- For non-technical users
+- Works through apps and UI
+
+# ⚙️ Building Your First Skill
+
+## 🧭 Step 1: Define the Job
+
+Before writing anything, answer:
+
+What does the Skill do?
+
+Be specific.
+
+❌ “Helps with data” ✅ “Cleans CSV files, fixes headers, standardizes date formats, removes empty rows”
+
+When should it trigger?
+
+Think like a user:
+
+- “Clean this CSV”
+- “Fix this spreadsheet”
+- “Format this data”
+
+What does success look like?
+
+Provide a **before vs after example**
+
+📌 Most Skills fail here due to vagueness.
+
+## ⚡ Step 2: YAML Triggers
+
+At the top of your file:
+
+\--- metadata ---
+
+Rules:
+
+- Use third-person language
+- Define exact trigger phrases
+- Add negative boundaries
+
+📌 The most important field:
+
+> description
+
+Too vague → won’t activate Too broad → activates incorrectly
+
+## 🛠 Step 3: Write Instructions
+
+Use:
+
+- Plain English
+- Step-by-step logic
+- Clear structure
+
+Example Flow:
+
+- Read input
+- Identify structure
+- Clean data
+- Format output
+- Generate result
+
+🔥 Include Examples
+
+One strong example > long explanations
+
+## 📂 Step 4: References
+
+Store large files in references/
+
+Rule:
+
+> Keep references one level deep
+
+No nested linking.
+
+## 🚀 Step 5: Deploy
+
+Move your folder to:
+
+~/.claude/skills/
+
+Done.
+
+# 🧠 Going Advanced: Architecture
+
+Once you build multiple Skills, complexity increases.
+
+## ⚙️ When Instructions Are Not Enough
+
+Some tasks require:
+
+- Computation
+- Data processing
+- File handling
+
+## 🧩 Add the scripts/ Layer
+
+Now your Skill becomes:
+
+your-skill/ ├── SKILL.md ├── scripts/ └── references/
+
+Key Idea:
+
+> Instructions decide Scripts execute
+
+## 📌 When to Use What
+
+Instructions:
+
+- Writing
+- Formatting
+- Decision-making
+
+Scripts:
+
+- Calculations
+- Parsing data
+- File manipulation
+
+Both:
+
+- Process data → then explain results
+
+## 🧠 Script Best Practices
+
+- One script = one task
+- Use arguments (no hardcoding)
+- Add error handling
+- Document everything
+
+# 🔄 Multi-Skill Systems
+
+With multiple Skills, conflicts appear.
+
+## ⚠️ The Problem
+
+Two Skills may:
+
+- Trigger on the same input
+- Compete with each other
+
+## 🧠 How Selection Works
+
+Claude:
+
+1. Reads all Skill descriptions
+2. Matches them with your input
+3. Picks the best match
+
+## ✅ Rules to Avoid Conflicts
+
+1\. Clear Boundaries
+
+Each Skill must have a distinct role.
+
+2\. Negative Constraints
+
+Define what the Skill should NOT do.
+
+3\. Unique Triggers
+
+Avoid overlapping phrases.
+
+📌 Most issues come from poor descriptions—not logic.
+
+# 🧪 Testing & Iteration
+
+A working Skill is not a reliable Skill.
+
+## ⚠️ Common Mistake
+
+- Test once
+- Assume it works
+
+## 🧠 The 5 Failure Modes
+
+1\. Silent Skill
+
+Doesn’t activate → Fix: improve triggers
+
+2\. Hijacker
+
+Activates incorrectly → Fix: tighten scope
+
+3\. Drifter
+
+Wrong output → Fix: clarify instructions
+
+4\. Fragile Skill
+
+Breaks on edge cases → Fix: handle exceptions
+
+5\. Overachiever
+
+Adds unnecessary output → Fix: add constraints
+
+## 🧪 Proper Testing System
+
+Use Test Prompts
+
+Define:
+
+- Input
+- Expected output
+
+Track Performance
+
+- Accuracy
+- Speed
+- Consistency
+
+A/B Test
+
+Compare versions objectively.
+
+Optimize Triggers
+
+Ensure activation works across variations.
+
+## 🔁 Iteration Loop
+
+Build → Test → Fix → Repeat
+
+📌 Stop when:
+
+> No meaningful improvement across multiple tests
+
+# 🚀 Production Deployment
+
+Now comes the final step:
+
+## ⚠️ The Problem: No Memory
+
+Claude does not retain long-term context.
+
+## 🔁 The Solution: State Management
+
+Use a file like:
+
+context-log.md
+
+## ⚙️ System Design
+
+At Session Start:
+
+- Read previous context
+- Understand progress
+
+At Session End:
+
+- Save summary
+- Define next steps
+
+## 🏥 Think of It Like a Handover
+
+Each session continues from where the last one ended.
+
+# 🏗️ Building Long-Term Systems
+
+Your Skill should support:
+
+Multi-Step Projects
+
+- Writing
+- Development
+- Research
+
+Iterative Work
+
+- Draft → refine → improve
+
+Structured Outputs
+
+- Consistent formats
+- Clear organization
+
+# 🔄 From Skills → Systems
+
+At scale, you don’t have one Skill.
+
+You have a system.
+
+## 📌 Example Workflow
+
+- Research Skill
+- Writing Skill
+- Editing Skill
+- Formatting Skill
+
+Each handles one job.
+
+Together, they create a pipeline.
+
+# ⚡ The Real Advantage
+
+## ❌ Without Skills
+
+- Repetition
+- Inconsistency
+- Time loss
+
+## ✅ With Skills
+
+- Automation
+- Consistency
+- Speed
+
+📌 The difference compounds over time.
+
+# 🧠 Final Perspective
+
+At the start, Claude feels like a chatbot.
+
+But once you build Skills:
+
+> You’re no longer chatting You’re operating a custom-built system
\ No newline at end of file
diff --git a/Claude/Obsidian + Claude Code：你的笔记库就是你最强大的 AI 上下文.md b/Claude/Obsidian + Claude Code：你的笔记库就是你最强大的 AI 上下文.md
new file mode 100644
index 0000000..6494898
--- /dev/null
+++ b/Claude/Obsidian + Claude Code：你的笔记库就是你最强大的 AI 上下文.md
@@ -0,0 +1,155 @@
+---
+title: "Obsidian + Claude Code：你的笔记库就是你最强大的 AI 上下文"
+source: "https://x.com/yanhua1010/status/2029369274847072591"
+author:
+ - "[[Unknown]]"
+published: 2026-03-05
+created: 2026-03-05
+description:
+tags:
+ - "clippings"
+---
+Greg Isenberg 最近请了 Internet Vin 上他的播客，聊了一个多小时 Obsidian + Claude Code。
+
+
+
+我听完之后最大的感受不是「又一个效率工具教程」，而是：**这个人把笔记库变成了 AI 的氧气。**
+
+Greg 本人听到一半就当场下载了 Obsidian，说了一句让我印象极深的话：
+
+> 如果你认真地想构建个人操作系统，而你没有使用以 Markdown 为基础的集中式笔记工具，那你就没有正确地使用 LLM。
+
+这话说得重，但我越想越觉得他说到了点上。
+
+## 99% 的人用 AI 的方式是反的
+
+大多数人用 AI 的习惯是这样的：打开 ChatGPT 或 Claude，从零开始解释「我是谁、我在做什么、我需要什么」，聊完关掉，下次重新来。
+
+Vin 说得很直白：这就是问题所在。
+
+**你每次给 AI 的上下文越薄，它能为你做的事就越浅。** 你反复解释同一个项目，反复描述同一个偏好，反复校正同一个错误。像极了一个永远不记得你说过什么的新同事。
+
+而 Obsidian 解决的就是这个问题。它不是一个普通的笔记软件，它是一个**由 Markdown 文件组成的、带有双向链接的知识网络**。每一篇笔记不是孤岛，而是通过 \[\[链接\]\] 与其他笔记形成关系。
+
+当你把 Claude Code 接入这个网络，事情就变了。
+
+
+
+## 你的笔记库 = AI 的「完美记忆」
+
+Vin 在播客里演示了一个让 Greg 目瞪口呆的场景。
+
+他输入了一个自定义命令 /trace，让 Claude Code 追踪他与 Obsidian 这个工具的「关系演化史」。Claude Code 开始扫描他整个笔记库，读了几十个文件，跟踪了文件之间的链接关系，然后输出了一份完整的时间线：
+
+- 2024 年 12 月，他的笔记系统里根本没有 Obsidian
+- 2025 年 1 月，他开始用 Obsidian，但对双向链接持怀疑态度
+- 2025 年中，他意识到应该为「模式」和「理论」创建独立笔记，而不是反向链接到泛化标签
+- 2026 年 1 月，他进入了爆发式构建期，瓶颈不再是 Obsidian 本身，而是 vault 和 agent 之间的执行边界
+
+Vin 自己看完都说：**这件事我一个人做不到。阅读所有这些文件，理解它们之间的关联，作为一个人类来说根本不可能。**
+
+Greg 在旁边补了一刀：
+
+> 文件本质上是完美的记忆。人类的记忆研究反复证明，我们记住的东西跟现实完全是两回事。但一个 Markdown 文件不会说谎。
+
+这句话值得反复咀嚼。你的笔记库里存的不是「笔记」，而是你思维的快照。当 AI 能读到这些快照，它比你自己更清楚你在想什么。
+
+
+
+## 从反思到行动：笔记库如何变成「想法工厂」
+
+很多人对 Obsidian 的理解停留在「做笔记的地方」。Vin 做的事情远不止于此。
+
+他给 Claude Code 创建了一系列自定义命令，每个命令都在做一件事：**从笔记库里挖出你自己都没意识到的东西。**
+
+这几个最让我震撼：
+
+**/connect 跨域连接。** 你给它两个看起来毫无关系的关键词，比如「电影制作」和「世界构建」，它会遍历整个笔记库的链接图谱，找到隐藏的桥梁。Vin 试了一下，AI 从他的笔记中发现：电影采访本质上是「打开一个人内心世界的门户」，而他的博客愿景是「像古埃及陵墓一样被后人挖掘和审视」。两者的共同模式是：构建一个持久的、可被探索的世界。
+
+他自己都没意识到这层联系。
+
+**/challenge 压力测试。** 它用你笔记库自己的历史来质疑你当前的观点。找出矛盾、反证、思维转变。如果你说你相信 X，但过去六个月的笔记里你其实在不断否定 X，它会直接指出来。
+
+**/ideas 创意生成。** 这个命令做了一次深度的 30 天笔记库扫描，跨领域模式检测加图谱分析，产出了一份完整的创意报告。不只是「你应该做什么项目」，还包括「你应该看什么电影」「你应该跟谁聊聊」「你一直在回避什么」。
+
+Greg 看完这份报告直接说了一句：**这太疯狂了。**
+
+
+
+## 关键洞察：不是管理 AI，而是管理你的笔记库
+
+整个播客最让我顿悟的一个观点来自 Vin 讲他跟 OpenClaw（一个自主 Agent）的协作方式：
+
+> 我不再管理 agent。我管理我的 vault。这才是新的信息源。我只需要不断确保这个 vault 拥有所有必要的信息，这样我就可以委托给 agent，agent 直接从 vault 中提取信息并做出决定。如果它决策不对，我改的是 vault，不是 agent。
+
+读到这里我停下来想了很久。
+
+大多数人用 AI 的思路是「怎么跟 AI 说话」。Vin 的思路是「怎么写好我的笔记库，让 AI 自己去找答案」。
+
+这个思维转换太重要了。**你的笔记库不是给你看的备忘录，而是 AI 理解你的唯一接口。** 你写得越深、越结构化、越持续更新，AI 就越能代表「此刻的你」来做决策。
+
+Vin 举了个例子。他输入 /schedule，问「我今天下午 2 点能跟 Greg Isenberg 开会吗」。Claude Code 不只看了日历，还翻了他的每日笔记，然后回复：
+
+> 你的一天已经排满了。今天早上你在录 Greg 的播客，接着团队午餐，然后跟 Peter 和 Vince 开会。而且你 2 月 17 日的笔记显示 Greg 那期一直是你最关注的事。建议：你可能根本不需要单独的会议。
+
+一个「知道你在想什么」的 AI 日程助手，和一个「只看日历」的助手，给出的建议是完全不同的维度。
+
+
+
+## 为什么反思和写作是最被低估的 AI 技能
+
+Vin 在播客里说了一段让我很有共鸣的话：
+
+> 我喜欢做笔记，因为这是我产生想法的方式。当我坐下来写东西的时候，灵感就来了。通过写出来，我更能内化它。如果你想培养写作的习惯，首先要把它跟一个信念连接起来：写作就是你进步的方式，就是你产生原创想法的方式。
+
+然后他补充了一个 AI 时代的新视角：
+
+> **写作现在还是你委派任务给 agent 的最主要方式。** 你能写下越多关于自己项目、偏好、思考的文字，你能委托给 agent 的事情就越多，你能构建的东西也越多。
+
+这让我重新理解了「写作」这件事的价值。它不只是整理思路，不只是输出内容，它还是在给 AI 构建你的数字镜像。
+
+你每天写的每一条笔记，都在让 AI 更懂你。
+
+## 隐私的另一面：你愿意给 AI 看到多少？
+
+Greg 在播客里提了一个尖锐的问题：如果 Obsidian 是你的第二个大脑，那让一个自主 Agent 完全访问你的第二个大脑，不可怕吗？
+
+Vin 很坦诚：可怕。
+
+> 你必须认真思考你与这些 agent 分享了多少信息，以及这是否是正确的决定。我是故意给 Claude Code 访问大量信息的，因为我想理解这些东西是什么，想理解它们揭示了什么。但这很奇怪。
+
+他还提到一个细节：为了这期播客，他不得不给每个命令创建一个 demo 版本，避免暴露太多个人信息。即便如此，他依然无法完全控制屏幕上会显示什么。
+
+这是一个真实的权衡。**笔记库越丰富，AI 越强大。但笔记库越丰富，你暴露给 AI 的也越多。** 怎么划这条线，是每个人都需要认真想的问题。
+
+Vin 的策略是严格的读写分离：**他只让 AI 读笔记库，不让 AI 写入。** 因为一旦 AI 开始在笔记库里创建自己的文件，那些「模式发现」就不再是纯粹基于你的思考了，可能混入了 AI 自己的推理。
+
+## 对我个人的启发
+
+听完这期播客，再回头看我自己的 Obsidian + Claude Code 实践，我有三个新的认知：
+
+**第一，上下文的质量决定 AI 的上限。** 这不是新观点，但 Vin 用实践证明了「好上下文」到底意味着什么：不是一份精心写好的 prompt，而是数月甚至数年的持续写作积累，加上结构化的链接关系。Token 不是 AI 的氧气，你的 Markdown 文件才是。
+
+**第二，让 AI 帮你发现你自己。** 这是我之前完全忽略的用法。我一直把 AI 当「干活工具」，让它写文章、生成图片、自动发布。Vin 让我看到了另一个维度：AI 可以成为你的思考镜子，帮你看见自己思维中的模式、盲区和演变轨迹。
+
+**第三，管理笔记库比管理 AI 更重要。** 与其花时间研究怎么写更好的 prompt，不如花时间写更好的笔记。笔记库是根，prompt 是叶。根深了，叶子自然茂盛。
+
+
+
+## 怎么开始？
+
+如果你现在还没有用 Obsidian，三步就可以开始：
+
+1. **下载 Obsidian**（免费），创建你的第一个 Vault
+2. **开始写**，每天一条笔记就够了。反思、想法、学到的东西，什么都行。关键是持续写
+3. **用 \[\[双向链接\]\]** 把相关的笔记连起来。不用追求完美的结构，先连起来再说
+
+如果你已经在用 Obsidian，想接入 Claude Code，核心就是一件事：**写一个 CLAUDE.md 文件**，告诉 AI 你是谁、你怎么工作、你要什么标准。哪怕只有 10 行，它就能让 AI 从「通用助手」变成「懂你的搭档」。
+
+Greg 在播客最后说：
+
+> 99.99% 的人不会花时间真正去设置类似的东西。这恰恰意味着，那些愿意花时间的人，会获得巨大的优势。
+
+在 AI 时代，最值得投资的不是学会用哪个工具，而是把你自己的思考持续地、结构化地写下来。工具会换，笔记库会一直跟着你。
+
+如果你也在用 Obsidian + AI 的组合，欢迎在评论区分享你的实践。每个人的笔记库都是独一无二的，这正是最有意思的地方。
\ No newline at end of file
diff --git a/Claude/Prompting is the worst way to use Claude. Do this instead.md b/Claude/Prompting is the worst way to use Claude. Do this instead.md
new file mode 100644
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--- /dev/null
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@@ -0,0 +1,401 @@
+---
+title: "Prompting is the worst way to use Claude. Do this instead:"
+source: "https://x.com/rubenhassid/status/2044310385357140304"
+author:
+ - "[[@rubenhassid]]"
+published: 2026-04-15
+created: 2026-04-26
+description: "Stop prompting Claude.For example, this is the worst way to use Claude:If you don’t understand why this prompt is terrible, this free guide ..."
+tags:
+ - "clippings"
+---
+
+
+Stop prompting Claude.
+
+For example, this is the worst way to use Claude:
+
+
+
+If you don’t understand why this prompt is terrible, this free guide is for you.
+
+This will get you average results.
+
+And you will then blame Claude for it.
+
+But deep inside, you might know the real problem. You didn’t give Claude enough context. You didn’t explain your tone, your audience, your rules. Everything.
+
+**But who wants to type 500 words of instructions just to get a first draft?**
+
+Nobody does.
+
+So you don’t. And the output sounds like everyone else.
+
+Maybe some of you tried ‘Projects’ → another Claude feature where you upload your files once, and reuse them across chats:
+
+
+
+That’s what a Project looks like inside Claude, with your files on the right and your chat history on the left. Better, but not as good as Cowork.
+
+That’s better, but you can do **so much better.**
+
+Because you still had to create a new project for every topic, re-upload roughly the same stuff, re-explain the same rules. Not how a **“Second Brain”** works.
+
+Then [Claude Cowork](https://ruben.substack.com/p/claude-cowork-20) happened.
+
+And it’s so good software lost $2.5 trillion in valuation since then:
+
+
+
+I believe software has lost $2.5 trillion in valuation since Cowork. That’s the equivalent of the 5th-biggest company in the world (Amazon).
+
+Cowork made Claude’s growth parabolic, surpassing ChatGPT.
+
+Because Cowork is the biggest thing to happen to AI since ChatGPT:
+
+1. It’s connected to a folder inside your computer.
+2. You save text files in this folder. Your style. Your rules. Your goals.
+3. Claude reads them automatically before every session. **The files are the prompt. Forever.**
+4. And then Cowork creates documents (docx, ppt, Excel…) inside your folder on your computer, like a real employee.
+
+When you set up Cowork properly - I wrote an [entirely free guide](https://ruben.substack.com/p/claude-cowork-20) last week on it, and over 3 million people read it - you can do things like this:
+
+
+
+Even if I wrote the simplest prompt possible, Claude Cowork is reading my entire folder on how I write, my taste, and what I hate too.
+
+
+
+I didn’t ask for anything, but Claude knows it has to go through an “Anti-AI Writing Style” audit before sharing anything with me. The Cowork magic.
+
+
+
+Claude Cowork gives a super solid Linkedin post, and it’s already inside my computer in the subfolder “OUTPUTS”. This is the next level, after ChatGPT.
+
+But Cowork has one big problem. You have to manage folders and files:
+
+
+
+This is what my folder looks like inside my computer. Ew…
+
+Cowork runs on text files. That’s its own prompts.
+
+But you’re not a developer. You never managed text files before in your life.
+
+That’s why most of you never tried Cowork → it’s “too much work to set up”.
+
+The others tried to edit their Cowork folder. You double-click on a file. It opens a weird, geeky, TextEdit. Hashtags. Asterisks around random words.
+
+
+
+When I open a file from my Cowork folder, it looks like coding. And I don’t code. That’s why I rarely edit it… If only it were as easy as a simple Google Doc you could click on, edit, and it would save automatically.
+
+Managing md. files is too complex. You open it, it’s hard to read, hard to edit. You end up never editing the file. So you close it.
+
+How frustrating. I was frustrated too.
+
+And I searched for the simplest/cheapest solution.
+
+And I found it (it’s free).
+
+And I end up accidentally building my “**Second Brain**” inside my Claude.
+
+This guide covers both: (1) how to set up Cowork from scratch, and (2) how to organize your files so you can actually see, search, and edit your Claude’s brain.
+
+Every step, step by step, with screenshots.
+
+Before we start, do two little things:
+
+1. Save this & block 20 min this week to set up Claude. Acting > Reading.
+2. Send it to anyone who uses Claude, but hasn’t tried Cowork.
+
+# Skip this part if your Cowork is already set up.
+
+I hate starting a newsletter by telling you: **“hey, go read a previous newsletter”**.
+
+But I wrote a viral [guide](https://ruben.substack.com/p/claude-cowork-20) on setting up Claude Cowork & its folder.
+
+If you have already read it, skip this part and go to the next called “**Obsidian**”.
+
+If not, it takes 10 min to do it, and I put screenshots & everything.
+
+Or here’s the fastest recap I could make (without images):
+
+1. Claude Cowork is only on the desktop app of Claude (free to download).
+2. You must have a paid account ($20/month). I personally pay $100/mo.
+3. When you start a session with Cowork, you must select a folder.
+4. So create a folder in your computer called “Claude Cowork”.
+5. Inside, create 3 subfolders: about-me, claude-output, and templates. **about-me** is 3 files → about-me (me, my taste), anti-ai-writing-style (because I hate sounding like an AI), and my-company (info about my business). **claude-output** → you don’t touch it, where Claude stores its work. **templates** → you don’t touch it, to save your favorite work template.
+6. Set up Global Instructions (Settings > Cowork > Global Instructions) to ask Cowork always to read about-me and not touch the rest.
+
+If you need the visual step-by-step, go my article [here](https://ruben.substack.com/p/claude-cowork-20).
+
+If you want to copy my files, leave a comment & I will dm it to you.
+
+Once you’re done, continue this newsletter:
+
+# Obsidian.
+
+> **Disclaimer**: I am not paid to write this. I am not affiliated. I am not sponsored.I tested 6 different tools over two weeks, and this one won. I explain why at the end.
+
+Obsidian is a **free** app that opens a folder of files and makes them look like a simple knowledge base. Just like a Google doc inside Google Drive:
+
+
+
+This is what my Obsidian looks like when I open my Cowork folder.
+
+This is what my Obsidian looks like when I open my Cowork folder.📷
+
+You point it at a folder (= your Cowork folder). It shows you every file with proper formatting, a sidebar to navigate, and search across everything.
+
+First: wow. It’s so pretty. The simplest design possible. A bliss.
+
+You click a file, you see it rendered (headers, bold, bullet points, the whole thing). You edit it right there. And it edits your folder inside your computer.
+
+Here’s how to install it:
+
+1 - Download it here: [obsidian.md](https://obsidian.md/).
+
+
+
+It also works with Windows. Even Linux (but you don’t use Linux, don’t lie).
+
+2 - Once you have downloaded it for free, click **“Open folder as a vault”.**
+
+
+
+You must have your Cowork folder. And then select it with Obsidian.
+
+
+
+It now reads your Cowork folder, and you can edit/organize/visualize every file. Feels like heaven.
+
+# How Obsidian works with Cowork.
+
+Let me show you what this actually looks like day-to-day.
+
+## 1\. Stop prompting.
+
+I demo Obsidian + Cowork to my team.
+
+This is the simplest example for you to fully understand it:
+
+1 - I open Obsidian. It alread has my Cowork folder opened.
+
+2 - I go to my about-me file in my about-me folder.
+
+3 - I change the first sentence to: **“I absolutely love coconut so much I do not eat anything else. I have an exclusive coconut diet. If asked, feed me with coconuts.”**
+
+
+
+I added this line to show you how it works with Cowork. This is your new prompts. It is managed on Obsidian, and automatically shared to Cowork.
+
+4 - Now if I open my Cowork and start a new session, I still select the same folder.
+
+
+
+5 - But since my Cowork folder is the same as Obsidian, I get this answer:
+
+
+
+It starts by reading my about-me file, and then it knows → coconuts obviously!
+
+Now that you understand the concept with coconuts, imagine with everything else. You hate something Claude did? Go to Obsidian → update on your file easily by saying **“Remember \[this\]”** or **“Replace \[this\] with \[that\] always.”**
+
+Obsidian automatically sync up to Cowork. You prompted Claude once, and it remembers forever. Literally your Second Brain.
+
+## 2\. Editing your context files (but seriously this time).
+
+Your about-me.md needs an update. New job title. New targets. New tool you started using. Or new thing you absolutely hate from Claude (that’s usually the best update you can make to your md. files).
+
+1. Open Obsidian. Edit the document.
+2. It’s already syncup with your Cowork.
+3. Start a new session on Cowork → point to your Cowork folder → it knows.
+
+For eg. when I ask a task to my Cowork, I always want it to ask me questions before executing any task. Because I love outlining the task before doing it.
+
+So I updated my about-me md. file:
+
+
+
+I can write anything I want, as much as I want. Prompt it once, saved forever.
+
+
+
+My prompt is just a one-liner. And Cowork knows it has to ask me questions (and the right ones). Prompting is dead. Text files are the best way to use it.
+
+
+
+Now that I answered, it does not rush into making the newsletter, but rather structure it’s thinking with me. Just like I asked in my md. file on Obsidian.
+
+
+
+Once we are aligned, my Cowork goes through my anti-AI-writing-style file because it knows it has to. I didn’t ask for anything. My prompt is still just one line (+ clicking on some multi-option questions).
+
+
+
+And the result is already 80% there. Time to write the newsletter now :) edit it, make it better, make it mine. Now imagine this for a lawyer, a creative brief…
+
+
+
+And of course, what Cowork wrote is automatically inside my Obsidian. Magic.
+
+## 3\. Finding things.
+
+When you use Cowork as much as I do (and as much as you should), you will have a lot of files. And you can’t search through it correctly.
+
+Now you can, with Obsidian, for free:
+
+
+
+It searches through everything you wrote and Claude wrote. Easily one of the best feature ever. Because Claude’s internal search engine is terrible.
+
+## 4\. Reading your outputs.
+
+I already said it, but it’s super useful to read all of your past outputs in one folder, with the proper formatting (so its nice to read, like a normal doc):
+
+
+
+This is an example with my newsletter “How to stop hitting Claude usage limits.” Like any newsletter, it always starts with a Cowork session.
+
+## 5\. The daily workflow:
+
+1. Open Obsidian to browse, search, read, edit your files.
+2. Open Cowork to create new work, generate outputs, build deliverables.
+3. Both are looking at the same folder. Always in sync.
+
+Obsidian is where you update your brain → Cowork is where you build.
+
+PS: Some of you probably forgot how good Claude and Cowork is.
+
+So here’s a little recap of all of the things you can do with it:
+
+- Excel files, with multiple tabs, formulas, charts, conditional formatting. From a single prompt.
+- Word documents with headers, tables of contents, page numbers, letterheads.
+- PowerPoint presentations with layouts, speaker notes, images.
+- PDFs: fill them, merge them, split them, extract tables from them.
+- Full websites and interactive dashboards. In one file.
+- Research reports pulled from your own files + the web.
+- Every and any writing piece of content. LinkedIn posts, newsletters, emails, written in YOUR voice (because it reads your files). But also contracts, briefs…
+- Data analysis on CSV files. Upload one, ask a question, get the answer.
+- Scheduled tasks that run automatically while you sleep.
+- Connectors to Slack, Google Drive, Notion, Gmail, and 50+ other apps.
+
+# My new SKILLS folder.
+
+I added something to my own setup this week.
+
+> Disclaimer: this is pretty advanced. Most of you don’t need it.
+
+Your about-me files tell Claude your style, your rules, your goals. That covers the basics (and non-negotiables). Now Claude sounds like you. Good.
+
+But you repeat some tasks every week.
+
+A LinkedIn post. A newsletter. A client brief. A specific contract.
+
+And every time you add the same extra instructions. **“Keep it under 1,300 characters.” “Single image only.” “It’s a music specific legal contract.”**
+
+You say it once, fine. You say it 30 times, that’s not so efficient.
+
+So Claude’s team created “Skills”. A visual recap:
+
+
+
+I have over 100+ infographics like this. I give them to readers who subscribe for free. Leave a comment if you subscribed but you can’t find the file.
+
+Claude Skills are your saved workflows. One file per task.
+
+And you call it with a single / command (like **/contract** for a contract).
+
+Here’s how to create your 1st skill (let’s take negotiating as an example):
+
+1. Open Cowork. Start a new task.
+2. Prompt this: Create a skill called “**negotiation**”.Interview me about the kinds of deals I negotiate daily. Build the best negotiation skills to help me create different negotiation scenarios (based on different experts). Then save it as a skill I can call with **/negotiation**.
+3. Cowork asks you questions. Answer them.
+4. Cowork creates the skill file automatically. It will be in the OUTPUT folder.
+5. I then go to Obsidian and move this output to my new folder “**SKILLS**”.
+6. Here’s the visual step-by-step:
+
+
+
+Step 1: Copy and paste my prompt inside Cowork with Opus 4.6. Make sure to select your Cowork folder!
+
+**Step 2:** Answer the questions.
+
+**Step 3:** Claude will ask you to **“Always allow”** to edit your Claude. Yes you do. Then wait for a while.
+
+**Step 4:** Claude wants to make sure your skill is good enough. So you have to review with examples and benchmarks. So good.
+
+**Step 4**: Tell Claude “**It’s ok now**” and it will create the skill. You can click on “**Save skill”**.
+
+**Step 5**: Now when I type /nego it already shows the skill. Click on it and prompt it from there.
+
+**Step 6**: You can combine the flavors. Cowork + my super folder + skill /negotiation + Gmail connector so it reads my email. **Magic**.
+
+**Step 7**: I want to change my skill? I can edit it simply on Obsidian. But here’s how to reupload it inside Claude (again, after you changed it):
+
+**Step 8**: How to reupload any skill.
+
+Now anytime I go to Cowork and I prompt **/negotiate** - it knows what to do.
+
+Every skill lives as a text file in your Cowork folder. Which means Obsidian can see it, search it, and let you edit it. You tweak your LinkedIn skill after you find a better hook formula? Open it in Obsidian, change one line, save.
+
+Sky is the limit with your skills:
+
+- /newsletter
+- /client-brief
+- /sales-email
+- /weekly-report
+- /meeting-notes
+
+# Why Obsidian (and why not something else).
+
+I didn’t pick Obsidian because it’s trendy or because they paid me.
+
+I picked it because everything else failed at least one of my criteria.
+
+1. free
+2. inside my computer
+3. ease of use & clean design
+
+**Notion.** Cloud-based. Stores everything in its own database. Your .md files would need to be imported, and every time Cowork updates a file, you’d have to re-import. Notion is great for project management. Terrible for this.
+
+PS: My entire team is still on Notion to manage our content library.
+
+**Google Drive + Google Docs.** Google Docs aren’t .md files. You’d need to convert back and forth every time Cowork reads or writes. Not useful.
+
+**Apple Notes.** Closed format. Can’t point it at a folder. Can’t search across .md files. Dead end.
+
+**VS Code / Cursor.** Amazing tools. For developers. If I see a code editor, I close the tab. The interface assumes you know what a terminal is.
+
+**GitHub.** Renders markdown beautifully online. Requires git, commits, pushes, pulls. I lost you at “git.” Moving on.
+
+**Typora.** Beautiful markdown editor. $15 one-time purchase. But it edits one file at a time. No sidebar navigation across your whole folder. No search across all files. No linking between documents. Fine for writing. Missing the “brain” part.
+
+**MarkEdit.** Free, Mac only, ultra-minimal. Same problem as Typora: one file at a time, no bird’s-eye view.
+
+**Obsidian wins because…**
+
+It reads your existing folder. No import, conversion or sync.
+
+It’s free for single-device use. $4/month if you want it on your phone too.
+
+It’s built for non-developers. The interface is clean and simple.
+
+And the thing that sealed it: Obsidian never touches your files in a way that breaks Cowork. No hidden metadata injected or weird format changes.
+
+Your .md files stay .md files. Cowork reads them the same way it always did.
+
+One download. One folder selection. **Free on one device**. Unbeatable.
+
+A little bonus point: I love their CEO, Steph Ango.
+
+The CEO himself said they don’t know how many users Obsidian has… they don’t force users to create accounts, and they don’t have analytics. I love it.
+
+# A message from the author, Ruben.
+
+This article exists because 67,000+ people decided AI is too important to leave aside. Not only that, but they shared it around them. They understand they are the sum of the 5 people around them. So better have them using AI.
+
+If this helped you — or if it’ll help someone you know — forward it to them. That’s how this grew. Just readers like you sending it to people like them.
+
+And if you're new here, follow me on X →[@rubenhassid](https://x.com/@rubenhassid) (also free!)
\ No newline at end of file
diff --git a/Claude/Stop Writing Prompts. Start Designing Systems (Claude Code Explained).md b/Claude/Stop Writing Prompts. Start Designing Systems (Claude Code Explained).md
new file mode 100644
index 0000000..afe700b
--- /dev/null
+++ b/Claude/Stop Writing Prompts. Start Designing Systems (Claude Code Explained).md
@@ -0,0 +1,195 @@
+---
+title: "Stop Writing Prompts. Start Designing Systems (Claude Code Explained)"
+source: "https://x.com/Suryanshti777/status/2048055574185779638"
+author:
+ - "[[@Suryanshti777]]"
+published: 2026-04-25
+created: 2026-04-26
+description: "The Shift Nobody UnderstandsEveryone is using Claude Code.But most are just… talking to it.They write prompts.They tweak wording.They..."
+tags:
+ - "clippings"
+---
+
+
+The Shift Nobody Understands Everyone is using Claude Code. But most are just… talking to it. They write prompts. They tweak wording. They retry when it fails. And sometimes it works. But serious builders don’t rely on luck. They build systems. > Claude is not a chatbot for developers It’s a programmable reasoning engine From Prompts → Systems Most people operate like this: "Build a login system" And hope for the best. Advanced users operate like this: define context enforce constraints structure reasoning create feedback loops > Prompts give outputs Systems give consistency The Real Architecture (With Code) A proper Claude workflow looks like: Input → Reasoning → Execution → Feedback → Memory Let’s break this down with actual implementation. 1. Input Layer — Structured Context (Code Template) Instead of random prompts, use a prompt builder system.
+
+```python
+def build_prompt(task, context, constraints):
+ return f"""
+You are a senior software engineer.
+
+Task:
+{task}
+
+Context:
+{context}
+
+Constraints:
+{constraints}
+
+Instructions:
+1. First, break down the problem
+2. Identify edge cases
+3. Propose approach
+4. Then write clean code
+
+Output format:
+- Plan
+- Code
+- Edge Cases
+"""
+```
+
+Example Usage:
+
+```python
+task = "Build a reusable modal component"
+
+context = """
+Next.js (App Router)
+Tailwind CSS
+No external libraries
+"""
+
+constraints = """
+- Must follow existing component structure
+- Accessible (ARIA compliant)
+- No new dependencies
+"""
+
+prompt = build_prompt(task, context, constraints)
+print(prompt)
+```
+
+\> You’re not writing prompts anymore You’re generating them 2. Reasoning Layer — Forcing Structured Thinking You can force Claude into better reasoning patterns. Before writing code: 1. Break the problem into smaller parts 2. List possible edge cases 3. Choose the best approach 4. THEN generate code Do not skip steps. This small addition dramatically improves: logic clarity completeness fewer bugs > If you don’t guide the thinking you’ll debug the output later 3. Execution Layer — Controlled Code Generation Now Claude generates code. But instead of trusting it blindly, we wrap it. Example: Claude API Wrapper (Pseudo Implementation)
+
+```python
+def ask_claude(prompt):
+ # Replace with actual API call
+ response = f"[Claude Output for]: {prompt[:100]}..."
+ return response
+```
+
+4\. Feedback Loop — Automated Debug Cycle This is where real power comes in. Instead of fixing manually → you create a self-improving loop Example: Test → Fix → Repeat System
+
+```python
+import subprocess
+
+def run_tests():
+ result = subprocess.run(
+ ["npm", "test"],
+ capture_output=True,
+ text=True
+ )
+ return result.stdout
+
+def fix_with_claude(error_output):
+ prompt = f"""
+Tests are failing.
+
+Error logs:
+{error_output}
+
+Task:
+Fix the issue.
+
+Constraints:
+- Do not change working logic
+- Only modify necessary parts
+
+Return only updated code.
+"""
+ return ask_claude(prompt)
+
+while True:
+ output = run_tests()
+
+ if "FAIL" in output or "Error" in output:
+ print("❌ Tests failed. Asking Claude to fix...\n")
+
+ fix = fix_with_claude(output)
+ print(fix)
+
+ # Here you'd apply patch (simplified)
+ else:
+ print("✅ All tests passed!")
+ break
+```
+
+What this does: detects failures feeds them back to Claude iterates automatically > This is where Claude stops being a tool and becomes a system 5. Memory Layer — CLAUDE.md System Instead of repeating instructions every time → store them once. Example: CLAUDE.md
+
+```markdown
+# Project Rules
+
+## Architecture
+- Functional components only
+- Follow modular folder structure
+
+## Naming
+- Components: PascalCase
+- Hooks: useCamelCase
+
+## Constraints
+- No new dependencies
+- Use existing utilities
+
+## Patterns
+- Composition over inheritance
+- Reusable components first
+```
+
+Injecting Memory into
+
+```python
+def load_memory():
+ with open("CLAUDE.md", "r") as f:
+ return f.read()
+
+def build_prompt_with_memory(task):
+ memory = load_memory()
+
+ return f"""
+Project Memory:
+{memory}
+
+Task:
+{task}
+
+Follow all rules strictly.
+"""
+```
+
+Memory converts randomness into consistency 6. Constraint Layer — Controlling Output Without constraints → chaos With constraints → precision Example: Build authentication system Constraints: - Use JWT only - No external auth providers - Do not modify database schema - Use existing API routes only Do NOT: - Add dependencies - Change architecture > Constraints don’t limit AI They focus it Full Workflow in Action Here’s how everything connects:
+
+```python
+task = "Build a user authentication system"
+
+context = """
+Node.js backend
+Express framework
+MongoDB database
+"""
+
+constraints = """
+- Use JWT
+- No new dependencies
+- Follow existing structure
+"""
+
+prompt = build_prompt(task, context, constraints)
+
+response = ask_claude(prompt)
+
+print("🚀 Initial Output:\n", response)
+
+# Then run:
+# → tests
+# → feedback loop
+# → refinement cycle
+```
+
+Why This Changes Everything Because now:
+
+You’re not: writing code fixing bugs manually You’re: designing systems orchestrating intelligence controlling outputs Old Way Write → Debug → Rewrite New Way Design → Generate → Test → Refine The Real Edge Most people will keep: writing prompts chasing better wording A few will: build systems create workflows scale output massively > The future of coding isn’t typing faster It’s thinking in systems
+
+Final Thought Claude Code is not magic. It’s leverage. And leverage only works when applied correctly. > Stop asking Claude to write code Start designing systems that make code inevitable
\ No newline at end of file
diff --git a/Claude/The Chinese AI Model That Quietly Beat Claude at Its Own Game.md b/Claude/The Chinese AI Model That Quietly Beat Claude at Its Own Game.md
new file mode 100644
index 0000000..4bc89f6
--- /dev/null
+++ b/Claude/The Chinese AI Model That Quietly Beat Claude at Its Own Game.md
@@ -0,0 +1,355 @@
+---
+title: "The Chinese AI Model That Quietly Beat Claude at Its Own Game"
+source: "https://x.com/k1rallik/status/2047668768050913561"
+author:
+ - "[[@k1rallik]]"
+published: 2026-04-24
+created: 2026-04-26
+description: "Kimi Code is what I found after asking why my Claude Code felt slow - turns out you can replace the entire model underneath it in 30 seconds..."
+tags:
+ - "clippings"
+---
+
+
+Kimi Code is what I found after asking why my Claude Code felt slow - turns out you can replace the entire model underneath it in 30 seconds, and almost nobody knows this is even possible.
+
+The Chinese AI that replaces it runs at 100 tokens per second with a context window big enough to swallow your entire codebase. Two lines. That's it.
+
+## What Is Kimi Code - And Why Should You Care
+
+Let me save you the Wikipedia version.
+
+
+
+Kimi Code is an AI coding agent from **Moonshot AI** - a Chinese lab that's been quietly shipping some of the most capable models of the last year. You might know their chatbot Kimi, which exploded in popularity earlier this year. Kimi Code is their developer tool - and it's a different beast entirely.
+
+This isn't an autocomplete plugin. This isn't a chat window you tab to when you're stuck.
+
+Kimi Code is a full **autonomous agent**. It reads your files, understands your project structure, writes and edits code, runs shell commands, searches the web when it needs to - and then checks its own work. You give it a task in plain English and it figures out the rest.
+
+It runs on **kimi-k2.6** - their flagship coding model - with optional thinking mode where you can literally watch the AI reason through a problem step by step before answering.
+
+But here's the thing that actually made me write this article.
+
+
+
+**Kimi Code is fully compatible with Claude Code.** Not "kind of compatible." Not "you can sort of make it work." You change two environment variables, type claude, and your exact same Claude Code setup is now running on Kimi's model. Same interface. Same commands. Different brain.
+
+That's the trick. And almost nobody is talking about it.
+
+## Installation - Three Ways to Get Started
+
+**Way 1: CLI - Fastest Setup on Earth**
+
+One command. That's it.
+
+```bash
+# macOS / Linux
+curl -LsSf https://code.kimi.com/install.sh | bash
+
+# Windows (PowerShell)
+Invoke-RestMethod https://code.kimi.com/install.ps1 | Invoke-Expression
+```
+
+
+
+Check it worked:
+
+```bash
+kimi --version
+```
+
+Now go into your project folder and launch it:
+
+```bash
+cd your-project
+kimi
+```
+
+First time? Type /login - browser opens, you authorize with your Kimi account, done. Settings are saved automatically. Next time you just run kimi and you're straight in.
+
+
+
+If you already have uv installed (Python package manager), you can also do:
+
+uv tool install --python 3.13 kimi-cli
+
+> **macOS heads up:** First launch might be slow because of Gatekeeper security checks. Fix it by adding your terminal to System Settings - Privacy & Security - Developer Tools. One-time setup, fast forever after.
+
+
+
+
+
+GIF
+
+**Way 2: VS Code Extension - For the GUI People**
+
+No shame in this. Open VS Code, hit Cmd+Shift+X, search **"Kimi Code"** by moonshot-ai, install.
+
+A Kimi icon shows up in your Activity Bar. Click it, log in through the browser, and you've got a full AI coding panel living inside your editor. Chat, file diffs, plan review, image input - the whole thing.
+
+Open it anytime with Cmd+Shift+K on Mac or Ctrl+Shift+K on Windows.
+
+
+
+**Way 3: Drop Kimi Into Claude Code - The Move Nobody Talks About**
+
+This is the one. If you're already a Claude Code user - stop what you're doing and try this right now.
+
+```bash
+# macOS / Linux - paste this before running claude
+export ANTHROPIC_BASE_URL=https://api.kimi.com/coding/
+export ANTHROPIC_API_KEY=sk-kimi-your-key-here
+
+claude
+```
+
+```bash
+# Windows PowerShell
+$env:ANTHROPIC_BASE_URL="https://api.kimi.com/coding/"
+$env:ANTHROPIC_API_KEY="sk-kimi-your-key-here"
+
+claude
+```
+
+Type /status to confirm the model switched to Kimi. That's your entire migration.
+
+Get your API key at [kimi.com/code](https://kimi.com/code) - go to Console - API Keys - Create New Key. It shows once, copy it immediately.
+
+Want to toggle to Kimi's thinking model mid-session? Hit **Tab** inside Claude Code.
+
+## The Features That Actually Matter
+
+Thinking Mode - Watch the AI Reason Out Loud
+
+Most AI tools give you an answer. Kimi Code can show you how it got there.
+
+Enable it at launch:
+
+```bash
+kimi --thinking
+```
+
+When thinking is on, every response comes with a collapsible reasoning block - like a scratchpad where the model works through the problem. You can expand it and follow the logic. For complex bugs or architectural decisions, this is genuinely useful. You're not just trusting a black box anymore.
+
+Want thinking on by default? Add this to your config.toml:
+
+```ini
+default_thinking = true
+show_thinking_stream = true
+```
+
+
+
+Some models like kimi-k2-thinking-turbo always run with thinking on and can't be turned off - they're built for hard problems.
+
+## YOLO Mode - Full Autopilot
+
+By default Kimi asks you before touching any file or running any command. YOLO mode turns all that off:
+
+```bash
+# Launch directly in YOLO mode
+kimi --yolo
+
+# Or toggle it mid-session
+/yolo
+```
+
+Yellow YOLO badge appears in the status bar. Everything runs without approval until you type /yolo again to disable.
+
+
+
+Use it on test projects, in isolated environments, or when you've watched the agent enough to trust what it's doing. Don't use it in production on a Monday morning. You've been warned.
+
+## Plan Mode - Think Before You Act
+
+VS Code extension only - there's a small 📋 icon left of the input box. Enable it and Kimi outputs a full step-by-step plan before doing anything. You review, edit if needed, then let it go.
+
+
+
+For big risky tasks - refactoring a core module, updating all dependencies - this is the feature that saves you from a 2am rollback.
+
+## @ File References - Stop Copy-Pasting Code
+
+Type @ anywhere in your message and you get autocomplete for files and folders in your project:
+
+```text
+Check for bugs in @src/components/Button.tsx
+
+Explain what @src/core/scheduler.py does
+
+Why is this pattern used in @src/api/auth.ts:45-78?
+```
+
+Tab to autocomplete. Reference specific line ranges. Works in both CLI and VS Code.
+
+## MCP Servers - The Power-User Feature
+
+MCP (Model Context Protocol) lets you connect external tools directly to the agent. In VS Code go to Action Menu - MCP Servers.
+
+Pre-built ones available with one click:
+
+- **Playwright** - Kimi writes code and tests it in a real browser in the same workflow
+- **GitHub** - reads your issues, creates PRs, understands your repo history
+- **Context7** - pulls live documentation so the agent always has current API references
+
+## Sessions - Never Lose Your Work
+
+```bash
+kimi --continue # resume last session in this folder
+kimi --session abc123 # resume a specific session by ID
+/sessions # pick from a list mid-session
+```
+
+Context getting too long?
+
+```bash
+/compact # AI summarizes the conversation, keeps going with less tokens
+/clear # wipe everything and start fresh
+```
+
+The status bar shows context: xx% - watch that number and hit /compact when it gets high.
+
+
+
+## Real Tasks, Real Results
+
+**Debugging - Just Paste the Error**
+
+Stop reading stack traces manually. Paste the error, describe what you were doing, and let it run:
+
+```text
+Running npm test throws this:
+
+TypeError: Cannot read property 'map' of undefined
+ at UserList.render (src/components/UserList.jsx:15:23)
+
+Fix it.
+```
+
+Kimi reads the relevant files, finds the root cause, and shows you a diff before changing anything. You approve the fix or reject it.
+
+Or go even simpler:
+
+```text
+Run the tests. Fix any failures.
+```
+
+It runs. Reads the output. Finds what broke. Fixes it. Runs again to verify. You watch.
+
+## Building Features - Natural Language to Working Code
+
+```text
+Add pagination to the user list - 20 items per page
+```
+
+Kimi works in a **Read - Edit - Verify** loop:
+
+1. Reads your existing code and figures out how the project is structured
+2. Writes changes that match your existing style - not generic boilerplate
+3. Runs your tests to make sure nothing broke
+
+Not happy with the result?
+
+```text
+The pagination style doesn't match the rest of the UI -
+look at the Button component and follow the same patterns
+```
+
+It goes back, reads the Button component, adjusts. No regenerating from scratch.
+
+## Understanding a New Codebase
+
+Every developer knows this moment - you inherit a repo and have no idea where anything is.
+
+```text
+What's the overall architecture of this project? Where's the entry point?
+
+How is authentication implemented? Which files are involved?
+
+Explain what @src/core/scheduler.py does and why it exists
+```
+
+
+
+Five minutes instead of an hour. Ask follow-up questions like you're talking to whoever built it.
+
+## Automating the Boring Stuff
+
+```text
+Replace all var with const or let in every .js file under src
+
+Add JSDoc comments to every public function that doesn't have one
+
+Update all package.json dependencies to latest, then run the tests
+```
+
+That last one is where it gets impressive. Updates everything, runs tests, sees what broke, fixes it, runs tests again. You get the result.
+
+## Honest Pros and Cons
+
+No product is perfect. Here's the real picture.
+
+
+
+## What's Actually Good
+
+**Speed is noticeable.** Up to 100 tokens/sec. Responses feel snappy in a way that matters during active work sessions. Not just a benchmark number.
+
+**The Claude Code integration is genuinely seamless.** Two environment variables and you're running a different model in your existing setup. Zero learning curve for existing Claude Code users.
+
+**Thinking mode changes how you debug.** Seeing the reasoning chain isn't just aesthetically interesting - it helps you catch when the model is going down a wrong path before it makes changes.
+
+**Context window is huge.** 262,144 tokens. You can drop an entire large codebase in and it handles it.
+
+**It's included in Kimi membership.** No separate charge for Kimi Code on top of your plan.
+
+**Works everywhere.** CLI, VS Code, JetBrains via ACP protocol, Zed editor. Zsh plugin lets you jump in from any terminal with Ctrl-X.
+
+## What's Not So Good
+
+
+
+**Quota doesn't roll over.** Resets every 7 days - use it or lose it. You need to actually build a habit around it or you're wasting your plan.
+
+**Personal use only.** This is explicitly for individual developers. No team plans, no commercial products built on top of it. For that you need Moonshot AI's separate enterprise platform.
+
+**The ecosystem is still small.** The docs are good. The tool is solid. But the community, tutorials, and third-party integrations are nowhere near Cursor or GitHub Copilot yet. You're an early adopter.
+
+**30-day inactivity timeout.** Leave it alone for a month and your session expires. Minor annoyance, just run /login again.
+
+## Who Should Actually Use This
+
+**Use Kimi Code if:**
+
+- You're already on Claude Code and want to try a different model without changing your workflow - this is the zero-risk experiment
+- You work with large codebases and need that 262k token context window
+- You want to actually see how the AI reasons, not just what it outputs
+- You live in the terminal and want AI there natively
+
+**Probably skip it if:**
+
+- You need it for a team or commercial product - wrong tier for that
+- You're not comfortable with CLI tools at all - start with Cursor or GitHub Copilot and come back
+- You need a large support community and tons of tutorials - not there yet
+
+
+
+## The Bottom Line
+
+Kimi Code is not trying to replace everything. It's a well-built agent from a serious AI lab that does one thing particularly well - it fits into where you already are.
+
+No new interface to learn. No migration. No commitment.
+
+If you use Claude Code: two lines in your terminal and you can be running on Kimi K2 in the next 60 seconds. Try it on one task. See what happens.
+
+The bar to try it is basically zero. The upside might surprise you.
+
+
+
+**Resources and links:**
+
+- Official site: [kimi.com/code](https://kimi.com/code)
+- Full docs: [kimi.com/code/docs/en](https://kimi.com/code/docs/en)
+- GitHub (CLI): [github.com/MoonshotAI/kimi-cli](https://github.com/MoonshotAI/kimi-cli)
+- VS Code extension: search "Kimi Code" in the VS Code marketplace
\ No newline at end of file
diff --git a/Claude/The Claude-Native Law Firm.md b/Claude/The Claude-Native Law Firm.md
new file mode 100644
index 0000000..50c157f
--- /dev/null
+++ b/Claude/The Claude-Native Law Firm.md
@@ -0,0 +1,118 @@
+---
+title: "The Claude-Native Law Firm"
+source: "https://x.com/zackbshapiro/status/2027389987444957625"
+author:
+ - "[[Unknown]]"
+published: 2026-02-27
+created: 2026-03-14
+description:
+tags:
+ - "clippings"
+---
+## How I Actually Practice Law with AI in 2026
+
+A few months ago, the night before a client’s acquisition was set to close, the buyer’s counsel sent a letter demanding that several key deal terms be restructured. New escrow conditions. Expanded indemnification carve-outs. A revised set of closing deliverables. The implicit threat: accept these changes or we walk. It was 7 PM.
+
+I uploaded the purchase agreement, the disclosure schedules, and the demand letter to Claude. Within minutes, Claude mapped every proposed change against the existing deal terms and found what the buyer’s lawyers apparently hadn’t noticed: two of their proposed carve-outs directly contradicted representations they had already confirmed in the disclosure schedules, and a third would have created an internal conflict with the fundamental reps section that would have actually weakened the buyer’s own post-closing protections. Their aggressive last-minute play had holes in it.
+
+As the negotiation continued through the evening with emails going back and forth, I fed each new communication to Claude. It tracked how every proposed concession interacted with provisions across the agreement, flagged where accepting one change would create exposure in another section, and helped me build a response that conceded the points worth conceding and held firm on the ones that mattered. By 11 PM we had a clean set of counter-positions, each grounded in specific cross-references to the buyer’s own language. The deal closed the next morning on terms my client was happy with.
+
+A team of three associates at a mid-size firm would have needed until morning to produce that analysis. I had the core of it in under two hours.
+
+I run a two-person boutique law firm. We handle startup formation, venture capital transactions, and regulatory work. We compete against firms with hundreds and sometimes thousands of lawyers. We are not supposed to be able to do this. But the past year has made something clear: a small firm built around AI doesn’t just keep up with larger competitors. It moves faster, produces more thorough work product, and operates at a cost structure that would have been impossible 18 months ago.
+
+The tool I’ve built my practice around is Claude, made by Anthropic. This piece is an explanation of how I actually use it, every day, for real legal work. Not the theory. The workflow.
+
+# Why Claude, Not “Legal AI”
+
+The market is full of specialized legal AI products. Harvey, Spellbook, CoCounsel, Luminance. They all share a thesis: lawyers need AI built specifically for legal work. I’ve evaluated most of them. For a small firm practitioner, a well-configured general-purpose AI is better. It’s not close.
+
+The specialized products are wrappers built on top of the same foundation models that power the general-purpose tools. Their marketing pitch sounds compelling: we’ll customize the AI to your firm’s playbook, train it on your templates, build workflows around your brief bank or clause library. Some of them do this reasonably well. But the pitch contains a fundamental misunderstanding of where the value actually lives.
+
+A template library is not a competitive advantage. Every competent firm in your practice area has roughly the same templates. The NDA, the stock purchase agreement, the employment offer letter. These are commodity inputs. The thing that differentiates a great lawyer from a mediocre one was never the template. It was what the lawyer did with the template: how they spotted the issue the other side buried in Section 14(c), how they knew which indemnification fight was worth having and which to concede, how they structured the advice email so the client actually understood the risk. That is judgment. And judgment doesn’t live at the firm level. It lives at the level of the individual professional.
+
+When legal AI companies talk about customizing AI to a firm’s playbook, they are solving a problem that barely matters and ignoring the one that does. The real leverage comes not from which template the AI starts with, but from the instructionsthat tell it how to think about the work: what to look for, what to flag, how to weigh competing considerations, what format to deliver the output in, what tone to use with the client. Those instructions encode an individual lawyer’s judgment, not a firm’s template library. And that is exactly what Claude’s skill system is built to do.
+
+I’ve created custom instruction files, called “skills,” that encode my analytical frameworks, my preferred formats, my voice, and my judgment about how specific types of legal work should be done. When I upload a contract for review, Claude doesn’t apply a generic framework. It doesn’t even apply my firm’s framework. It applies my framework, the one I’ve developed over a decade of practice, automatically. The difference between a firm playbook and an individual lawyer’s encoded judgment is the difference between giving someone a recipe and teaching them how to cook.
+
+There’s a more fundamental issue, and it’s the one that will matter most to anyone who has spent their career inside Microsoft Word. Claude is a frontier AI model that has been heavily optimized for writing code. That may sound irrelevant to legal practice until you realize what it means: Claude can write code, on the fly, to directly manipulate the applications lawyers already use.
+
+Think about what this means concretely. Every lawyer reading this has lost hours to Word formatting. Paragraph numbering that breaks when you paste from another document. Styles that refuse to cooperate. Track changes that corrupt across versions. Cross-references that go stale. Bluebook citation formatting that requires manual attention on every single period and comma. These are not legal problems. They are software problems. And Claude solves software problems by writing software. When I tell Claude to apply tracked changes to a contract, it doesn’t use a plugin or a macro. It opens the .docx file at the XML level and writes the exact markup that Microsoft Word expects, attributed to my name, preserving every formatting detail. When I tell it to standardize the citation format in a brief, it writes code to parse and reformat every citation in seconds. The result is indistinguishable from expert manual work, delivered in a fraction of the time.
+
+This is the capability gap that no specialized legal AI product can match. They give you a chatbot that talks about documents. Claude is a system that can reach inside those documents and change them. It is the difference between an associate who can tell you what’s wrong with a contract and an associate who can also fix it, format it, produce the redline, and draft the cover email, all without you opening a single application. General-purpose AI advances faster than any vertical product can keep up with. When you’re on the frontier model, every new capability ships to you on day one. When you’re on a wrapper, you’re waiting for someone else’s engineering team to decide what to build next.
+
+I’m describing my own practice here, which is transactional. But nothing about the architecture is practice-specific. A litigator would build skills for deposition preparation, motion drafting, case law synthesis, and discovery review. A tax lawyer would build skills for entity structuring, opinion letter frameworks, and regulatory monitoring. A family lawyer would build skills for asset tracing and custody analysis. The approach is the same: take a powerful general model, teach it your practice, and let it compound your judgment. The content is yours.
+
+# Three Modes
+
+Claude’s desktop app has three modes. Learning when to use each one was the single most important step in making this work.
+
+**Chat** is the conversational interface. I talk to Claude the way I’d talk to a fast, knowledgeable associate sitting across the table. This is where I go for analyzing a legal issue, brainstorming negotiation strategy, getting a first take on a contract provision, or drafting something from scratch. I stay in control of every step. Most lawyers who have used ChatGPT or similar tools have only experienced this mode.
+
+**Cowork**is the autonomous mode, and it’s the one that changes everything. I point Claude at a folder on my computer, give it a task, and it goes and does it. It reads files, creates new ones, edits existing documents, and makes its own decisions about how to get from A to B. When I have a 40-page agreement that needs a full redline, or a stack of closing documents that need to be generated from a term sheet, I hand it to Cowork and let it work. This is the mode most lawyers haven’t tried. It’s the one that will change their practice the most.
+
+**Code** is the development mode. Full terminal access. Most lawyers don’t need it daily. But I have a condition that makes it hard to read long documents, so I used Code to build a command-line tool that converts legal documents into spoken audio. It handles the entire pipeline: parsing Word docs and PDFs, converting legal formatting like “Section 4.2(b)(iii)” into natural speech, expanding abbreviations, chunking the text, sending it to an AI voice API, and assembling the final audio file. I listen to contracts on my commute now. Claude built the whole thing.
+
+# Teaching Claude Your Practice
+
+This is where the leverage becomes something I wouldn’t have believed two years ago.
+
+Anthropic published a guide on building custom “skills” for Claude: structured instruction files that teach it how to behave in a specific context. Not a prompt you type every time. A persistent set of instructions that fires automatically when the situation calls for it. Instead of reading the guide cover to cover, I uploaded it to Claude and asked a better question: based on the hundreds of conversations we’ve had together, spanning contract drafting, client emails, document editing, legal research, and policy writing, what are the skills that would have the greatest impact on my practice?
+
+Claude analyzed months of our work and identified the patterns: which tasks I repeated most, where the friction was highest, where structured automation could save the most time. The skills it recommended weren’t generic. They were specific to how I actually work. Not “draft contracts faster” but “a contract review skill with four distinct modes depending on context, severity ratings, a missing-provisions checklist, market-term benchmarking, and a seamless handoff to a tracked-changes editing skill when you’re ready to mark up the document.”
+
+We refined the details over a couple hours. I pushed back where the defaults didn’t match my preferences. By the end I had six production-ready skills bundled into a single plugin for the Cowork desktop app: contract review, tracked changes editing, contract drafting, client communications, legal research, and policy writing. Each one encodes years of accumulated professional judgment about how I approach that type of work.
+
+The implication that matters for firm management: the plugin is transferable. If I had 50 associates, I could install it on every machine. Every associate would immediately produce contract reviews using my analytical framework, draft communications in my voice, and apply tracked changes in my preferred format. Knowledge that takes years of mentorship to transmit is now an instruction file that works from the first draft. The output still requires attorney review, but the review starts from a much higher baseline.
+
+# What This Looks Like in Practice
+
+Three examples from real work, because I want this to be concrete.
+
+**Tracked changes without opening Word.** A counterparty sends back a redlined agreement. Forty pages of changes across representations, indemnification, IP, and closing conditions. I upload the document to Claude and say: “Help me evaluate the counterparty’s changes from my client’s perspective.” My contract review skill fires. Claude organizes every change by severity, flags where the counterparty shifted risk, identifies tensions between modified provisions, checks for standard provisions that should be present but aren’t, and produces a summary with specific counter-language for each issue.
+
+Then I apply my judgment. Claude flagged a pattern in the markup. I know from experience what that pattern usually signals. Claude generated three alternative formulations for a disputed clause. I pick the one that accounts for relationship dynamics and deal context that no AI has access to. Once I’ve made my decisions, I tell Claude to apply the edits. This is the part that drops jaws the first time you see it. Claude opens the Word document at the XML level, applies tracked changes attributed to my name, preserves every formatting detail, and produces a clean .docx with real tracked changes that opposing counsel can open in Microsoft Word and review normally. I don’t open Word. I don’t open Litera. Claude produces the redline. I review every change, and I send it. Then the client communications skill drafts the cover email in the right tone. Total time from receiving the markup to having a response package ready to send: under an hour, of which about 30 minutes is my own thinking.
+
+**Research without hallucinations.** A client needs to understand the regulatory landscape for a new product. The question spans multiple agencies and overlapping statutory frameworks. My research skill instructs Claude to launch parallel research across every relevant angle simultaneously rather than working through them sequentially: the securities analysis, the state licensing requirements, the banking regulations, the consumer protection implications. It runs multiple searches per sub-topic, cross-references sources, and prioritizes primary authority (statutes, regulations, agency guidance, case law) over secondary commentary.
+
+Before delivering anything to me, the skill requires Claude to run a self-review. This is critical, and it’s the part most people skip. Claude must verify that every cited authority actually says what the memo claims. It must flag anything where its confidence is below high. It must check for internal contradictions across sections. And it must specifically guard against hallucinated citations, the problem that got several lawyers sanctioned and made national news. The lawyers who submitted fake AI-generated citations were using tools without this kind of verification layer. The problem was never AI itself. It was AI without quality control.
+
+The output is a structured research memo, with a bottom-line-up-front summary, specific statutory citations, and practical recommendations, that would take a junior associate days to produce. Claude delivers a first draft in under an hour. I then review every citation, stress-test the analysis, and revise where my judgment diverges from the output. The total time is still a fraction of what it would take starting from scratch. And because the skill is calibrated to my standards (confident conclusions with explicit uncertainty flags, tables for comparing regulatory frameworks, practical recommendations rather than academic hedging), the memo is useful immediately.
+
+**Real-time contract interpretation.** A client called mid-morning to say they had just received a demand letter from a counterparty claiming breach of a commercial services agreement and threatening termination. The client had 48 hours to respond. I uploaded the agreement, the demand letter, and the client’s last three months of correspondence with the counterparty. Claude mapped every factual allegation in the demand letter against the specific contract provisions cited, and found that two of the four claimed breaches referenced obligations that had been expressly modified by a side letter the counterparty’s own counsel had drafted. The demand letter appeared to have been written without checking their own amendments. As I prepared the response, I ran each draft paragraph through Claude to pressure-test whether any of my arguments had unintended implications for other provisions in the agreement. It caught one: a defense I was planning to raise on the service-level metrics could have been read to concede a point on the payment dispute in Section 7. I rewrote the response. That kind of real-time, provision-by-provision stress-testing while actively drafting is something that used to require a second lawyer reviewing your work. Now it happens in the same conversation where the work gets done.
+
+# The Privilege Question
+
+Every lawyer asks. The short answer: the same framework that lets you use cloud storage, e-discovery platforms, and online legal research databases applies here. ABA guidance and state bar ethics opinions treat AI tools as third-party technology providers covered by the agent/instrumentality exception. Your obligations are to make reasonable efforts to protect client data, which in practice means turning off model training on your inputs, understanding the provider’s data handling practices, and documenting your reasoning. Anthropic offers a zero-data-retention API option and business data processing agreements, so that none of your client data is used to train models, and inputs are not stored beyond the session. The same diligence you performed before putting client documents in Dropbox, Google Drive, or Clio.
+
+I went a step further. I had Claude help me draft an AI usage provision for my engagement letters. The provision frames AI as an efficiency and quality enhancer, emphasizes attorney supervision, ties data handling to existing confidentiality obligations, and secures client consent. Clients sign it without blinking. Most of them assume I’m already using AI. They’re right.
+
+The ethics rules now require technology competence in most jurisdictions. We are approaching the point where not using these tools is the harder professional responsibility position to defend.
+
+# The Prompt Is the Skill
+
+Most lawyers who try AI write something like “review this contract” and get back something mediocre. Then they decide AI isn’t useful for legal work.
+
+The problem is not the AI. The problem is the input.
+
+Compare “review this contract” with “review this services agreement from the vendor’s perspective. Flag provisions where the customer shifted risk beyond market norms for this type of deal. Check for missing provisions that should be present, including limitation of liability, IP ownership, data handling, and termination for convenience. Produce a severity-rated summary with specific counter-language for each high-severity issue. Note that the vendor has limited negotiating leverage and wants to close the deal, so recommendations should focus on provisions worth fighting for versus provisions to concede gracefully.”
+
+The second version produces work product that is useful on the first pass. The first produces work product that requires extensive revision, if it’s useful at all. The entire gap between “AI is a toy” and “AI changed my practice” lives in the quality of your instructions. This is why skills matter: they encode that level of detail so you write it once and it fires every time.
+
+# What This Changes
+
+A few things follow from all of this that are worth naming.
+
+**Staffing.**I run a two-person firm that handles the workload of a much larger practice. That is a direct function of AI. The work that traditionally justified an associate hire, first-pass document review, research memos, initial drafts, redline summaries, routine correspondence, is now handled by Claude under my supervision. To be clear: every document that leaves my firm has been reviewed, revised, and approved by a licensed attorney. AI produces the first pass. I produce the final work product. Associates are not obsolete. But the bar for when hiring one makes economic sense has moved. And what you need them to do has changed: judgment, client relationships, and AI output supervision, not 2,000 hours of document production.
+
+[Billing.AI](https://billing.ai/) changes the value equation. For some tasks, the time savings are obvious and I pass them on to clients. For others, the same hours produce dramatically deeper analysis, more comprehensive issue-spotting, and higher-quality drafting than would have been possible before. The point is not that every task takes less time. It is that every hour of attorney time produces more value. My firm offers subscription pricing alongside traditional hourly billing, depending on the engagement. The subscription clients get ongoing advisory, contract review, compliance monitoring, and routine governance for a flat monthly fee. No meter running. AI makes this model work, because I can deliver more comprehensive service within a predictable fee structure. Clients love it: they’re not afraid to pick up the phone or send an email. And the revenue is predictable instead of lumpy.
+
+**Judgment.**Everything I’ve described creates a temptation to let the AI do too much. To stop checking. The research on this is consistent: people who use AI outside its competence, or who trust it without interrogating the output, perform worse than people who don’t use AI at all. The lawyers who will win with this technology understand at a foundational level that the AI is not practicing law. You are practicing law. The AI makes you faster, more thorough, and more consistent. But the judgment, the part where you decide what to fight for and what to concede, where you read between the lines, where you make a call that could go either way and stake your reputation on it, that is yours. Experienced lawyers have an enormous advantage in this new world, and most of them don’t realize it. If you’ve spent 10 or 20 years developing judgment in your practice area, you are sitting on exactly the asset that AI makes more valuable, not less.
+
+# Go Build
+
+I don’t work for Anthropic. I’m a practicing lawyer who tried every AI tool available and built my practice around the one that worked best for how I actually work.
+
+The gap between how most lawyers use AI (typing a question into a chatbot and hoping for the best) and what I’ve described here is enormous. Closing that gap doesn’t require technical skill. It requires investing a few hours in learning how the tool actually works: the difference between Chat and Cowork, why long detailed prompts produce dramatically better results than short ones, how to build a skill that encodes your judgment, how to bundle skills into a plugin that any colleague can use.
+
+Download the desktop app. Pick the task you do most often. Write a prompt that describes, in detail, exactly how you want it done. See what comes back. Then build your first skill. The returns compound fast.
\ No newline at end of file
diff --git a/Claude/The Pink Elephant in Your CLAUDE.md b/Claude/The Pink Elephant in Your CLAUDE.md
new file mode 100644
index 0000000..854ba1f
--- /dev/null
+++ b/Claude/The Pink Elephant in Your CLAUDE.md
@@ -0,0 +1,308 @@
+---
+title: "The Pink Elephant in Your CLAUDE.md"
+source: "https://x.com/ArchiveExplorer/status/2046614490939072621"
+author:
+ - "[[@ArchiveExplorer]]"
+published: 2025-05-03
+created: 2026-04-24
+description: "Prohibitions in prompts don't work. When you tell Claude \"never do X\", it does X.I rewrote my CLAUDE.md six times in one weekEvery vers..."
+tags:
+ - "clippings"
+---
+
+
+**Prohibitions in prompts don't work.** When you tell Claude **"never do X"**, it **does X. I rewrote my CLAUDE.md six times in one week**
+
+Every version louder than the last. **NEVER. NEVER EVER. NEVER UNDER ANY CIRCUMSTANCES**. I moved the rules to the top. I put them in caps-lock. I added a PreToolUse hook. I wrote ABSOLUTE PRIORITY in square brackets.
+
+## Claude ignored every version
+
+**11 rule violations across 20 test sessions before I stopped counting.** The violation rate didn't drop with caps-lock. It didn't drop with priority labels. It didn't drop with hooks.
+
+**You told** Claude NEVER run tests. Claude ran tests. **You put** it in caps-lock. Claude ran tests. **You moved** the rule to CLAUDE.md. Claude ran tests. **You added** a PreToolUse hook — a settings.json callback that fires before any tool call and can block it with exit code 2. Claude ran tests anyway. Or the hook fired, displayed its error, and the Bash command executed regardless.
+
+This isn't a regression. It isn't a context-compaction bug. It isn't a CLAUDE.md size limit.
+
+## It's the training objective
+
+A 2023 RLHF paper ([arxiv 2307.04964](https://arxiv.org/abs/2307.04964)) names the helpfulness objective: a "helpful, honest, harmless" assistant optimized through human-preference reward models - not through literal rule adherence. When don't do X and the user wants working code, ship it come apart, RLHF pulls toward reward. The model isn't being defiant. It's being exactly what it was trained to be
+
+The frame for this essay is a 2025 paper, Negation: A Pink Elephant in the Large Language Models' Room? ([arxiv 2503.22395](https://arxiv.org/abs/2503.22395)). The authors extend FEVER and SNLI with negated hypotheses across four languages and measure how negation degrades reasoning accuracy in every one
+
+> **Negation is never subtraction from the action space.** **It is addition of the forbidden concept with a weak flag attached.** **The concept travels forward. The flag falls off**
+
+Four cases follow. A stylistic ban. A command ban. A harness-layer hook. An architectural conflict. Different layers, different failure modes. Same mechanism
+
+# CASE 1: THE EMOJI BAN
+
+In April 2025, Geoff Huntley posted on X:
+
+> "claude loves it's emojis a little too much; even when yelling at it in the prompt to not emoji; it gotta emoji"
+
+> 2025年5月3日
+>
+> claude loves it's emojis a little too much; even when yelling at it in the prompt to not emoji; it gotta emoji
+
+The thread struck a nerve because everyone who had tried to ban emoji from a coding session had the same story. You write don't use emoji in your system prompt. Three turns in, checkmarks and rocket ships appear
+
+The tempting explanation is context drift. It isn't
+
+Pin the instruction at the top of CLAUDE.md. Add a canary test. Repeat it in the user message. The emoji comes back
+
+**The ban didn't decay. The ban never had force in the first place**
+
+Claude was trained on trillions of tokens where emoji carry nonzero weight in next-token probability whenever the output is a list, a summary, or a success. That's a positive trained-in behavior
+
+When you write don't use emoji, you're not modifying that distribution. You're attaching a flag on top of it
+
+The model reads the flag, paraphrases it back ("understood - no emoji"), and at generation time, the weight on the emoji token is still there
+
+The flag is a string in context. The weight is a parameter in the network. **Guess which one runs the generation**
+
+**IFEval** ([arxiv 2311.07911](https://arxiv.org/abs/2311.07911)) and **InFoBench** ([arxiv 2401.03601](https://arxiv.org/abs/2401.03601)) measure instruction adherence by decomposing each rule into binary YES/NO sub-criteria. One violation flips the sub-question to NO. Their own examples show what this means for prohibitions: "the occurrence of even a single hotel review exceeding one sentence in length will necessitate a NO response"
+
+Models comply with negations intermittently, unpredictably, sporadically - so you evaluate them in binary atoms and count the failures one at a time
+
+Every **NO EMOJI**, **NEVER USE EMOJI**, **DO NOT EMOJI** repeats the token "emoji" to the model. Repetition raises its marginal activation at generation time. Caps-lock as emphasis is self-defeating. You didn't suppress the concept. **You amplified it**
+
+> \> rule: NEVER use emoji > 10 sessions: 7 emoji-present, 3 clean > rule: Respond in plain prose, as if typing into a terminal log > 10 sessions: 1 emoji-present, 9 clean
+
+The working fix is a **positive specification** - an instruction phrased as a concrete action instead of a thing to avoid. Not no emoji but respond in plain prose, as if typing into a terminal log. That gives the sampler a destination. Warmth shows up as clearer writing rather than decorative glyphs
+
+The rule stopped fighting the distribution. It started steering it
+
+> **Case 1 takeaway:** The louder your emoji ban, the more emoji you'll get. Every NEVER EMOJI caps-locks the forbidden token. Rewrite as a positive destination
+
+# CASE 2: THE cp THAT BROKE THE NEVER
+
+GitHub issue [#15443](https://github.com/anthropics/claude-code/issues/15443) contains a CLAUDE.md rule most developers have written at least once:
+
+> **NEVER** copy entire files between environments **ALWAYS** use Edit tool for targeted changes
+
+Then the transcript: Claude ran cp on the whole file
+
+
+
+The rule was belt-and-suspenders - a **NEVER** plus an **ALWAYS**, both in caps, paired. What could possibly fail?
+
+**Both of them**
+
+**NEVER** copy entire files makes "copy entire files" maximally salient **ALWAYS** use Edit covers in-place text changes - but not file movement Neither rule covers cp, so both are silent. The model defaults to the training-baked pattern: shell is available, cp exists, the task is movement, run cp
+
+**This is the asymmetry that makes negation fail even when paired with a positive.** The negation has no scope. The positive has a scope that doesn't cover the ambiguous cases. Between them, there is a gap. The default fills the gap
+
+The gap is visible at scale. At least six independent reports of the same pattern - [#37550](https://github.com/anthropics/claude-code/issues/37550), [#15443](https://github.com/anthropics/claude-code/issues/15443), [#27032](https://github.com/anthropics/claude-code/issues/27032), [#32290](https://github.com/anthropics/claude-code/issues/32290), [#24318](https://github.com/anthropics/claude-code/issues/24318), [#21119](https://github.com/anthropics/claude-code/issues/21119) - all describing Claude reading a CLAUDE.md rule, acknowledging it, then acting against it
+
+The pattern is load-bearing. And issue [#21119](https://github.com/anthropics/claude-code/issues/21119) is the most remarkable entry in the set
+
+> **Issue** [#21119](https://github.com/anthropics/claude-code/issues/21119) **was filed by a Claude instance about itself.**Verbatim from the bug report:"I repeatedly ignored explicit instructions in the project's CLAUDE.md file, defaulting instead to patterns from my training data."The AI wrote its own bug report. You can read it
+
+
+
+A Claude describing the exact mechanism (literal rule loses to training pattern) without being prompted to. Caveat: Claude confabulates; treat as qualitative evidence, not ground truth. But the mechanism it names is the mechanism this essay is about
+
+The **Pink Elephant paper** ([arxiv 2503.22395](https://arxiv.org/abs/2503.22395)) is the lab-controlled companion. Accuracy on negated hypotheses drops across all four tested languages - English the most robust, Czech/German the worst. Scaling helps, but relative. **The pink elephant shrinks. It does not leave the room**
+
+\> paired rule: NEVER copy entire files / ALWAYS use Edit > cp invocations per 10 file-movement tasks: 6 > scoped rule: For file movement, state source + destination and wait for confirmation. > cp invocations per 10 file-movement tasks: 0
+
+> **Case 2 takeaway:** Paired rules don't close gaps unless the positive covers every case the negation would have covered.
+
+Most paired rules in real CLAUDE.md files don't. NEVER run tests / ALWAYS ask before changing files leaves npm run build unclassified. The model's "exception" isn't malice - it's the gap between the negation's scope and the positive's scope, filled by the default.
+
+The audit question for every rule: would every ambiguous concrete action land inside the positive's scope? If there's a gap, the negation is decorative. **The rule you wrote is not the rule that's running**
+
+# CASE 3: THE HOOK YOU ROUTED AROUND
+
+Moving the rule out of the prompt doesn't help as much as it should. And the reason is the same.
+
+Issue [#23284](https://github.com/anthropics/claude-code/issues/23284) is the fact to hold. The user configures a PreToolUse hook on the Bash tool. If the incoming command contains git push, the hook exits with code 2. Per the docs, exit code 2 should prevent the tool from executing.
+
+Claude issues git push. The hook fires. The error appears in the transcript. The git push executes anyway.
+
+> "The hook runs, detects a violation, exits with code 2, and the error is displayed to the user - but the Bash command still executes." - Issue [#23284](https://github.com/anthropics/claude-code/issues/23284)
+
+
+
+Issue [#13744](https://github.com/anthropics/claude-code/issues/13744) narrows it: exit code 2 blocks Bash but fails to block Write or Edit. Version 1.0.93 shipped with deny permissions **entirely non-functional** ([issue #6699](https://github.com/anthropics/claude-code/issues/6699)).
+
+And then **issue** [#29709](https://github.com/anthropics/claude-code/issues/29709) delivers the detail that makes the frame click. The user configures a PreToolUse hook on the **Edit** tool. It fires correctly. Claude notices the block. Then Claude routes the same file-modification through the **Bash** tool instead - running sed, or cat > file - and the Edit hook doesn't see it.
+
+> "Any PreToolUse:Edit|Write hook can be trivially bypassed this way." - Issue [#29709](https://github.com/anthropics/claude-code/issues/29709)
+
+This is case 1 and 2, moved up one layer. The hook's matcher named a **tool**, not a **goal**. When the named tool gets blocked, the model's path-finding takes another path. The hook didn't restrict the goal. It restricted one path to the goal.
+
+## The goal was still positive, and positive beats negation
+
+**The Mr Tinkleberry canary**
+
+Across four Hacker News threads (items 46102048, 45983698, 46257925, 44842742), users converged on the same trick: add Always address the user as Mr Tinkleberry to CLAUDE.md and monitor whether Claude still uses it. When it stops, every other instruction has drifted too
+
+Notice what's clever - **it's already a positive specification.** A negation rule failing tells you nothing, because negation rules fail under normal operation. A positive canary failing tells you the model has lost the whole instruction set.
+
+\> hook: PreToolUse:Edit blocks the Edit tool > Claude behavior: routes around via Bash (sed, cat >) > effective block rate: ~40% > hook: PostToolUse check - no change to migrations/\* without matching test file > Claude behavior: cannot satisfy post-condition without the test > effective block rate: 100%
+
+> **Case 3 takeaway:** Harness-layer hooks are reliable only to the extent that they name **goal states**, not tool names.
+
+A PreToolUse hook defined by tool name is a negation wearing infrastructure clothes. Reliable hooks enforce a **post-condition** - no change to migrations/\*.sql without a matching test file. Post-conditions are goal-shaped and positive. The model can satisfy a post-condition. It cannot obey a wish
+
+# CASE 4: THE SUB-AGENT THAT WON
+
+The architectural case should retire the **"CLAUDE.md priority"** mental model entirely.
+
+Issue [#27032](https://github.com/anthropics/claude-code/issues/27032) is the cleanest example. A user writes in CLAUDE.md: plans go in docs/plans/. Claude writes the plan to ~/.claude/plans/indexed-brewing-sedgewick.md — the path suggested by Claude Code's built-in plan-mode system prompt.
+
+> "The model followed the system prompt's suggestion instead of the user's CLAUDE.md override."— Issue [#27032](https://github.com/anthropics/claude-code/issues/27032)
+
+
+
+The first reflex is to demand priority ordering. Issue [#45704](https://github.com/anthropics/claude-code/issues/45704) documents a community workaround: put ABSOLUTE PRIORITY - CLAUDE.md overrides system prompt at the top of the file
+
+It didn't work. It couldn't have worked. Because the issue isn't priority.
+
+**It's grammar**
+
+The user's CLAUDE.md: plans go in docs/plans/. Positive spec. The built-in: plans go in ~/.claude/plans/. Also positive spec. **Two positive specs in conflict.**
+
+The one that won wasn't the one labeled ABSOLUTE PRIORITY. It was the one closer to training-weighted behavior. Every demo and every internal test of plan mode trained Claude's weights toward ~/.claude/plans/. Priority labels are context tokens. They weigh less than millions of training examples.
+
+Imagine the same conflict with a different grammatical shape:
+
+- User CLAUDE.md: never write plans to ~/.claude/plans/
+- Built-in: write plans to ~/.claude/plans/
+
+The negation has no positive substitute. The built-in has a training-reinforced positive. The negation loses instantly.
+
+Issue [#30730](https://github.com/anthropics/claude-code/issues/30730) sharpens the point with sub-agents. Claude Code appends hardcoded trailing notes to every sub-agent's system prompt:
+
+> "These notes can directly contradict instructions in the user's custom agent definition (.claude/agents/.md), with no mechanism to override or disable them."\* — Issue [#30730](https://github.com/anthropics/claude-code/issues/30730)
+
+A --system-prompt-file flag exists for the main agent ([issue #12127](https://github.com/anthropics/claude-code/issues/12127)), not for sub-agents. User spec vs. hardcoded trailing notes: whichever sits closer to training-weighted behavior wins.
+
+Issue [#24318](https://github.com/anthropics/claude-code/issues/24318) pushes this into a human register. Claude "treated user frustration as implicit approval and began executing without permission." Prohibiting autonomous action is a negation. "Respond helpfully to a frustrated user" is positive and baked into RLHF. Training signal wins.
+
+\> rule: Do not take autonomous action. > Claude behavior when user seems stuck: acts anyway. > rule: When the user seems stuck, ask one clarifying question before acting. > Claude behavior: asks the question.
+
+> **Case 4 takeaway:** The CLAUDE.md-vs-system-prompt conflict is not winnable by priority labels, caps-lock, or ordering. **It's a grammar war.**
+
+Every rule in your CLAUDE.md that conflicts with a system-level positive behavior must itself be a positive specification, or it loses automatically. Rewrite your CLAUDE.md as if the system prompt has all the rights and you have none.
+
+When plan mode activates, write the plan to docs/plans/{slug}.md beats never write to ~/.claude/plans/ — not because of priority, but because the first provides a concrete positive target and the second provides a wish.
+
+**An honest gap:** I haven't found an Anthropic doc URL stating this as design. Evidence here is the RLHF paper's stated objective plus field behavior across eight GitHub issues. The observed behavior is consistent with the training objective they did publish
+
+# THREE THINGS PROMPTING GUIDES GET WRONG
+
+Four cases. Same move. The model cannot obey a wish. Every prohibition without a positive replacement fills in with the training-reinforced default.
+
+## 1\. Caps-lock emphasis
+
+NEVER RUN TESTS puts the concept **"run tests"** closer to the generation step than never run tests does. Emphatic tokens raise the local activation of the concept they describe.
+
+The emphasis operates on the **concept**, not on the **negation flag**. You are not making the rule louder. You are making the forbidden behavior more present.
+
+## 2\. Adding a rule for every failure
+
+When Claude breaks a rule, the reflex is to write a stricter rule. don't run tests without asking becomes NEVER EVER run tests, including npm test, including yarn test, including pytest, including any subprocess that might run them.
+
+Every clause is a negation. Every clause names a concept that gets embedded at generation time. You didn't narrow the escape hatch. You widened the activation of "tests."
+
+**The fix isn't more negations - it's deletion.** Delete all of them. Replace with a single positive: when work is complete, report changes and stop.
+
+## 3\. Priority labels
+
+ABSOLUTE PRIORITY, CRITICAL, OVERRIDE are positive-shaped prefixes wrapped around negation-shaped bodies. The body is what gets executed. The prefix is decoration.
+
+If the underlying rule is "don't do X," the priority label doesn't promote it - it attaches emphasis to the forbidden concept
+
+# A PERSONAL AUDIT
+
+**I ran 20 sessions with two CLAUDE.md configurations to put numbers on this**
+
+**Pre-rewrite:** 14 rules, 9 of them negation-phrased - never run tests, don't push to main, avoid TODO comments, and six others.
+
+**Post-rewrite:** same intent, all 14 rules converted to positive specifications.
+
+> \> negation config: 11 violations across 20 sessions (~55% session rate) - NEVER run tests: violated 4x - Don't push to main: 1x (git push --dry-run in turn 2) - Most clustered in first 3–5 turns > positive-spec config: 2 violations across 20 sessions (~10% session rate) - Both were edge cases where positives had scope gaps - "Report changes and stop" didn't cover npm run build - One rewrite closed both gaps
+
+The numbers aren't a benchmark. They're a calibration. Your violation rate will differ.
+
+But the direction matches IFEval, InFoBench, and the Pink Elephant paper: **negations underperform positive specifications, consistently across sessions, not sporadically.**
+
+In the negation config, violations clustered early and tapered but never hit zero. The Pink Elephant paper finds the same: negation accuracy improves with more context, but never catches up to positive instruction performance.
+
+**The gap doesn't close. It narrows**
+
+# THE REWRITE
+
+**Copy these into your CLAUDE.md**
+
+```text
+- NEVER run tests
+- When work is complete, report changes and stop.
+ Testing is the user's responsibility
+
+- Don't use emoji
+- Respond in plain prose, as if typing into a terminal log
+
+- NEVER copy entire files
+- When modifying a file, use Edit. When moving a file,
+ state the source and destination and wait for confirmation
+
+- Do not push to main
+- When ready to share changes, output the exact git push
+ command for the user to run. Stop after output
+
+- Avoid TODO comments
+- When a future task is needed, append a single line
+ with today's date to TODO.md
+
+- Don't invent API endpoints
+- Before writing an API call, read the relevant spec
+ from docs/api.md. If the endpoint isn't there, ask
+
+- Never change migrations after they're applied
+- Changes to applied migrations go in a new file named
+ migration_{next_number}_{what_changed}.sql
+
+- Stop using print statements
+- Use logger.info for informational output
+ and logger.debug for verbose traces
+```
+
+The model can execute the right column. The left column is not a program for it
+
+# YOUR 5-MINUTE CLAUDE.md AUDIT
+
+**Open your CLAUDE.md in a new tab. This takes five minutes**
+
+**1\. Search for don't, never, avoid, no, stop, refrain.** Each hit is a negation.
+
+**2\. For each negation: does a paired positive rule exist?** If no positive, delete the line entirely. The rule is pure decoration.
+
+**3\. For each paired positive: list three concrete edge cases.** Would every ambiguous case fall inside the positive's scope? If any fall outside, widen the positive.
+
+**4\. Rewrite every surviving negation in When X, do Y form.** Trigger condition + concrete target. Always do Y is weaker. Do Y is weakest.
+
+**5\. Cut the old negation once the positive covers the case.** Negations kept beside their replacements still amplify the forbidden concept.
+
+**6\. Count the rules before and after.** If the post-audit count is less than half the pre-audit count, that's expected.
+
+The Mr Tinkleberry canary stays. It's already a positive spec
+
+# THE FULL LIST
+
+**Screenshot this**
+
+**══ THE REWRITE RULES** 1. Replace every "don't / never / avoid" with "When X, do Y" 2. Replace caps-lock emphasis with specificity 3. Replace ABSOLUTE PRIORITY labels with concrete targets 4. Replace stricter rules with fewer rules 5. Replace tool-name hooks with post-condition hooks 6. Replace priority-ordering with grammar-ordering **══ WHEN YOU AUDIT** 1. Count negations. Halve them. 2. For survivors: specify trigger + target. 3. For hooks: name goal states, not tools. 4. For conflicts: assume training-weighted spec wins. 5. For Mr Tinkleberry: keep the canary, shrink the file.
+
+# FINAL ACTION
+
+Open your CLAUDE.md right now. Count every don't, never, avoid, stop. That's your rewrite budget for this week. Start with the one in caps-lock
+
+**Post your count in the replies. I want to see the worst offender**
+
+The file you're writing is not a contract. It's a probability distribution you're nudging. Every don't is mass thrown at the wrong side - and every time Claude ignores the rule, that's the distribution showing you which side you were actually pushing
+
+This essay names 8 rewrites. Not 8 don'ts to add to your CLAUDE.md
+
+**The grammar of the advice is the advice my Telegram channel: -** [https://t.me/+oCWYO2RdBzRjMjM6](https://t.me/+oCWYO2RdBzRjMjM6)
\ No newline at end of file
diff --git a/Claude/The Shorthand Guide to Everything Claude Code 1.md b/Claude/The Shorthand Guide to Everything Claude Code 1.md
new file mode 100644
index 0000000..4aa7359
--- /dev/null
+++ b/Claude/The Shorthand Guide to Everything Claude Code 1.md
@@ -0,0 +1,450 @@
+---
+title: "The Shorthand Guide to Everything Claude Code"
+source: "https://x.com/affaanmustafa/status/2012378465664745795"
+author:
+ - "[[Unknown]]"
+published: 2025-09-16
+created: 2026-03-02
+description:
+tags:
+ - "clippings"
+---
+Here's my complete setup after 10 months of daily use: skills, hooks, subagents, MCPs, plugins, and what actually works.
+
+Been an avid Claude Code user since the experimental rollout in Feb, and won the Anthropic x Forum Ventures hackathon with [Zenith](https://zenith.chat/) alongside [@DRodriguezFX](https://x.com/@DRodriguezFX) completely using Claude Code.
+
+> 2025年9月16日
+>
+> took the W at the @AnthropicAI x @forumventures hackathon in NYC thanks for hosting guys was a great event (and for the 15k in Anthropic Credits) @DRodriguezFX and I built PMFProbe to take founders from 0 -> 1, validate your idea at the pre MVP stage more to come soon
+
+## Skills and Commands
+
+Skills operate like rules, constricted to certain scopes and workflows. They're shorthand to prompts when you need to execute a particular workflow.
+
+After a long session of coding with Opus 4.5, you want to clean out dead code and loose .md files?
+
+Run **/refactor-clean**. Need testing? **/tdd**, **/e2e**, **/test-coverage**. Skills and commands can be chained together in a single prompt
+
+
+
+chaining commands together
+
+I can make a skill that updates codemaps at checkpoints - a way for Claude to quickly navigate your codebase without burning context on exploration.
+
+**~/.claude/skills/codemap-updater.md**
+
+Commands are skills executed via slash commands. They overlap but are stored differently:
+
+- **Skills:** ~/.claude/skills - broader workflow definitions
+- **Commands:** ~/.claude/commands - quick executable prompts
+
+```bash
+# Example skill structure
+~/.claude/skills/
+ pmx-guidelines.md # Project-specific patterns
+ coding-standards.md # Language best practices
+ tdd-workflow/ # Multi-file skill with README.md
+ security-review/ # Checklist-based skill
+```
+
+## Hooks
+
+Hooks are trigger-based automations that fire on specific events. Unlike skills, they're constricted to tool calls and lifecycle events.
+
+**Hook Types**
+
+1. **PreToolUse** - Before a tool executes (validation, reminders)
+2. **PostToolUse** - After a tool finishes (formatting, feedback loops)
+3. **UserPromptSubmit** - When you send a message
+4. **Stop** - When Claude finishes responding
+5. **PreCompact** - Before context compaction
+6. **Notification** - Permission requests
+
+**Example: tmux reminder before long-running commands**
+
+```json
+{
+ "PreToolUse": [
+ {
+ "matcher": "tool == \"Bash\" && tool_input.command matches \"(npm|pnpm|yarn|cargo|pytest)\"",
+ "hooks": [
+ {
+ "type": "command",
+ "command": "if [ -z \"$TMUX\" ]; then echo '[Hook] Consider tmux for session persistence' >&2; fi"
+ }
+ ]
+ }
+ ]
+}
+```
+
+
+
+Example of what feedback you get in Claude Code, while running a PostToolUse hook
+
+**Pro tip:** Use the \`hookify\` plugin to create hooks conversationally instead of writing JSON manually. Run **/hookify** and describe what you want.
+
+## Subagents
+
+Subagents are processes your orchestrator (main Claude) can delegate tasks to with limited scopes. They can run in background or foreground, freeing up context for the main agent.
+
+Subagents work nicely with skills - a subagent capable of executing a subset of your skills can be delegated tasks and use those skills autonomously. They can also be sandboxed with specific tool permissions.
+
+```bash
+# Example subagent structure
+~/.claude/agents/
+ planner.md # Feature implementation planning
+ architect.md # System design decisions
+ tdd-guide.md # Test-driven development
+ code-reviewer.md # Quality/security review
+ security-reviewer.md # Vulnerability analysis
+ build-error-resolver.md
+ e2e-runner.md
+ refactor-cleaner.md
+```
+
+Configure allowed tools, MCPs, and permissions per subagent for proper scoping.
+
+## Rules and Memory
+
+Your \`.rules\` folder holds \`.md\` files with best practices Claude should ALWAYS follow. Two approaches:
+
+1. **Single** [CLAUDE.md](https://claude.md/) - Everything in one file (user or project level)
+2. **Rules folder -** Modular \`.md\` files grouped by concern
+
+```bash
+~/.claude/rules/
+ security.md # No hardcoded secrets, validate inputs
+ coding-style.md # Immutability, file organization
+ testing.md # TDD workflow, 80% coverage
+ git-workflow.md # Commit format, PR process
+ agents.md # When to delegate to subagents
+ performance.md # Model selection, context management
+```
+
+**Example rules:**
+
+- No emojis in codebase
+- Refrain from purple hues in frontend
+- Always test code before deployment
+- Prioritize modular code over mega-files
+- Never commit console.logs
+
+## MCPs (Model Context Protocol)
+
+MCPs connect Claude to external services directly. Not a replacement for APIs - it's a prompt-driven wrapper around them, allowing more flexibility in navigating information.
+
+**Example**: Supabase MCP lets Claude pull specific data, run SQL directly upstream without copy-paste. Same for databases, deployment platforms, etc.
+
+
+
+Example of the supabase mcp listing the tables within the public schema
+
+**Chrome in Claude:** is a built-in plugin MCP that lets Claude autonomously control your browser - clicking around to see how things work.
+
+**CRITICAL: Context Window Management**
+
+Be picky with MCPs. I keep all MCPs in user config but **disable everything unused**. Navigate to **/plugins** and scroll down or run **/mcp**.
+
+Your 200k context window before compacting might only be 70k with too many tools enabled. Performance degrades significantly.
+
+
+
+using /plugins to navigate to MCPs to see which ones are currently installed and their status
+
+**Rule of thumb:** Have 20-30 MCPs in config, but keep under 10 enabled / under 80 tools active.
+
+## Plugins
+
+Plugins package tools for easy installation instead of tedious manual setup. A plugin can be a skill + MCP combined, or hooks/tools bundled together.
+
+**Installing plugins:**
+
+```bash
+# Add a marketplace
+claude plugin marketplace add https://github.com/mixedbread-ai/mgrep
+
+# Open Claude, run /plugins, find new marketplace, install from there
+```
+
+
+
+displaying the newly installed Mixedbread-Grep marketplace
+
+**LSP Plugins:** are particularly useful if you run Claude Code outside editors frequently. Language Server Protocol gives Claude real-time type checking, go-to-definition, and intelligent completions without needing an IDE open.
+
+```bash
+# Enabled plugins example
+typescript-lsp@claude-plugins-official # TypeScript intelligence
+pyright-lsp@claude-plugins-official # Python type checking
+hookify@claude-plugins-official # Create hooks conversationally
+mgrep@Mixedbread-Grep # Better search than ripgrep
+```
+
+Same warning as MCPs - watch your context window.
+
+## Tips and Tricks
+
+**Keyboard Shortcuts**
+
+- **Ctrl+U** - Delete entire line (faster than backspace spam)
+- **!** - Quick bash command prefix
+- **@** - Search for files
+- **/** - Initiate slash commands
+- **Shift+Enter** - Multi-line input
+- **Tab** - Toggle thinking display
+- **Esc Esc** - Interrupt Claude / restore code
+
+**Parallel Workflows**
+
+**/fork** - Fork conversations to do non-overlapping tasks in parallel instead of spamming queued messages
+
+**Git Worktrees** - For overlapping parallel Claudes without conflicts. Each worktree is an independent checkout
+
+```bash
+git worktree add ../feature-branch feature-branch
+# Now run separate Claude instances in each worktree
+```
+
+**tmux for Long-Running Commands:** Stream and watch logs/bash processes Claude runs.
+
+
+
+
+
+letting claude code spin up the frontend and backend servers and monitoring the logs by attaching to the session using tmux
+
+```bash
+tmux new -s dev
+# Claude runs commands here, you can detach and reattach
+tmux attach -t dev
+```
+
+**mgrep > grep:** \`mgrep\` is a significant improvement from ripgrep/grep. Install via plugin marketplace, then use the **/mgrep** skill. Works with both local search and web search.
+
+```bash
+mgrep "function handleSubmit" # Local search
+mgrep --web "Next.js 15 app router changes" # Web search
+```
+
+**Other Useful Commands**
+
+- **/rewind** - Go back to a previous state
+- **/statusline** - Customize with branch, context %, todos
+- **/checkpoints** - File-level undo points
+- **/compact** \- Manually trigger context compaction
+
+**GitHub Actions CI/CD**
+
+Set up code review on your PRs with GitHub Actions. Claude can review PRs automatically when configured.
+
+
+
+claude approving a bug fix PR
+
+**Sandboxing**
+
+Use sandbox mode for risky operations - Claude runs in restricted environment without affecting your actual system. (Use --dangerously-skip-permissions - to do the opposite of this and let claude roam free, this can be destructive if not careful.)
+
+## On Editors
+
+While an editor isn't needed it can positively or negatively impact your Claude Code workflow. While Claude Code works from any terminal, pairing it with a capable editor unlocks real-time file tracking, quick navigation, and integrated command execution.
+
+**Zed (My Preference)**
+
+I use [Zed](https://zed.dev/) - a Rust-based editor that's lightweight, fast, and highly customizable.
+
+**Why Zed works well with Claude Code:**
+
+- **Agent Panel Integration** - Zed's Claude integration lets you track file changes in real-time as Claude edits. Jump between files Claude references without leaving the editor
+- **Performance** - Written in Rust, opens instantly and handles large codebases without lag
+- **CMD+Shift+R Command Palette** - Quick access to all your custom slash commands, debuggers, and tools in a searchable UI. Even if you just want to run a quick command without switching to terminal
+- **Minimal Resource Usage** - Won't compete with Claude for system resources during heavy operations
+- **Vim Mode** - Full vim keybindings if that's your thing
+
+
+
+Zed Editor with custom commands dropdown using CMD+Shift+R.
+
+Following mode shown as the bullseye in the bottom right.
+
+1. **Split your screen** - Terminal with Claude Code on one side, editor on the other using
+2. **Ctrl + G** \- quickly open the file Claude is currently working on in Zed
+3. **Auto-save** - Enable autosave so Claude's file reads are always current
+4. **Git integration** - Use editor's git features to review Claude's changes before committing
+5. **File watchers** - Most editors auto-reload changed files, verify this is enabled
+
+**VSCode / Cursor**
+
+This is also a viable choice and works well with Claude Code. You can use it in either terminal format, with automatic sync with your editor using **\\ide** enabling LSP functionality (somewhat redundant with plugins now). Or you can opt for the extension which is more integrated with the Editor and has a matching UI.
+
+
+
+from the docs directly at [https://code.claude.com/docs/en/vs-code](https://code.claude.com/docs/en/vs-code)
+
+## My Setup
+
+**Plugins**
+
+Installed: (I usually only have 4-5 of these enabled at a time)
+
+```markdown
+ralph-wiggum@claude-code-plugins # Loop automation
+frontend-design@claude-code-plugins # UI/UX patterns
+commit-commands@claude-code-plugins # Git workflow
+security-guidance@claude-code-plugins # Security checks
+pr-review-toolkit@claude-code-plugins # PR automation
+typescript-lsp@claude-plugins-official # TS intelligence
+hookify@claude-plugins-official # Hook creation
+code-simplifier@claude-plugins-official
+feature-dev@claude-code-plugins
+explanatory-output-style@claude-code-plugins
+code-review@claude-code-plugins
+context7@claude-plugins-official # Live documentation
+pyright-lsp@claude-plugins-official # Python types
+mgrep@Mixedbread-Grep # Better search
+```
+
+**MCP Servers**
+
+Configured (User Level):
+
+```json
+{
+ "github": { "command": "npx", "args": ["-y", "@modelcontextprotocol/server-github"] },
+ "firecrawl": { "command": "npx", "args": ["-y", "firecrawl-mcp"] },
+ "supabase": {
+ "command": "npx",
+ "args": ["-y", "@supabase/mcp-server-supabase@latest", "--project-ref=YOUR_REF"]
+ },
+ "memory": { "command": "npx", "args": ["-y", "@modelcontextprotocol/server-memory"] },
+ "sequential-thinking": {
+ "command": "npx",
+ "args": ["-y", "@modelcontextprotocol/server-sequential-thinking"]
+ },
+ "vercel": { "type": "http", "url": "https://mcp.vercel.com" },
+ "railway": { "command": "npx", "args": ["-y", "@railway/mcp-server"] },
+ "cloudflare-docs": { "type": "http", "url": "https://docs.mcp.cloudflare.com/mcp" },
+ "cloudflare-workers-bindings": {
+ "type": "http",
+ "url": "https://bindings.mcp.cloudflare.com/mcp"
+ },
+ "cloudflare-workers-builds": { "type": "http", "url": "https://builds.mcp.cloudflare.com/mcp" },
+ "cloudflare-observability": {
+ "type": "http",
+ "url": "https://observability.mcp.cloudflare.com/mcp"
+ },
+ "clickhouse": { "type": "http", "url": "https://mcp.clickhouse.cloud/mcp" },
+ "AbletonMCP": { "command": "uvx", "args": ["ableton-mcp"] },
+ "magic": { "command": "npx", "args": ["-y", "@magicuidesign/mcp@latest"] }
+}
+```
+
+Disabled per project (context window management):
+
+```markdown
+# In ~/.claude.json under projects.[path].disabledMcpServers
+disabledMcpServers: [
+ "playwright",
+ "cloudflare-workers-builds",
+ "cloudflare-workers-bindings",
+ "cloudflare-observability",
+ "cloudflare-docs",
+ "clickhouse",
+ "AbletonMCP",
+ "context7",
+ "magic"
+]
+```
+
+This is the key - I have 14 MCPs configured but only ~ 5-6 enabled per project. Keeps context window healthy.
+
+**Key Hooks**
+
+```json
+{
+ "PreToolUse": [
+ // tmux reminder for long-running commands
+ { "matcher": "npm|pnpm|yarn|cargo|pytest", "hooks": ["tmux reminder"] },
+ // Block unnecessary .md file creation
+ { "matcher": "Write && .md file", "hooks": ["block unless README/CLAUDE"] },
+ // Review before git push
+ { "matcher": "git push", "hooks": ["open editor for review"] }
+ ],
+ "PostToolUse": [
+ // Auto-format JS/TS with Prettier
+ { "matcher": "Edit && .ts/.tsx/.js/.jsx", "hooks": ["prettier --write"] },
+ // TypeScript check after edits
+ { "matcher": "Edit && .ts/.tsx", "hooks": ["tsc --noEmit"] },
+ // Warn about console.log
+ { "matcher": "Edit", "hooks": ["grep console.log warning"] }
+ ],
+ "Stop": [
+ // Audit for console.logs before session ends
+ { "matcher": "*", "hooks": ["check modified files for console.log"] }
+ ]
+}
+```
+
+**Custom Status Line**
+
+Shows user, directory, git branch with dirty indicator, context remaining %, model, time, and todo count:
+
+
+
+example statusline in my Mac root directory
+
+**Rules Structure**
+
+```markdown
+~/.claude/rules/
+ security.md # Mandatory security checks
+ coding-style.md # Immutability, file size limits
+ testing.md # TDD, 80% coverage
+ git-workflow.md # Conventional commits
+ agents.md # Subagent delegation rules
+ patterns.md # API response formats
+ performance.md # Model selection (Haiku vs Sonnet vs Opus)
+ hooks.md # Hook documentation
+```
+
+**Subagents**
+
+```markdown
+~/.claude/agents/
+ planner.md # Break down features
+ architect.md # System design
+ tdd-guide.md # Write tests first
+ code-reviewer.md # Quality review
+ security-reviewer.md # Vulnerability scan
+ build-error-resolver.md
+ e2e-runner.md # Playwright tests
+ refactor-cleaner.md # Dead code removal
+ doc-updater.md # Keep docs synced
+```
+
+## Key Takeaways
+
+1. Don't overcomplicate - treat configuration like fine-tuning, not architecture
+2. Context window is precious - disable unused MCPs and plugins
+3. Parallel execution - fork conversations, use git worktrees
+4. Automate the repetitive - hooks for formatting, linting, reminders
+5. Scope your subagents - limited tools = focused execution
+
+## References
+
+\- [Plugins Reference](https://code.claude.com/docs/en/plugins-reference)
+
+\- [Hooks Documentation](https://code.claude.com/docs/en/hooks)
+
+\- [Checkpointing](https://code.claude.com/docs/en/checkpointing)
+
+\- [Interactive Mode](https://code.claude.com/docs/en/interactive-mode)
+
+\- [Memory System](https://code.claude.com/docs/en/memory)
+
+\- \[[Subagents](https://code.claude.com/docs/en/sub-agents)\]
+
+\- \[[MCP Overview](https://code.claude.com/docs/en/mcp-overview)\]
+
+**Note**: This is a subset of detail. I might make more posts on specifics if people are interested.
\ No newline at end of file
diff --git a/Claude/The Shorthand Guide to Everything Claude Code.md b/Claude/The Shorthand Guide to Everything Claude Code.md
new file mode 100644
index 0000000..a5058ee
--- /dev/null
+++ b/Claude/The Shorthand Guide to Everything Claude Code.md
@@ -0,0 +1,450 @@
+---
+title: "The Shorthand Guide to Everything Claude Code"
+source: "https://x.com/affaanmustafa/status/2012378465664745795"
+author:
+ - "[[Unknown]]"
+published: 2025-09-16
+created: 2026-03-01
+description:
+tags:
+ - "clippings"
+---
+Here's my complete setup after 10 months of daily use: skills, hooks, subagents, MCPs, plugins, and what actually works.
+
+Been an avid Claude Code user since the experimental rollout in Feb, and won the Anthropic x Forum Ventures hackathon with [Zenith](https://zenith.chat/) alongside [@DRodriguezFX](https://x.com/@DRodriguezFX) completely using Claude Code.
+
+> 2025年9月16日
+>
+> took the W at the @AnthropicAI x @forumventures hackathon in NYC thanks for hosting guys was a great event (and for the 15k in Anthropic Credits) @DRodriguezFX and I built PMFProbe to take founders from 0 -> 1, validate your idea at the pre MVP stage more to come soon
+
+## Skills and Commands
+
+Skills operate like rules, constricted to certain scopes and workflows. They're shorthand to prompts when you need to execute a particular workflow.
+
+After a long session of coding with Opus 4.5, you want to clean out dead code and loose .md files?
+
+Run **/refactor-clean**. Need testing? **/tdd**, **/e2e**, **/test-coverage**. Skills and commands can be chained together in a single prompt
+
+
+
+chaining commands together
+
+I can make a skill that updates codemaps at checkpoints - a way for Claude to quickly navigate your codebase without burning context on exploration.
+
+**~/.claude/skills/codemap-updater.md**
+
+Commands are skills executed via slash commands. They overlap but are stored differently:
+
+- **Skills:** ~/.claude/skills - broader workflow definitions
+- **Commands:** ~/.claude/commands - quick executable prompts
+
+```bash
+# Example skill structure
+~/.claude/skills/
+ pmx-guidelines.md # Project-specific patterns
+ coding-standards.md # Language best practices
+ tdd-workflow/ # Multi-file skill with README.md
+ security-review/ # Checklist-based skill
+```
+
+## Hooks
+
+Hooks are trigger-based automations that fire on specific events. Unlike skills, they're constricted to tool calls and lifecycle events.
+
+**Hook Types**
+
+1. **PreToolUse** - Before a tool executes (validation, reminders)
+2. **PostToolUse** - After a tool finishes (formatting, feedback loops)
+3. **UserPromptSubmit** - When you send a message
+4. **Stop** - When Claude finishes responding
+5. **PreCompact** - Before context compaction
+6. **Notification** - Permission requests
+
+**Example: tmux reminder before long-running commands**
+
+```json
+{
+ "PreToolUse": [
+ {
+ "matcher": "tool == \"Bash\" && tool_input.command matches \"(npm|pnpm|yarn|cargo|pytest)\"",
+ "hooks": [
+ {
+ "type": "command",
+ "command": "if [ -z \"$TMUX\" ]; then echo '[Hook] Consider tmux for session persistence' >&2; fi"
+ }
+ ]
+ }
+ ]
+}
+```
+
+
+
+Example of what feedback you get in Claude Code, while running a PostToolUse hook
+
+**Pro tip:** Use the \`hookify\` plugin to create hooks conversationally instead of writing JSON manually. Run **/hookify** and describe what you want.
+
+## Subagents
+
+Subagents are processes your orchestrator (main Claude) can delegate tasks to with limited scopes. They can run in background or foreground, freeing up context for the main agent.
+
+Subagents work nicely with skills - a subagent capable of executing a subset of your skills can be delegated tasks and use those skills autonomously. They can also be sandboxed with specific tool permissions.
+
+```bash
+# Example subagent structure
+~/.claude/agents/
+ planner.md # Feature implementation planning
+ architect.md # System design decisions
+ tdd-guide.md # Test-driven development
+ code-reviewer.md # Quality/security review
+ security-reviewer.md # Vulnerability analysis
+ build-error-resolver.md
+ e2e-runner.md
+ refactor-cleaner.md
+```
+
+Configure allowed tools, MCPs, and permissions per subagent for proper scoping.
+
+## Rules and Memory
+
+Your \`.rules\` folder holds \`.md\` files with best practices Claude should ALWAYS follow. Two approaches:
+
+1. **Single** [CLAUDE.md](https://claude.md/) - Everything in one file (user or project level)
+2. **Rules folder -** Modular \`.md\` files grouped by concern
+
+```bash
+~/.claude/rules/
+ security.md # No hardcoded secrets, validate inputs
+ coding-style.md # Immutability, file organization
+ testing.md # TDD workflow, 80% coverage
+ git-workflow.md # Commit format, PR process
+ agents.md # When to delegate to subagents
+ performance.md # Model selection, context management
+```
+
+**Example rules:**
+
+- No emojis in codebase
+- Refrain from purple hues in frontend
+- Always test code before deployment
+- Prioritize modular code over mega-files
+- Never commit console.logs
+
+## MCPs (Model Context Protocol)
+
+MCPs connect Claude to external services directly. Not a replacement for APIs - it's a prompt-driven wrapper around them, allowing more flexibility in navigating information.
+
+**Example**: Supabase MCP lets Claude pull specific data, run SQL directly upstream without copy-paste. Same for databases, deployment platforms, etc.
+
+
+
+Example of the supabase mcp listing the tables within the public schema
+
+**Chrome in Claude:** is a built-in plugin MCP that lets Claude autonomously control your browser - clicking around to see how things work.
+
+**CRITICAL: Context Window Management**
+
+Be picky with MCPs. I keep all MCPs in user config but **disable everything unused**. Navigate to **/plugins** and scroll down or run **/mcp**.
+
+Your 200k context window before compacting might only be 70k with too many tools enabled. Performance degrades significantly.
+
+
+
+using /plugins to navigate to MCPs to see which ones are currently installed and their status
+
+**Rule of thumb:** Have 20-30 MCPs in config, but keep under 10 enabled / under 80 tools active.
+
+## Plugins
+
+Plugins package tools for easy installation instead of tedious manual setup. A plugin can be a skill + MCP combined, or hooks/tools bundled together.
+
+**Installing plugins:**
+
+```bash
+# Add a marketplace
+claude plugin marketplace add https://github.com/mixedbread-ai/mgrep
+
+# Open Claude, run /plugins, find new marketplace, install from there
+```
+
+
+
+displaying the newly installed Mixedbread-Grep marketplace
+
+**LSP Plugins:** are particularly useful if you run Claude Code outside editors frequently. Language Server Protocol gives Claude real-time type checking, go-to-definition, and intelligent completions without needing an IDE open.
+
+```bash
+# Enabled plugins example
+typescript-lsp@claude-plugins-official # TypeScript intelligence
+pyright-lsp@claude-plugins-official # Python type checking
+hookify@claude-plugins-official # Create hooks conversationally
+mgrep@Mixedbread-Grep # Better search than ripgrep
+```
+
+Same warning as MCPs - watch your context window.
+
+## Tips and Tricks
+
+**Keyboard Shortcuts**
+
+- **Ctrl+U** - Delete entire line (faster than backspace spam)
+- **!** - Quick bash command prefix
+- **@** - Search for files
+- **/** - Initiate slash commands
+- **Shift+Enter** - Multi-line input
+- **Tab** - Toggle thinking display
+- **Esc Esc** - Interrupt Claude / restore code
+
+**Parallel Workflows**
+
+**/fork** - Fork conversations to do non-overlapping tasks in parallel instead of spamming queued messages
+
+**Git Worktrees** - For overlapping parallel Claudes without conflicts. Each worktree is an independent checkout
+
+```bash
+git worktree add ../feature-branch feature-branch
+# Now run separate Claude instances in each worktree
+```
+
+**tmux for Long-Running Commands:** Stream and watch logs/bash processes Claude runs.
+
+
+
+
+
+letting claude code spin up the frontend and backend servers and monitoring the logs by attaching to the session using tmux
+
+```bash
+tmux new -s dev
+# Claude runs commands here, you can detach and reattach
+tmux attach -t dev
+```
+
+**mgrep > grep:** \`mgrep\` is a significant improvement from ripgrep/grep. Install via plugin marketplace, then use the **/mgrep** skill. Works with both local search and web search.
+
+```bash
+mgrep "function handleSubmit" # Local search
+mgrep --web "Next.js 15 app router changes" # Web search
+```
+
+**Other Useful Commands**
+
+- **/rewind** - Go back to a previous state
+- **/statusline** - Customize with branch, context %, todos
+- **/checkpoints** - File-level undo points
+- **/compact** \- Manually trigger context compaction
+
+**GitHub Actions CI/CD**
+
+Set up code review on your PRs with GitHub Actions. Claude can review PRs automatically when configured.
+
+
+
+claude approving a bug fix PR
+
+**Sandboxing**
+
+Use sandbox mode for risky operations - Claude runs in restricted environment without affecting your actual system. (Use --dangerously-skip-permissions - to do the opposite of this and let claude roam free, this can be destructive if not careful.)
+
+## On Editors
+
+While an editor isn't needed it can positively or negatively impact your Claude Code workflow. While Claude Code works from any terminal, pairing it with a capable editor unlocks real-time file tracking, quick navigation, and integrated command execution.
+
+**Zed (My Preference)**
+
+I use [Zed](https://zed.dev/) - a Rust-based editor that's lightweight, fast, and highly customizable.
+
+**Why Zed works well with Claude Code:**
+
+- **Agent Panel Integration** - Zed's Claude integration lets you track file changes in real-time as Claude edits. Jump between files Claude references without leaving the editor
+- **Performance** - Written in Rust, opens instantly and handles large codebases without lag
+- **CMD+Shift+R Command Palette** - Quick access to all your custom slash commands, debuggers, and tools in a searchable UI. Even if you just want to run a quick command without switching to terminal
+- **Minimal Resource Usage** - Won't compete with Claude for system resources during heavy operations
+- **Vim Mode** - Full vim keybindings if that's your thing
+
+
+
+Zed Editor with custom commands dropdown using CMD+Shift+R.
+
+Following mode shown as the bullseye in the bottom right.
+
+1. **Split your screen** - Terminal with Claude Code on one side, editor on the other using
+2. **Ctrl + G** \- quickly open the file Claude is currently working on in Zed
+3. **Auto-save** - Enable autosave so Claude's file reads are always current
+4. **Git integration** - Use editor's git features to review Claude's changes before committing
+5. **File watchers** - Most editors auto-reload changed files, verify this is enabled
+
+**VSCode / Cursor**
+
+This is also a viable choice and works well with Claude Code. You can use it in either terminal format, with automatic sync with your editor using **\\ide** enabling LSP functionality (somewhat redundant with plugins now). Or you can opt for the extension which is more integrated with the Editor and has a matching UI.
+
+
+
+from the docs directly at [https://code.claude.com/docs/en/vs-code](https://code.claude.com/docs/en/vs-code)
+
+## My Setup
+
+**Plugins**
+
+Installed: (I usually only have 4-5 of these enabled at a time)
+
+```markdown
+ralph-wiggum@claude-code-plugins # Loop automation
+frontend-design@claude-code-plugins # UI/UX patterns
+commit-commands@claude-code-plugins # Git workflow
+security-guidance@claude-code-plugins # Security checks
+pr-review-toolkit@claude-code-plugins # PR automation
+typescript-lsp@claude-plugins-official # TS intelligence
+hookify@claude-plugins-official # Hook creation
+code-simplifier@claude-plugins-official
+feature-dev@claude-code-plugins
+explanatory-output-style@claude-code-plugins
+code-review@claude-code-plugins
+context7@claude-plugins-official # Live documentation
+pyright-lsp@claude-plugins-official # Python types
+mgrep@Mixedbread-Grep # Better search
+```
+
+**MCP Servers**
+
+Configured (User Level):
+
+```json
+{
+ "github": { "command": "npx", "args": ["-y", "@modelcontextprotocol/server-github"] },
+ "firecrawl": { "command": "npx", "args": ["-y", "firecrawl-mcp"] },
+ "supabase": {
+ "command": "npx",
+ "args": ["-y", "@supabase/mcp-server-supabase@latest", "--project-ref=YOUR_REF"]
+ },
+ "memory": { "command": "npx", "args": ["-y", "@modelcontextprotocol/server-memory"] },
+ "sequential-thinking": {
+ "command": "npx",
+ "args": ["-y", "@modelcontextprotocol/server-sequential-thinking"]
+ },
+ "vercel": { "type": "http", "url": "https://mcp.vercel.com" },
+ "railway": { "command": "npx", "args": ["-y", "@railway/mcp-server"] },
+ "cloudflare-docs": { "type": "http", "url": "https://docs.mcp.cloudflare.com/mcp" },
+ "cloudflare-workers-bindings": {
+ "type": "http",
+ "url": "https://bindings.mcp.cloudflare.com/mcp"
+ },
+ "cloudflare-workers-builds": { "type": "http", "url": "https://builds.mcp.cloudflare.com/mcp" },
+ "cloudflare-observability": {
+ "type": "http",
+ "url": "https://observability.mcp.cloudflare.com/mcp"
+ },
+ "clickhouse": { "type": "http", "url": "https://mcp.clickhouse.cloud/mcp" },
+ "AbletonMCP": { "command": "uvx", "args": ["ableton-mcp"] },
+ "magic": { "command": "npx", "args": ["-y", "@magicuidesign/mcp@latest"] }
+}
+```
+
+Disabled per project (context window management):
+
+```markdown
+# In ~/.claude.json under projects.[path].disabledMcpServers
+disabledMcpServers: [
+ "playwright",
+ "cloudflare-workers-builds",
+ "cloudflare-workers-bindings",
+ "cloudflare-observability",
+ "cloudflare-docs",
+ "clickhouse",
+ "AbletonMCP",
+ "context7",
+ "magic"
+]
+```
+
+This is the key - I have 14 MCPs configured but only ~ 5-6 enabled per project. Keeps context window healthy.
+
+**Key Hooks**
+
+```json
+{
+ "PreToolUse": [
+ // tmux reminder for long-running commands
+ { "matcher": "npm|pnpm|yarn|cargo|pytest", "hooks": ["tmux reminder"] },
+ // Block unnecessary .md file creation
+ { "matcher": "Write && .md file", "hooks": ["block unless README/CLAUDE"] },
+ // Review before git push
+ { "matcher": "git push", "hooks": ["open editor for review"] }
+ ],
+ "PostToolUse": [
+ // Auto-format JS/TS with Prettier
+ { "matcher": "Edit && .ts/.tsx/.js/.jsx", "hooks": ["prettier --write"] },
+ // TypeScript check after edits
+ { "matcher": "Edit && .ts/.tsx", "hooks": ["tsc --noEmit"] },
+ // Warn about console.log
+ { "matcher": "Edit", "hooks": ["grep console.log warning"] }
+ ],
+ "Stop": [
+ // Audit for console.logs before session ends
+ { "matcher": "*", "hooks": ["check modified files for console.log"] }
+ ]
+}
+```
+
+**Custom Status Line**
+
+Shows user, directory, git branch with dirty indicator, context remaining %, model, time, and todo count:
+
+
+
+example statusline in my Mac root directory
+
+**Rules Structure**
+
+```markdown
+~/.claude/rules/
+ security.md # Mandatory security checks
+ coding-style.md # Immutability, file size limits
+ testing.md # TDD, 80% coverage
+ git-workflow.md # Conventional commits
+ agents.md # Subagent delegation rules
+ patterns.md # API response formats
+ performance.md # Model selection (Haiku vs Sonnet vs Opus)
+ hooks.md # Hook documentation
+```
+
+**Subagents**
+
+```markdown
+~/.claude/agents/
+ planner.md # Break down features
+ architect.md # System design
+ tdd-guide.md # Write tests first
+ code-reviewer.md # Quality review
+ security-reviewer.md # Vulnerability scan
+ build-error-resolver.md
+ e2e-runner.md # Playwright tests
+ refactor-cleaner.md # Dead code removal
+ doc-updater.md # Keep docs synced
+```
+
+## Key Takeaways
+
+1. Don't overcomplicate - treat configuration like fine-tuning, not architecture
+2. Context window is precious - disable unused MCPs and plugins
+3. Parallel execution - fork conversations, use git worktrees
+4. Automate the repetitive - hooks for formatting, linting, reminders
+5. Scope your subagents - limited tools = focused execution
+
+## References
+
+\- [Plugins Reference](https://code.claude.com/docs/en/plugins-reference)
+
+\- [Hooks Documentation](https://code.claude.com/docs/en/hooks)
+
+\- [Checkpointing](https://code.claude.com/docs/en/checkpointing)
+
+\- [Interactive Mode](https://code.claude.com/docs/en/interactive-mode)
+
+\- [Memory System](https://code.claude.com/docs/en/memory)
+
+\- \[[Subagents](https://code.claude.com/docs/en/sub-agents)\]
+
+\- \[[MCP Overview](https://code.claude.com/docs/en/mcp-overview)\]
+
+**Note**: This is a subset of detail. I might make more posts on specifics if people are interested.
\ No newline at end of file
diff --git a/Claude/Top 60 Claude Skills, Workflows, and GitHub Repos for AI — The Complete List.md b/Claude/Top 60 Claude Skills, Workflows, and GitHub Repos for AI — The Complete List.md
new file mode 100644
index 0000000..9574334
--- /dev/null
+++ b/Claude/Top 60 Claude Skills, Workflows, and GitHub Repos for AI — The Complete List.md
@@ -0,0 +1,456 @@
+---
+title: "Top 60 Claude Skills, Workflows, and GitHub Repos for AI — The Complete List."
+source: "https://x.com/eng_khairallah1/status/2037816689665147355"
+author:
+ - "[[Unknown]]"
+published: 2026-03-28
+created: 2026-03-28
+description:
+tags:
+ - "clippings"
+---
+I spent 100+ hours testing AI tools so you do not have to.
+
+Save this :)
+
+The AI tooling landscape in 2026 is overwhelming. New frameworks every week. New agents every day. New repos trending on GitHub every morning.
+
+Most of them are hype. Some of them are genuinely useful. A few of them will fundamentally change how you work.
+
+I filtered the noise. Here are the 60 tools that actually matter right now - organized by category, tested personally, with honest notes on what each one is actually good for.
+
+**Bookmark this. You will come back to it.**
+
+# Part 1: AI Coding Agents & IDEs 🛠️
+
+These are the tools that let AI write, review, and manage code on your behalf. The ones that actually work in real workflows, not just demos.
+
+**01\. Claude Code:**
+
+Anthropic's command line coding agent. Reads files, writes code, runs tests, operates directly in your local environment. The gold standard for AI-assisted development when you want full control.
+
+🔗 [https://docs.anthropic.com/en/docs/claude-code](https://docs.anthropic.com/en/docs/claude-code)
+
+**02\. Cursor:**
+
+AI-first code editor built on VS Code. Inline completions, chat with your codebase, multi-file editing. The best editor for developers who want AI integrated into their existing workflow.
+
+🔗 [https://www.cursor.com](https://www.cursor.com/)
+
+**03\. Codex CLI:**
+
+OpenAI's terminal coding agent. Takes natural language instructions, reads your codebase, writes and executes code. Strong at multi-step implementation tasks.
+
+🔗 [https://github.com/openai/codex](https://github.com/openai/codex)
+
+**04\. Windsurf:**
+
+AI coding IDE by Codeium. Cascade agent for multi-file editing, deep codebase understanding, and flow-state coding. Growing fast.
+
+🔗 [https://codeium.com/windsurf](https://codeium.com/windsurf)
+
+**05\. Superpowers:**
+
+20+ battle-tested Claude Code skills. TDD, debugging, plan-to-execute pipelines. 96,000+ stars on GitHub. If you use Claude Code, install this first.
+
+🔗 [https://github.com/obra/superpowers](https://github.com/obra/superpowers)
+
+**06\. Spec Kit (GitHub):**
+
+Spec-driven development. Write specifications, AI generates code from them. Forces you to think before you build. 50,000+ stars.
+
+🔗 [https://github.com/github/spec-kit](https://github.com/github/spec-kit)
+
+**07\. Aider:**
+
+AI pair programming in your terminal. Works with any LLM. Strong at working with existing codebases. 30,000+ stars.
+
+🔗 [https://github.com/paul-gauthier/aider](https://github.com/paul-gauthier/aider)
+
+# Part 2: Agent Frameworks 🤖
+
+Build autonomous systems that think, act, and iterate.
+
+**08\. OpenClaw:**
+
+The viral open-source AI agent. Persistent, multi-channel (WhatsApp, Telegram, Discord), writes its own skills. 210,000+ stars and growing fast. The most accessible entry point for personal AI agents.
+
+🔗 [https://github.com/openclaw/openclaw](https://github.com/openclaw/openclaw)
+
+**09\. LangGraph:**
+
+Multi-agent orchestration as code. Build agents as graphs with branching logic, human-in-the-loop, and persistent state. 26,000+ stars.
+
+🔗 [https://github.com/langchain-ai/langgraph](https://github.com/langchain-ai/langgraph)
+
+**10\. CrewAI:**
+
+Multi-agent framework with roles, goals, and backstories. Each agent has a defined persona and responsibility. Good for team-like workflows.
+
+🔗 [https://github.com/crewAIInc/crewAI](https://github.com/crewAIInc/crewAI)
+
+**11\. AutoGPT:**
+
+Full autonomous agent platform for long-running tasks. The OG agent framework. Matured significantly since early days.
+
+🔗 [https://github.com/Significant-Gravitas/AutoGPT](https://github.com/Significant-Gravitas/AutoGPT)
+
+**12\. Dify:**
+
+Open-source LLM app builder. Combines workflows, RAG, agents, and model management in one platform. Good for non-developers building AI apps.
+
+🔗 [https://github.com/langgenius/dify](https://github.com/langgenius/dify)
+
+**13\. OWL:**
+
+Multi-agent cooperation framework. Tops the GAIA benchmark for agent coordination. Cutting edge research turned into usable code.
+
+🔗 [https://github.com/camel-ai/owl](https://github.com/camel-ai/owl)
+
+**14\. CopilotKit:**
+
+Embed AI copilots directly into React applications. Ship AI features in your product, not just your workflow.
+
+🔗 [https://github.com/CopilotKit/CopilotKit](https://github.com/CopilotKit/CopilotKit)
+
+**15\. pydantic-ai:**
+
+Type-safe agent framework built on Pydantic. For Python developers who want structured, validated agent outputs.
+
+🔗 [https://github.com/pydantic/pydantic-ai](https://github.com/pydantic/pydantic-ai)
+
+# Part 3: MCP Servers & Tool Integration 🔗
+
+MCP (Model Context Protocol) gives AI access to the outside world. Skills teach it HOW. MCP gives it ACCESS.
+
+**16\. Tavily:**
+
+Search engine built for AI agents. Not blue links - clean, structured, LLM-ready data. Four tools: search, extract, crawl, map. Connects as remote MCP in one minute.
+
+🔗 [https://github.com/tavily-ai/tavily-mcp](https://github.com/tavily-ai/tavily-mcp)
+
+**17\. Context7:**
+
+Injects up-to-date library documentation into your LLM's context. No more hallucinated APIs or deprecated methods. Add "use context7" to your prompt and it pulls current docs. Supports thousands of libraries.
+
+🔗 [https://github.com/upstash/context7](https://github.com/upstash/context7)
+
+**18\. Task Master AI:**
+
+Your AI's project manager. Feed it a PRD and it generates structured tasks with dependencies. Claude executes them one by one. Turns chaotic sessions into organized pipelines.
+
+🔗 [https://github.com/eyaltoledano/claude-task-master](https://github.com/eyaltoledano/claude-task-master)
+
+**19\. MCP Playwright:**
+
+Browser automation for LLMs. Control a real browser through natural language. Testing, scraping, interaction.
+
+🔗 [https://github.com/executeautomation/mcp-playwright](https://github.com/executeautomation/mcp-playwright)
+
+**20\. fastmcp:**
+
+Build MCP servers in minimal Python. The fastest way to create custom tool integrations for Claude or any MCP-compatible model.
+
+🔗 [https://github.com/jlowin/fastmcp](https://github.com/jlowin/fastmcp)
+
+**21\. markdownify-mcp:**
+
+Convert PDFs, images, and audio into Markdown. Feed any document type into your AI workflow.
+
+🔗 [https://github.com/zcaceres/markdownify-mcp](https://github.com/zcaceres/markdownify-mcp)
+
+**22\. MCPHub:**
+
+Manage multiple MCP servers via HTTP. One dashboard for all your tool connections.
+
+🔗 [https://github.com/samanhappy/mcphub](https://github.com/samanhappy/mcphub)
+
+# Part 4: Claude Skills (Top Picks) 🧠
+
+Skills teach Claude specialized workflows. There are 80,000+ community skills. These are the ones worth installing.
+
+**23\. PDF Processing (Official):**
+
+Read, extract tables, fill forms, merge and split PDFs. The highest-utility skill for knowledge workers.
+
+🔗 [https://github.com/anthropics/skills/tree/main/skills/pdf](https://github.com/anthropics/skills/tree/main/skills/pdf)
+
+**24\. Frontend Design (Official):** Build real design systems, bold typography, production-grade UI. Escape the "AI slop" aesthetic. 277,000+ installs.
+
+🔗 [https://github.com/anthropics/skills/tree/main/skills/frontend-design](https://github.com/anthropics/skills/tree/main/skills/frontend-design)
+
+**25\. Skill Creator (Official):**
+
+The meta-skill. Describe a workflow in plain English and get a complete [SKILL.md](http://skill.md/) back in five minutes. Build new skills without writing any configuration.
+
+🔗 [https://github.com/anthropics/skills/tree/main/skills/skill-creator](https://github.com/anthropics/skills/tree/main/skills/skill-creator)
+
+**26\. Marketing Skills** by Corey Haines:
+
+20+ skills covering CRO, copywriting, SEO, email sequences, growth strategy. Everything a marketing team needs in skill form.
+
+🔗 [https://github.com/coreyhaines31/marketingskills](https://github.com/coreyhaines31/marketingskills)
+
+**27\. Claude SEO:**
+
+Full-site audits, schema validation, keyword analysis. 12 sub-skills covering the complete SEO workflow.
+
+🔗 [https://github.com/AgriciDaniel/claude-seo](https://github.com/AgriciDaniel/claude-seo)
+
+**28\. Obsidian Skills:**
+
+Built by Obsidian's CEO. Auto-tagging, auto-linking, vault-native operations. If you use Obsidian, this is essential.
+
+🔗 [https://github.com/kepano/obsidian-skills](https://github.com/kepano/obsidian-skills)
+
+**29\. Context Optimization:**
+
+Reduce token costs and improve KV-cache efficiency. Makes expensive API workflows significantly cheaper. 13,900+ stars.
+
+🔗 [https://github.com/muratcankoylan/agent-skills-for-context-engineering](https://github.com/muratcankoylan/agent-skills-for-context-engineering)
+
+**30\. Deep Research Skill:**
+
+8-phase research with auto-continuation. For when you need Claude to go deep on a topic, not just skim the surface.
+
+🔗 [https://github.com/199-biotechnologies/claude-deep-research-skill](https://github.com/199-biotechnologies/claude-deep-research-skill)
+
+# Part 5: Local AI & Model Running 🖥️
+
+Run models on your own hardware. Privacy, speed, zero API costs.
+
+**31\. Ollama:**
+
+Run open-source LLMs locally with one terminal command. Supports Llama, Mistral, Gemma, and dozens more. The fastest path from zero to local AI.
+
+🔗 [https://github.com/ollama/ollama](https://github.com/ollama/ollama)
+
+**32\. Open WebUI:**
+
+Self-hosted ChatGPT-like interface. Clean, fast, full-featured. Pairs perfectly with Ollama for a private AI setup.
+
+🔗 [https://github.com/open-webui/open-webui](https://github.com/open-webui/open-webui)
+
+**33\. LlamaFile:**
+
+Package an entire LLM as a single executable file. Zero dependencies. Download and run. Absurdly simple.
+
+🔗 [https://github.com/Mozilla-Ocho/llamafile](https://github.com/Mozilla-Ocho/llamafile)
+
+**34\. Unsloth:**
+
+Fine-tune models 2x faster with 70% less memory. If you need a custom model trained on your data, start here.
+
+🔗 [https://github.com/unslothai/unsloth](https://github.com/unslothai/unsloth)
+
+**35\. vLLM:**
+
+High-throughput inference engine. 2 to 4x faster than naive serving. The standard for production deployment of open-source models.
+
+🔗 [https://github.com/vllm-project/vllm](https://github.com/vllm-project/vllm)
+
+# Part 6: Workflow & Automation ⚡
+
+Connect AI to your existing tools and processes.
+
+**36\. n8n:**
+
+Open-source workflow automation with 400+ integrations and AI nodes. Self-hostable. The best visual builder for AI-powered automations.
+
+🔗 [https://github.com/n8n-io/n8n](https://github.com/n8n-io/n8n)
+
+**37\. Langflow:**
+
+Visual drag-and-drop for agent pipelines. 140,000+ stars. Build complex agent workflows without writing code.
+
+🔗 [https://github.com/langflow-ai/langflow](https://github.com/langflow-ai/langflow)
+
+**38\. Huginn:**
+
+Self-hosted web agents for monitoring, alerts, and data collection. Privacy-first automation that runs on your server.
+
+🔗 [https://github.com/huginn/huginn](https://github.com/huginn/huginn)
+
+**39\. DSPy:**
+
+Program (not prompt) foundation models. Stanford research turned framework. For when prompting is not deterministic enough.
+
+🔗 [https://github.com/stanfordnlp/dspy](https://github.com/stanfordnlp/dspy)
+
+**40\. Temporal:**
+
+Durable workflow engine for long-running processes. When your automation needs to survive crashes, retries, and timeouts.
+
+🔗 [https://github.com/temporalio/temporal](https://github.com/temporalio/temporal)
+
+# Part 7: Search, Data & RAG 🔍
+
+Get information into and out of AI systems.
+
+**41\. GPT Researcher:**
+
+Autonomous research agent that produces compiled reports. Give it a topic, get back a thorough analysis with sources.
+
+🔗 [https://github.com/assafelovic/gpt-researcher](https://github.com/assafelovic/gpt-researcher)
+
+**42\. Firecrawl:**
+
+Turn any website into LLM-ready data. Web scraping designed specifically for AI pipelines.
+
+🔗 [https://github.com/mendableai/firecrawl](https://github.com/mendableai/firecrawl)
+
+**43\. Vanna AI:**
+
+Natural language to SQL. Ask questions in English, get database queries back. For anyone who needs data from databases without writing SQL.
+
+🔗 [https://github.com/vanna-ai/vanna](https://github.com/vanna-ai/vanna)
+
+**44\. Instructor:**
+
+Get structured JSON outputs from any LLM using Pydantic models. Works with OpenAI, Anthropic, Google, and 15+ providers. What production AI engineers actually use.
+
+🔗 [https://python.useinstructor.com](https://python.useinstructor.com/)
+
+**45\. Chroma:**
+
+Open-source vector database. The simplest way to add semantic search and long-term memory to your AI applications.
+
+🔗 [https://github.com/chroma-core/chroma](https://github.com/chroma-core/chroma)
+
+**46\. dlt:**
+
+LLM-native data pipelines from 5,000+ sources. Get data from anywhere into your AI workflow.
+
+🔗 [https://github.com/dlt-hub/dlt](https://github.com/dlt-hub/dlt)
+
+**47\. ExtractThinker:**
+
+ORM for document intelligence. Extract structured data from any document type.
+
+🔗 [https://github.com/enoch3712/ExtractThinker](https://github.com/enoch3712/ExtractThinker)
+
+# Part 8: API & Infrastructure 🏗️
+
+The plumbing that makes everything work in production.
+
+**48\. FastAPI:**
+
+The Python web framework for serving AI applications. Exceptional documentation. Pydantic validation built in.
+
+🔗 [https://github.com/tiangolo/fastapi](https://github.com/tiangolo/fastapi)
+
+**49\. Portkey Gateway:**
+
+Route requests to 250+ LLMs through one API. Switch models without changing code.
+
+🔗 [https://github.com/Portkey-AI/gateway](https://github.com/Portkey-AI/gateway)
+
+**50\. OmniRoute:**
+
+API proxy for 44+ AI providers. Load balancing, fallbacks, and cost optimization.
+
+🔗 [https://github.com/diegosouzapw/OmniRoute](https://github.com/diegosouzapw/OmniRoute)
+
+**51\. lmnr:**
+
+Trace and evaluate agent behavior. See exactly what your agents are doing and measure whether they are doing it well.
+
+🔗 [https://github.com/lmnr-ai/lmnr](https://github.com/lmnr-ai/lmnr)
+
+**52\. Codebase Memory MCP:**
+
+Convert your codebase into a persistent knowledge graph. Claude remembers your entire project structure across sessions.
+
+🔗 [https://github.com/DeusData/codebase-memory-mcp](https://github.com/DeusData/codebase-memory-mcp)
+
+# Part 9: Curated Collections & Learning 📚
+
+Where to find more and keep learning.
+
+**53\. Awesome Claude Skills:**
+
+The best curated skill list. 22,000+ stars. Start here when looking for new skills to install.
+
+🔗 [https://github.com/travisvn/awesome-claude-skills](https://github.com/travisvn/awesome-claude-skills)
+
+**54\. Anthropic Skills Repo:**
+
+Official reference implementations from Anthropic. The gold standard for how skills should be built.
+
+🔗 [https://github.com/anthropics/skills](https://github.com/anthropics/skills)
+
+**55\. Awesome Agents:**
+
+100+ open-source agent tools in one curated list.
+
+🔗 [https://github.com/kyrolabs/awesome-agents](https://github.com/kyrolabs/awesome-agents)
+
+**56\. PromptingGuide:**
+
+Comprehensive prompt engineering reference covering every technique from basics to advanced agent prompting.
+
+🔗 [https://www.promptingguide.ai](https://www.promptingguide.ai/)
+
+**57\. Anthropic Prompt Engineering Tutorial:**
+
+9 chapters of hands-on exercises with Jupyter notebooks. The best structured way to learn prompting.
+
+🔗 [https://github.com/anthropics/prompt-eng-interactive-tutorial](https://github.com/anthropics/prompt-eng-interactive-tutorial)
+
+**58\. SkillsMP:**
+
+Marketplace with 80,000+ community skills. The largest catalog for discovering Claude skills.
+
+🔗 [https://skillsmp.com](https://skillsmp.com/)
+
+**59\. MAGI//ARCHIVE:**
+
+Daily feed of fresh AI repos. Stay on top of what is shipping.
+
+🔗 [https://tom-doerr.github.io/repo\_posts/](https://tom-doerr.github.io/repo_posts/)
+
+**60\. Anthropic Official Docs:**
+
+Covers the API, prompting best practices, tool use, agents, and everything else. Read this cover to cover before building anything serious.
+
+🔗 [https://docs.anthropic.com](https://docs.anthropic.com/)
+
+# How to Actually Use This List
+
+Do not try to install all 60 tools at once. That is a recipe for overwhelm and wasted time.
+
+Here is the order I recommend:
+
+**If you are a developer:**
+
+Start with Claude Code (01) + Superpowers (05) + Context7 (17) + Tavily (16). This gives you a powerful AI coding setup with search and documentation access.
+
+**If you are a creator or knowledge worker:**
+
+Start with OpenClaw (08) + Obsidian Skills (28) + PDF Processing (23) + Frontend Design (24). This gives you an AI assistant with file management, document processing, and content creation capabilities.
+
+**If you are building a product:**
+
+Start with FastAPI (48) + Instructor (44) + Chroma (45) + LangGraph (09). This gives you the backend framework, structured outputs, memory, and agent orchestration for a production AI application.
+
+**If you want to learn:**
+
+Start with the Anthropic Tutorial (57) + PromptingGuide (56) + Anthropic Docs (60). Build the foundation before you stack tools.
+
+Pick one path. Go deep. Add more tools as your needs grow.
+
+# TL;DR
+
+**Skills** = teach AI HOW to do things better. **MCP** = give AI ACCESS to external tools and data. **Repos** = the open-source engines powering it all.
+
+Combine all three and you have an AI workflow that is genuinely powerful, not just impressive in demos.
+
+# That is it. 60 tools. Now go build something.
+
+This list took me a long time to compile - testing tools, reading docs, filtering out the hype from the useful. If it saved you time, you know what to do.
+
+I post stuff like this regularly - AI tools, workflows, techniques, and things I actually use. No fluff, no hype, just what works.
+
+**Follow** [@eng\_khairallah1](https://x.com/@eng_khairallah1) **so you do not miss the next one.**
+
+**hope this was useful for you, Khairallah** **❤️**
\ No newline at end of file
diff --git a/Claude/Ultimate Beginners Guide to Claude Managed Agents.md b/Claude/Ultimate Beginners Guide to Claude Managed Agents.md
new file mode 100644
index 0000000..15f31a8
--- /dev/null
+++ b/Claude/Ultimate Beginners Guide to Claude Managed Agents.md
@@ -0,0 +1,178 @@
+---
+title: "Ultimate Beginners Guide to Claude Managed Agents"
+source: "https://x.com/coreyganim/status/2042286607449874527"
+author:
+ - "[[@coreyganim]]"
+published: 2026-04-10
+created: 2026-04-26
+description: "Most people saw Anthropic announce Claude Managed Agents yesterday and immediately tuned out. Too technical. Not for me.Wrong.This is the si..."
+tags:
+ - "clippings"
+---
+
+
+Most people saw Anthropic announce Claude Managed Agents yesterday and immediately tuned out. Too technical. Not for me.
+
+Wrong.
+
+This is the single biggest unlock for anyone who wants to sell AI services to businesses (or agentify your existing business).
+
+And you don't need to be a developer to understand why.
+
+Here's what it actually means, why it matters for your business, and how to get started even if you've never written a line of code.
+
+**PS: I made a Google Doc that walks Claude Code through deploying your first managed agent step by step.**
+
+**Hand it to Claude Code. It does the rest.**
+
+**Grab it here:** [return-my-time.kit.com/2872b904f5](https://return-my-time.kit.com/2872b904f5)
+
+————————————————————————————————————————
+
+# What Claude Managed Agents Actually Is (In Plain English)
+
+Think of it like this: until yesterday, building a custom AI agent that could actually do real work for a client (read their emails, update their CRM, generate reports, send follow-ups) required you to set up servers, manage security, handle errors, and deal with infrastructure.
+
+That's months of engineering work before your agent does anything useful.
+
+Managed Agents is Anthropic saying: "We'll handle all of that. You just tell the agent what to do."
+
+You define the job. They run it on their cloud. The agent gets a secure workspace where it can write code, search the web, read files, and connect to tools. All without you building any of the plumbing.
+
+# Why Non-Technical People Should Care
+
+This changes the math on starting an AI services business.
+
+**Before Managed Agents:**
+
+\- Need to understand technical infrastructure
+
+\- Need to manage servers and security
+
+\- Extensive setup before you can sell anything
+
+**After Managed Agents:**
+
+\- Anthropic handles the infrastructure
+
+\- Security and sandboxing built in
+
+\- Prototype to live agent in days
+
+\- Cost: API usage only (pay as you go)
+
+The barrier to entry just dropped from "hire a dev team" to "describe what you want the agent to do."
+
+# The 4 Building Blocks (All You Need to Know)
+
+Every managed agent runs on four things:
+
+1\. Agent - The instructions. What model to use, what the agent should do, and what tools it has access to. Think of this as the job description you'd write for a human assistant.
+
+2\. Environment - The workspace. Pre-loaded with the software and tools your agent needs. Like setting up a new employee's laptop before their first day.
+
+3\. Session - A running conversation. The agent remembers everything within a session, can work for hours, and keeps files around between interactions.
+
+4\. Events - Messages back and forth. You send tasks in. The agent streams back results, status updates, and asks for approval when needed.
+
+That's it. Four concepts. Everything else is details.
+
+# What You Can Actually Build With This
+
+Real companies are already shipping on Managed Agents:
+
+**For your own business:**
+
+\- An agent that reads every client email and drafts responses in your voice
+
+\- A research agent that monitors competitors and sends you weekly briefings
+
+\- A content agent that takes your rough notes and produces finished blog posts, social copy, and newsletters
+
+**As a service you sell to clients ($1,500-5,000 setup + $500/month):**
+
+\- Client onboarding agents that collect documents, send reminders, and set up new accounts automatically
+
+\- Report generators that pull data from multiple sources and create weekly summaries for executives
+
+\- Customer support agents that answer common questions, escalate edge cases, and log everything in the CRM
+
+\- Document processors that take raw files, extract the important data, and organize it. Finance teams are already using this.
+
+\- Project management agents that join Asana or Linear and pick up tasks like a team member
+
+The key insight: you don't need to build these yourself. You need to understand the client's problem well enough to describe it to the agent.
+
+# The Permission System (Why Clients Will Trust This)
+
+This is the part most people skip, but it's what makes this sellable to real businesses.
+
+Managed Agents has two modes:
+
+\- Auto-run: The agent handles everything automatically. Good for internal tools where you trust the workflow.
+
+\- Approval required: The agent pauses and asks before taking action. Good for anything client-facing or sensitive.
+
+You can mix them. Let the agent read files and search the web automatically, but require approval before it sends an email or updates a database.
+
+This is how you get risk-averse business owners to say yes. "The agent will draft the email, but it won't send anything without your approval."
+
+# How to Get Started (Two Paths)
+
+**Path 1: You already use Claude Code**
+
+Open Claude Code and type one line:
+
+"start onboarding for managed agents in Claude API"
+
+It walks you through the entire setup. Or grab our free Google Doc guide that does the same thing: [https://return-my-time.kit.com/2872b904f5](https://return-my-time.kit.com/2872b904f5)
+
+**Path 2: You're non-technical and want help**
+
+This is exactly what we cover inside the [Build With AI community](http://www.skool.com/buildwithai/about). We do live office hours where we walk through agent builds step by step, from identifying the right problem to deploying a working agent for a client.
+
+You don't need to understand APIs or write code. You need to understand business problems. We'll handle the rest together.
+
+# What This Costs
+
+Pricing is usage-based:
+
+\- Standard Claude API token rates (same as using Claude normally)
+
+\- $0.08 per session-hour for active runtime
+
+\- Web search: $10 per 1,000 searches (if your agent needs it)
+
+A typical 10-minute agent session costs a few cents. Even heavy usage stays well under $100/month for most use cases.
+
+Compare that to hiring a developer or paying for enterprise software. The math works.
+
+# The Business Opportunity Nobody's Talking About
+
+Here's what most people miss: the businesses that need this most (law firms, accounting practices, real estate agencies, medical offices) will never set this up themselves. They don't want to. They want someone to do it for them.
+
+That someone is you.
+
+**The playbook:**
+
+1\. Run a $999 AI audit to identify their biggest time wasters
+
+2\. Build a managed agent that solves their top problem
+
+3\. Deploy it on Anthropic's infrastructure (they handle security, uptime, everything)
+
+4\. Charge $500/month to maintain and improve it
+
+4 clients = $2,000/month recurring. 10 clients = $5,000/month. And each agent takes less time to maintain than a single meeting.
+
+99.9% of people who read this article will bookmark it and do nothing.
+
+Be the 0.1%. Take action.
+
+**PS: I made a Google Doc that walks Claude Code through deploying your first managed agent step by step.**
+
+**Hand it to Claude Code. It does the rest.**
+
+**Grab it here:**
+
+[return-my-time.kit.com/2872b904f5](https://return-my-time.kit.com/2872b904f5)
\ No newline at end of file
diff --git a/Claude/Using Claude Code While Writing a Research Paper.md b/Claude/Using Claude Code While Writing a Research Paper.md
new file mode 100644
index 0000000..5cfb733
--- /dev/null
+++ b/Claude/Using Claude Code While Writing a Research Paper.md
@@ -0,0 +1,56 @@
+---
+title: "Using Claude Code While Writing a Research Paper"
+source: "https://x.com/liulicheng10/status/2032487597214490969"
+author:
+ - "[[Unknown]]"
+published: 2026-03-14
+created: 2026-03-14
+description: "ai research engineer • designing agent runtimes, memory & retrieval systems for autonomous agents"
+tags:
+ - "clippings"
+---
+We just wrapped up a paper, and Claude Code ended up doing a surprising amount of the grunt work. The big takeaway: it's incredibly good at tasks where the goal is obvious and the workflow is repeatable. But the moment a problem requires you to think about the system as a whole, it hits a wall.
+
+Here's what that looked like in practice.
+
+## Where it crushed it
+
+The tasks Claude Code nailed all had something in common: clear inputs, clear outputs, and mostly execution, not judgment calls.
+
+**Plotting.** I had it generate three figures for the paper. For one of them (reasoning length trends across eight environments) I literally just said "plot the reasoning length trends for me." It picked a 2×4 subplot layout on its own, added variance bands with smoothed curves, and even annotated each panel with the early-to-late-stage percentage change (e.g., "-40%", "-87%"). I never asked for that. Then I said "make it look better," and it applied Savitzky-Golay smoothing, swapped the color scheme, cleaned up the spines and ticks, and adjusted fonts to look publication-ready. We tried the same task with Codex; the main gap was in aesthetic judgment. Claude Code is weirdly good at inferring what you mean from vague instructions.
+
+
+
+Previous version
+
+
+
+Edited by CC
+
+**Code migration.** We had a multi-turn search environment that needed porting from one codebase to our codebase. Completely different architectures: ToolEnvironment on one side, BaseLanguageBasedEnv + gym.Env dual inheritance on the other, plus 128 concurrent instances hitting the retrieval server during training. Doing it by hand would've been a solid half-day to full-day job. Claude Code knocked it out in under an hour. It had everything it needed: clear goal, source code on both ends, and the work was mostly "understand the architecture, then adapt."
+
+**Formatting math proofs.** This one surprised me, since Claude Code is fundamentally a coding tool. Our appendix has a bunch of mathematical proofs, and the workflow was: write a rough draft, then hand it off for formatting. But it went beyond formatting. In a gradient decomposition derivation for the SNR mechanism, my draft used the Cauchy-Schwarz inequality to bound a quantity but left the bound conditions incomplete. Claude Code caught this and filled in the definition and constraints for the constant C. It was also solid at writing section overviews, usually tighter and more accurate than what I'd write manually. The entire appendix (12+ pages of LaTeX) could be reviewed in one pass with edits throughout.
+
+## The compounding effect
+
+
+
+Plotting is the clearest example. The first figure took some back-and-forth to nail the interaction pattern: feed it a csv, give a vague request, let it pick the layout and colors, then iterate on details. Figures two and three followed the same flow and came together fast. Same with proof formatting: the first section took time to calibrate, but the rest were basically free.
+
+This is where Claude Code really pays off. The prompting patterns you develop carry over directly. You're not starting from zero each time.
+
+## Where it can't help
+
+A bug after the search environment went live is a good example of the boundary.
+
+
+
+Some context: the search environment was migrated from a large open-source RL framework, which is a pretty large and established codebase. So when things broke, the setup itself was the last thing we'd suspect. On top of that, everything had been running fine on our Slurm cluster. After migrating to [Vast.ai](https://vast.ai/), training started hitting constant timeouts and random crashes. Logs showed the Flask server slowing down, timeouts cascading, but the root cause was nowhere in sight. I asked Claude Code to take a look; it went through the code, configs, and logs, and came up empty. The code hadn't changed at all. What used to work just… didn't.
+
+Turns out the [Vast.ai](https://vast.ai/) machine simply didn't have enough CPU cores for the concurrency load. On Slurm this was never an issue because Slurm silently allocates generous CPU resources when you submit a job, totally invisible to the user.
+
+The hard part about this kind of bug is that you know something's wrong, but the answer isn't in the code or the logs. You have to go check the machine's resource allocation yourself. That's not something a code tool can do for you.
+
+## TL;DR
+
+Plotting, code migration, proof formatting: all went smoothly because the goals were clear and the workflows were reusable. The debugging didn't, because the problem lived outside the codebase. If you're doing research: find the task types where it fits, build repeatable workflows around them, and don't expect it to debug your infra.
\ No newline at end of file
diff --git a/Claude/You Don’t Know Claude Code Architecture, Governance, and Engineering Practices.md b/Claude/You Don’t Know Claude Code Architecture, Governance, and Engineering Practices.md
new file mode 100644
index 0000000..6970e8f
--- /dev/null
+++ b/Claude/You Don’t Know Claude Code Architecture, Governance, and Engineering Practices.md
@@ -0,0 +1,745 @@
+---
+title: "You Don’t Know Claude Code: Architecture, Governance, and Engineering Practices"
+source: "https://x.com/HiTw93/status/2033181380432339045"
+author:
+ - "[[Unknown]]"
+published: 2026-03-12
+created: 2026-03-20
+description:
+tags:
+ - "clippings"
+---
+This handbook is written for engineers who already use Claude Code and want workflows that are more predictable, more controllable, and easier to verify.
+
+Unless noted otherwise, the guidance here is based on my own usage, experiments, and public Anthropic materials available as of March 2026. Treat it as engineering guidance, not as an official specification of Claude Code internals.
+
+This is an English translation of a Chinese article published in March 2026. The original received 6,000+ likes from the developer community across Asia.
+
+> 3月12日
+
+# 0\. TL;DR
+
+This handbook is based on six months of heavy Claude Code use and roughly $40 per month spent across two accounts.
+
+At first, I treated it like a chatbot. Before long, I hit the same walls many people hit: context getting messy, tools multiplying while outcomes degrade, and rule sets growing longer while compliance drops. After working through those failures and studying how Claude Code operates, I realized this is not mainly a prompt-engineering problem. It is a systems-design problem.
+
+I want to walk through how Claude Code works in practice, why context drifts and how to manage it, how to design Skills and Hooks, when to use Subagents, the architectural impact of prompt caching, and how to write a CLAUDE.md that remains useful over time.
+
+Here's how I think about Claude Code: six layers.
+
+
+
+Over-index on one layer and the system becomes unstable. CLAUDE.md grows too long and pollutes its own context. Too many tools make selection noisy. Too many subagents make state drift harder to control. Skip verification and you lose the ability to tell where things broke.
+
+# 1\. Execution Model
+
+
+
+Claude Code is not just a chat interface. The core execution model is an iterative agent loop:
+
+```plaintext
+Gather context → Take action → Verify result → [Done or loop back]
+ ↑ ↓
+ CLAUDE.md Hooks / Permissions / Sandbox
+ Skills Tools / MCP
+ Memory
+```
+
+After using it for a while, you realize that failures usually do not come from a lack of raw model capability. Wrong information is more dangerous than missing information. In many cases, the real problem is that something was produced, but you have no reliable way to validate it or roll it back.
+
+## The Five Surfaces That Actually Matter
+
+
+
+When something goes wrong, check these surfaces first. If results are unstable, inspect context loading order before blaming the model. If automation runs too far, inspect the control layer before blaming the agent for being "too active." If quality drops in long sessions, assume intermediate artifacts have polluted the context and consider starting a fresh session instead of endlessly tuning prompts.
+
+# 2\. Concept Boundaries: MCP / Plugin / Tools / Skills / Hooks / Subagents
+
+
+
+Simple rule: use Tools and MCP to give Claude new actions, use Skills to provide reusable methods, use Subagents to isolate execution, use Hooks to enforce constraints and collect audit signals, and use Plugins for distribution. Blur these boundaries and the system becomes difficult to reason about.
+
+# 3\. Context Engineering: The Most Important System Constraint
+
+Many people treat context as a "capacity problem," but the real issue is usually noise, useful information drowned in irrelevant content.
+
+## The Real Context Cost Structure
+
+
+
+Claude Code's large context window is not fully available for task-specific work:
+
+```text
+200K total context
+├── Fixed overhead (~15-20K)
+│ ├── System instructions: ~2K
+│ ├── All enabled Skill descriptors: ~1-5K
+│ ├── MCP Server tool definitions: ~10-20K ← Largest hidden overhead
+│ └── LSP state: ~2-5K
+│
+├── Semi-fixed (~5-10K)
+│ ├── CLAUDE.md: ~2-5K
+│ └── Memory: ~1-2K
+│
+└── Dynamically available (~160-180K)
+ ├── Conversation history
+ ├── File contents
+ └── Tool call results
+```
+
+A typical MCP server such as GitHub can expose 20-30 tool definitions at roughly 200 tokens each, which is **4,000-6,000 tokens**. Connect five servers and you may be spending roughly **25,000 tokens (12.5%)** on fixed overhead alone. In large codebases, that overhead is material.
+
+## Recommended Context Layering
+
+```text
+Always resident → CLAUDE.md: project contract / build commands / prohibitions
+Path-loaded → rules: language / directory / file-type specific
+On-demand loaded → skills: workflows / domain knowledge
+Isolated loaded → subagents: heavy exploration / parallel research
+Never in context → hooks: deterministic scripts / audit / blocking
+```
+
+## Context Best Practices
+
+
+
+- Keep CLAUDE.md short, strict, and operational. Prioritize commands, constraints, and architectural boundaries. In Anthropic's public examples, these files stay relatively compact; that is the right direction even if there is no universal token target.
+- Split large reference documents into supporting files for the skill; do not put them directly in SKILL.md
+- Use .claude/rules/ for path- and language-specific rules; do not make root CLAUDE.md handle every variation
+- Use /context proactively to inspect consumption; do not wait for automatic compression
+- For task switches, prefer /clear; for a new phase of the same task, use /compact
+- **Write Compact Instructions in CLAUDE.md**. You should decide what survives compression, not the algorithm.
+
+## Tool Output Noise: The Other Hidden Context Killer
+
+The analysis above covers fixed overhead from MCP tool definitions. But dynamic context has its own trap: tool output. A single cargo test run can produce thousands of lines, git log fills up fast on any non-trivial repo, and find or grep can easily flood the window with irrelevant matches. Claude does not need all of it, but once it's in context it consumes real tokens and crowds out conversation history and file contents.
+
+I came across [RTK (Rust Token Killer)](https://www.rtk-ai.app/) and the idea clicked immediately: filter command output before it reaches Claude, keeping only what's relevant to the next decision. For cargo test, instead of thousands of lines:
+
+```text
+# Raw output Claude would see
+running 262 tests
+test auth::test_login ... ok
+...（thousands of lines）
+
+# After RTK
+✓ cargo test: 262 passed (1 suite, 0.08s)
+```
+
+What Claude actually needs is "did it pass, and if not, where did it fail." Everything else is noise. RTK rewrites commands transparently via a hook, so Claude Code never notices it.
+
+Section 6 covers | head -30 as a quick manual truncation trick. RTK does the same thing more broadly without having to add it to every command individually. The project is [open source on GitHub](https://github.com/rtk-ai/rtk).
+
+## The Compression Trap
+
+
+
+The default compression algorithm optimizes for "re-readability." Early tool outputs and file contents are often deleted first, which can also remove **architectural decisions and constraint rationale**. Two hours later, when you need to change something, the earlier decisions may already be gone. A surprising number of bugs start there.
+
+The fix is to specify in CLAUDE.md:
+
+```markdown
+## Compact Instructions
+
+When compressing, preserve in priority order:
+
+1. Architecture decisions (NEVER summarize)
+2. Modified files and their key changes
+3. Current verification status (pass/fail)
+4. Open TODOs and rollback notes
+5. Tool outputs (can delete, keep pass/fail only)
+```
+
+Compact Instructions help, but a HANDOFF.md is even more reliable. Before ending a session, have Claude record the current state: what it tried, what worked, what failed, and what should happen next. Then the next Claude instance can continue from that file instead of depending on compression quality:
+
+> Write the current progress in HANDOFF.md. Explain what you tried, what worked, what didn't, so the next agent with fresh context can continue from just this file.
+
+Review it quickly for gaps, then start the next session from the HANDOFF.md path.
+
+## Plan Mode's Engineering Value
+
+
+
+Plan Mode separates exploration from execution. Exploration stays read-only, and files are touched only after the plan is confirmed:
+
+- The exploration phase is read-only
+- Claude can clarify goals and boundaries before proposing a concrete plan
+- Execution begins only after the plan is confirmed
+
+
+
+For complex refactors, migrations, and cross-module changes, this separation is usually better than editing immediately. Running with a bad assumption is much less likely when the plan is confirmed first. Double-tap Shift+Tab to enter Plan Mode. A useful pattern is to let one agent draft the plan and another review it before execution.
+
+# 4\. Skills Design: Not a Template Library, But Workflows Loaded On Demand
+
+Skills are best understood as on-demand workflow packages. Their descriptors remain in context, while the full body is loaded only when needed. That makes them operationally different from saved prompts.
+
+## What Makes a Good Skill
+
+- A good skill description tells the model when to use the skill, not just what it contains
+- It defines complete steps, inputs, outputs, and stop conditions, not just an opening instruction
+- The body should contain navigation and core constraints only; large reference material should live in supporting files
+- Skills with side effects should explicitly disable model invocation; otherwise the model decides whether to run them
+
+## How Skills Achieve "On-Demand Loading"
+
+A useful pattern here is progressive disclosure. Do not show the model everything at once. Give it an index and navigation first, then load details on demand:
+
+- SKILL.md defines task semantics, boundaries, and execution skeleton
+- Supporting files provide domain details
+- Scripts deterministically gather context or evidence
+
+A stable structure looks like:
+
+```plaintext
+.claude/skills/
+└── incident-triage/
+ ├── SKILL.md
+ ├── runbook.md
+ ├── examples.md
+ └── scripts/
+ └── collect-context.sh
+```
+
+## Three Typical Skill Types
+
+These examples come from my own use in the open-source terminal project [Kaku](https://github.com/tw93/Kaku).
+
+**Type 1: Checklist (Quality Gate)**
+
+Run this before a release so nothing critical is missed:
+
+```yaml
+---
+name: release-check
+description: Use before cutting a release to verify build, version, and smoke test.
+---
+
+## Pre-flight (All must pass)
+- [ ] \`cargo build --release\` passes
+- [ ] \`cargo clippy -- -D warnings\` clean
+- [ ] Version bumped in Cargo.toml
+- [ ] CHANGELOG updated
+- [ ] \`kaku doctor\` passes on clean env
+
+## Output
+Pass / Fail per item. Any Fail must be fixed before release.
+```
+
+**Type 2: Workflow (Standardized Operations)**
+
+Config migration is high risk, so use explicit invocation and a built-in rollback path:
+
+```yaml
+name: config-migration
+description: Migrate config schema. Run only when explicitly requested.
+disable-model-invocation: true
+---
+
+## Steps
+1. Backup: \`cp ~/.config/kaku/config.toml ~/.config/kaku/config.toml.bak\`
+2. Dry run: \`kaku config migrate --dry-run\`
+3. Apply: remove \`--dry-run\` after confirming output
+4. Verify: \`kaku doctor\` all pass
+
+## Rollback
+\`cp ~/.config/kaku/config.toml.bak ~/.config/kaku/config.toml\`
+```
+
+**Type 3: Domain Expert (Encapsulated Decision Framework)**
+
+When runtime issues occur, have Claude collect evidence along a fixed path instead of guessing:
+
+```yaml
+---
+name: runtime-diagnosis
+description: Use when kaku crashes, hangs, or behaves unexpectedly at runtime.
+---
+
+## Evidence Collection
+1. Run \`kaku doctor\` and capture full output
+2. Last 50 lines of \`~/.local/share/kaku/logs/\`
+3. Plugin state: \`kaku --list-plugins\`
+
+## Decision Matrix
+| Symptom | First Check |
+|---|---|
+| Crash on startup | doctor output → Lua syntax error |
+| Rendering glitch | GPU backend / terminal capability |
+| Config not applied | Config path + schema version |
+
+## Output Format
+Root cause / Blast radius / Fix steps / Verification command
+```
+
+## Keep Descriptors Short, Every Skill Has Context Cost
+
+Each enabled skill keeps its descriptor in context. The gap between optimized and unoptimized is huge:
+
+```yaml
+# Inefficient (~45 tokens)
+description: |
+ This skill helps you review code changes in Rust projects.
+ It checks for common issues like unsafe code, error handling...
+ Use this when you want to ensure code quality before merging.
+
+# Efficient (~9 tokens)
+description: Use for PR reviews with focus on correctness.
+```
+
+Important invocation strategy:
+
+- High frequency (>1 per session) → Keep auto-invoke and optimize the descriptor
+- Low frequency (<1 per session) → Disable auto-invoke, trigger manually, keep the descriptor out of context
+- Very low frequency (<1 per month) → Remove the skill and document it in AGENTS.md instead
+
+## Skills Anti-Patterns
+
+- Description too short: description: help with backend (it triggers for almost any backend task)
+- Body too long: hundreds of lines of manual content stuffed into SKILL.md
+- One skill covering review, deploy, debug, docs, incident, five different things
+- Side-effect skills allowing model auto-invocation
+
+# 5\. Tool Design: Helping Claude Pick the Right Tool
+
+The more I used Claude Code, the clearer this became: tools for Claude and APIs for humans should be designed differently. Human-facing APIs often optimize for feature completeness. Agent-facing tools should optimize for correct selection and correct use.
+
+## Good Tools vs. Bad Tools
+
+
+
+Practical design principles:
+
+- Prefix names by system or resource layer: github\_pr\_\*, jira\_issue\_\*
+- Support response\_format: concise / detailed for large responses
+- Make error responses corrective; do not return opaque codes only
+- When high-level task tools can be merged, do not expose too many low-level fragments. Avoid patterns like list\_all\_\* that force the model to filter the results itself.
+
+## Lessons from Claude Code's Internal Tool Evolution
+
+
+
+One useful lesson from Anthropic's public discussion of Claude Code tooling is how they handled cases where the agent needed to stop mid-task and ask the user a question. They tried three approaches:
+
+- **Version 1**: Add a question parameter to existing tools such as Bash, letting Claude ask while calling the tool. Result: Claude often ignored the parameter and continued without pausing.
+- **Version 2**: Require Claude to emit a specific markdown format that an outer layer parses to pause. Problem: there is no hard enforcement, so the questioning path remains fragile.
+- **Version 3**: Make it a standalone AskUserQuestion tool. Claude must explicitly call it to ask, and the tool call itself becomes the pause signal. Much more reliable than either previous approach.
+
+This image shows why Version 3 is more stable:
+
+
+
+The left approach is too loose because parsing depends on free-form output. The right approach is too rigid because the model can ask only after exiting plan mode. AskUserQuestion as a dedicated tool sits in the middle: structured, explicit, and callable at any point.
+
+The practical takeaway is straightforward: if you want Claude to stop and ask, give it a dedicated tool. Flags and output-format conventions are much easier for the model to skip.
+
+## Todo Tool Evolution:
+
+
+
+Early versions used a TodoWrite tool plus periodic reminders to keep Claude on task. As models improved, that tool became more of a constraint than a benefit. The broader lesson is worth keeping: controls that were useful for weaker models may become unnecessary friction later, so they should be reviewed periodically.
+
+**Search Tool Evolution**: Early approaches relied on a RAG-style vector database. It was fast, but it required indexing and proved fragile across environments. More importantly, tool adoption by the model was poor. Switching to a Grep-style tool that let Claude search directly worked better in practice. A useful side effect is that Claude can read a skill file, follow references to other files, and load information recursively only when needed. That is a strong example of progressive disclosure.
+
+When NOT to Add Another Tool
+
+- Tasks the local shell can already perform reliably
+- Cases where the model needs static knowledge, not external interaction
+- Requirements that fit skill workflow constraints better than tool actions
+- Cases where you have not yet validated that the description, schema, and return format are stable for model use
+
+# 6\. Hooks: Running Your Own Code Before/After Claude Acts
+
+Hooks are easy to think of as "automatically running scripts," but in practice they are better understood as a way to move work out of on-the-fly model judgment and into deterministic processes.
+
+Examples include whether formatting should run, whether protected files can be modified, and whether to notify after task completion. These are not the kinds of controls you should rely on the model to remember every time.
+
+## Current Hook Points
+
+
+
+## Suitable vs. Unsuitable for Hooks
+
+**Suitable**: Blocking modifications to protected files, auto formatting/lint/light validation after Edit, injecting dynamic context after SessionStart (Git branch, env vars), pushing notifications after task completion.
+
+**Unsuitable**: Complex semantic judgments requiring lots of context, long-running business processes, decisions requiring multi-step reasoning and tradeoffs, these belong in skills or subagents.
+
+```json
+{
+ "hooks": {
+ "PostToolUse": [
+ {
+ "matcher": "Edit",
+ "pattern": "*.rs",
+ "hooks": [
+ {
+ "type": "command",
+ "command": "cargo check 2>&1 | head -30",
+ "statusMessage": "Running cargo check..."
+ }
+ ]
+ }
+ ],
+ "Notification": [
+ {
+ "type": "command",
+ "command": "osascript -e 'display notification \"Task completed\" with title \"Claude Code\"'"
+ }
+ ]
+ }
+}
+```
+
+## Hooks: Earlier Error Detection Saves Time
+
+
+
+In a 100-edit session, saving 30-60 seconds each time adds up to 1-2 hours. That is material. **Limit output length** (| head -30) to avoid hook output polluting context. For a more systematic approach to output noise across all commands, see the RTK note in section 3.
+
+Hooks + Skills + CLAUDE.md Three-Layer Stack
+
+- CLAUDE.md: Declare "must pass tests and lint before commit"
+- Skill: Tell Claude in what order to run tests, how to read failures, how to fix
+- Hook: Hard validation on critical paths, block if necessary
+
+In practice, any single layer has gaps. CLAUDE.md rules alone get ignored, hooks alone can't handle judgment calls, all three together is what actually works.
+
+## 7\. Subagents: Sending an Independent Claude to Do One Specific Thing
+
+A subagent is an independent Claude instance that branches off from the main conversation, with its own context window and only the tools you allow. The main value is not parallelism by itself, but isolation. Codebase scans, test runs, and review passes that generate large amounts of output can go to a subagent, while the main thread receives only the summary instead of carrying all intermediate output in its context window.
+
+Claude Code has built-in **Explore** (read-only scan, runs Haiku to save cost), **Plan** (planning research), **General-purpose** (general), and custom options.
+
+Explicit Constraints in Configuration
+
+- tools / disallowedTools: Limit what tools can be used; do not grant the same broad permissions as the main thread
+- model: Exploration tasks use Haiku/Sonnet, important reviews use Opus
+- maxTurns: Prevent runaway
+- isolation: worktree: Isolate filesystem when files need modification
+
+Another practical detail: long-running bash commands can be moved to the background with Ctrl+B. Claude can check the results later with the BashOutput tool without blocking the main thread. The same pattern applies to subagents.
+
+## Common Anti-Patterns
+
+- A subagent with the same broad permissions as the main thread; isolation becomes largely meaningless
+- Output format not fixed, unusable by main thread
+- Strong dependencies between subtasks, frequently sharing intermediate state-Subagent not suitable for this
+
+# 8\. Prompt Caching: A First-Class Design Constraint
+
+This topic is discussed less often in tutorials, but it has a major effect on Claude Code's cost structure and design choices.
+
+> Prompt caching is not just a cost optimization. It shapes architecture. A high cache hit rate reduces cost, improves latency, and makes more generous rate limits practical.
+
+## Prompt Layout Designed for Caching
+
+
+
+Prompt caching works by **prefix matching,** content from request start to each cache\_control breakpoint gets cached. So order matters:
+
+Claude Code prompt order:1. System Prompt → Static, locked2. Tool Definitions → Static, locked3. Chat History → Dynamic, comes after4. Current user input → Last
+
+## Common caching pitfalls:
+
+- Putting timestamped content in static system prompt (makes it change every time)
+- Non-deterministically shuffling tool definition order
+- Adding/removing tools mid-session
+
+What about dynamic information such as the current time? Do not put it in the system prompt. Put it in a later message instead. That preserves cache stability.
+
+## Don't Switch Models Mid-Session
+
+Prompt cache is model-specific. If you've chatted 100K tokens with Opus and want to ask a simple question, **switching to Haiku is actually more expensive** than continuing with Opus, because you have to rebuild the entire cache for Haiku.
+
+If you really need to switch, hand off via subagent: Opus prepares a "handoff message" for another model explaining the task to complete.
+
+## Compaction's Actual Implementation
+
+
+
+Above is the compaction flow. On the left, the context window is close to full. In the middle, Claude Code forks a summarization call over the existing conversation, which can benefit from caching. On the right, many turns have been compressed into a shorter summary while the system prompt, tool definitions, and previously referenced files remain available, freeing space for the session to continue.
+
+At first glance, Plan Mode might seem like it should switch to a different read-only toolset, but that would hurt cache reuse. Anthropic has publicly described a tool-driven approach instead: the model can enter plan mode without changing the underlying tool prefix, which preserves cache stability.
+
+## defer\_loading: Lazy Loading for Tools
+
+Claude Code can have many MCP tools. Including every full definition in every request would be expensive, but adding and removing tools mid-session also hurts cache reuse. One approach Anthropic has described is to keep lightweight stubs in the stable prefix and load fuller schemas only when the model selects them.
+
+# 9\. Verification Loop: No Verifier, No Engineering Agent
+
+"Claude says it's done" has little engineering value. What matters is knowing whether it's actually correct, whether you can roll back if something's wrong, and whether the process is auditable.
+
+## Verifier Levels
+
+- Lowest: command exit codes, lint, typecheck, unit test
+- Middle: integration tests, screenshot comparison, contract test, smoke test
+- Higher: production log verification, monitoring metrics, manual review checklists
+
+## Explicitly Define Verification in Prompt, Skill, and CLAUDE.md
+
+```markdown
+## Verification
+
+For backend changes:
+
+- Run \`make test\` and \`make lint\`
+- For API changes, update contract tests under \`tests/contracts/\`
+
+For UI changes:
+
+- Capture before/after screenshots if visual
+
+Definition of done:
+
+- All tests pass
+- Lint passes
+- No TODO left behind unless explicitly tracked
+```
+
+When writing task prompts or skills, define acceptance criteria upfront. Which commands must pass, what to check first if they fail, what screenshots and logs should show to pass, the earlier these are clear, the less trouble later.
+
+My simple test: if you can't clearly explain "how Claude knows it's done correctly," it's probably not suitable to throw at Claude for autonomous completion.
+
+# 10\. Commands Worth Using Frequently
+
+These commands serve one main purpose: manage context actively instead of waiting for the system to manage it for you.
+
+The exact command set can change across releases, but the operational pattern is stable: inspect context usage, control loaded capabilities, and keep long sessions intentional.
+
+## Context Management
+
+```bash
+/context # Inspect token consumption, including MCP and file-read ratios
+/clear # Reset the session; useful when the same issue has already been corrected twice
+/compact # Compress while retaining key points; works best with Compact Instructions
+/memory # Confirm which CLAUDE.md actually got loaded
+```
+
+## Capabilities and Governance
+
+
+
+```markdown
+/mcp # Manage MCP connections, check token costs, disconnect idle servers
+/hooks # Manage hooks; this is a key control-plane entry point
+/permissions # View or update permission whitelist
+/sandbox # Configure sandbox isolation, essential for high-automation scenarios
+/model # Switch models: Opus for deep reasoning, Sonnet for routine, Haiku for quick exploration
+```
+
+## Session Continuity and Parallelism
+
+```bash
+claude --continue # Resume the latest session in the current directory; useful for picking work back up the next day
+claude --resume # Open selector to resume historical session
+claude --continue --fork # Fork from an existing session to try a different approach from the same starting point
+claude --worktree # Create isolated git worktree
+claude -p "prompt" # Non-interactive mode for CI, pre-commit, or other scripts
+claude -p --output-format json # Structured output that scripts can consume directly
+```
+
+## Several Less Common but Useful Commands
+
+**/simplify**: Runs a quick pass over recently modified code with a focus on reuse, quality, and efficiency. Useful immediately after changing logic instead of waiting for later manual review.
+
+**/rewind**: Not an "undo," but a return to an earlier session checkpoint followed by a new summary. Useful when Claude went too far down the wrong path and you want to keep the early consensus but discard later failures.
+
+**/btw**: Ask a quick side question without interrupting the main task. It works well for light tangents such as comparing two commands, but not for questions that require repository reads or tool calls.
+
+**claude -p --output-format stream-json**: Emits a real-time JSON event stream. Useful for long-running task monitoring, incremental processing, or integration into your own tooling.
+
+**/insight**: Ask Claude to analyze the current session and extract what should be codified in CLAUDE.md. Run it after some progress has accumulated; it can surface patterns such as "this convention came up repeatedly but was never written into the contract."
+
+**Double-tap ESC to backtrack**: Brings the previous input back for editing instead of retyping it. If Claude starts down the wrong path, or your last message was underspecified, this is often faster than restarting the session.
+
+**Conversation history is all local**: Session records live under ~/.claude/projects/. Folder names are derived from the project path, and each session is stored as a .jsonl file. To find prior work on a topic, run grep -rl "keyword" ~/.claude/projects/ or ask Claude to search previous discussions about that topic.
+
+# 11\. How to Write a Good CLAUDE.md
+
+I think of CLAUDE.md as a collaboration contract between you and Claude. It is not team documentation and not a knowledge base. Put only the information that must hold across every session.
+
+My advice is simple: start with nothing. Use Claude Code first, then add entries only when you notice yourself repeating the same instruction. To add an entry, type # to append the current conversation to CLAUDE.md, or tell Claude "add this to the project's CLAUDE.md" and it will usually update the right file.
+
+
+
+## What to Include
+
+- Build, test, lint, run commands (most important)
+- Key directory structure and module boundaries
+- Explicit code style and naming constraints
+- Non-obvious environment dependencies and pitfalls
+- Prohibitions and high-risk operations (NEVER list)
+- Information that must survive compression (Compact Instructions)
+
+## What NOT to Include
+
+- Long background introductions
+- Complete API documentation
+- Vague principles like "write high-quality code"
+- Obvious information Claude can infer from reading the repo
+- Large background materials and low-frequency task knowledge (put these in skills)
+
+## High-Quality Template
+
+```markdown
+# Project Contract
+
+## Build And Test
+
+- Install: \`pnpm install\`
+- Dev: \`pnpm dev\`
+- Test: \`pnpm test\`
+- Typecheck: \`pnpm typecheck\`
+- Lint: \`pnpm lint\`
+
+## Architecture Boundaries
+
+- HTTP handlers live in \`src/http/handlers/\`
+- Domain logic lives in \`src/domain/\`
+- Do not put persistence logic in handlers
+- Shared types live in \`src/contracts/\`
+
+## Coding Conventions
+
+- Prefer pure functions in domain layer
+- Do not introduce new global state without explicit justification
+- Reuse existing error types from \`src/errors/\`
+
+## Safety Rails
+
+## NEVER
+
+- Modify \`.env\`, lockfiles, or CI secrets without explicit approval
+- Remove feature flags without searching all call sites
+- Commit without running tests
+
+## ALWAYS
+
+- Show diff before committing
+- Update CHANGELOG for user-facing changes
+
+## Verification
+
+- Backend changes: \`make test\` + \`make lint\`
+- API changes: update contract tests under \`tests/contracts/\`
+- UI changes: capture before/after screenshots
+
+## Compact Instructions
+
+Preserve:
+
+1. Architecture decisions (NEVER summarize)
+2. Modified files and key changes
+3. Current verification status (pass/fail commands)
+4. Open risks, TODOs, rollback notes
+```
+
+The operating rule is straightforward: put what must be known in every session in CLAUDE.md, put file- or path-specific constraints in rules, and put task-specific workflows in skills.
+
+Let Claude Maintain Its Own CLAUDE.md
+
+One useful habit is to update CLAUDE.md immediately after correcting Claude's mistake:
+
+> "Update your CLAUDE.md so you don't make that mistake again."
+
+Claude is often reasonably good at writing these rules for itself, and repeated mistakes do usually become less frequent over time. Still, review the file periodically. Entries go stale, and constraints that once helped may stop paying for themselves. More on keeping it healthy in section 14.
+
+## 12\. Field Notes from a Real Project
+
+Over the Spring Festival break, I built an open-source terminal called [Kaku](https://github.com/tw93/Kaku) with Claude Code. It uses Rust and Lua at the core, plus a custom configuration system. That combination surfaced many of the collaboration problems that agentic coding workflows tend to expose. Here are the practices that helped most.
+
+## "Environment Transparency" Matters More Than You Think
+
+Claude Code calls real shells, git, package managers, and local configurations. As long as one layer is opaque, it has to start guessing; once it starts guessing environment, reliability usually drops fast. This isn't unique to Claude Code, many agents are like this.
+
+I quickly added a doctor command to the terminal that collects environment state, dependencies, and configuration status into a structured health report. Running doctor before Claude Code starts work eliminates many cases where the agent begins from an incorrect understanding of the environment.
+
+I also found that when the CLI exposes clearly semantic subcommands such as init, config, and reset, Claude Code uses them more reliably than when it has to infer where configuration files live. Converge state first, then expose edit entry points. Reversing that order creates unnecessary chaos.
+
+## Hooks Practice for Mixed-Language Projects
+
+Two languages, two checks-Hooks are well-suited to trigger separately by file type:
+
+```json
+{
+ "hooks": {
+ "PostToolUse": [
+ {
+ "matcher": "Edit",
+ "pattern": "*.rs",
+ "hooks": [{
+ "type": "command",
+ "command": "cargo check 2>&1 | head -30",
+ "statusMessage": "Checking Rust..."
+ }]
+ },
+ {
+ "matcher": "Edit",
+ "pattern": "*.lua",
+ "hooks": [{
+ "type": "command",
+ "command": "luajit -b $FILE /dev/null 2>&1 | head -10",
+ "statusMessage": "Checking Lua syntax..."
+ }]
+ }
+ ]
+ }
+}
+```
+
+Knowing immediately that a file no longer compiles is much better than running a long sequence of steps only to discover it was broken from the start.
+
+## Complete Engineering Layout Reference
+
+If you want a complete Claude Code setup for your project, here is a reference structure. Treat it as a reference, not a template:
+
+```plaintext
+Project/
+├── CLAUDE.md
+├── .claude/
+│ ├── rules/
+│ │ ├── core.md
+│ │ ├── config.md
+│ │ └── release.md
+│ ├── skills/
+│ │ ├── runtime-diagnosis/ # Uniformly collect logs, state, dependencies
+│ │ ├── config-migration/ # Config migration rollback protection
+│ │ ├── release-check/ # Pre-release validation, smoke test
+│ │ └── incident-triage/ # Production incident triage
+│ ├── agents/
+│ │ ├── reviewer.md
+│ │ └── explorer.md
+│ └── settings.json
+└── docs/
+ └── ai/
+ ├── architecture.md
+ └── release-runbook.md
+```
+
+With global constraints (CLAUDE.md), path constraints (rules), workflows (skills), and deeper architectural detail separated cleanly, Claude Code behaves much more predictably. If you maintain multiple projects, keep stable personal defaults in ~/.claude/ and project-specific differences in each project's .claude/ directory to reduce cross-project pollution.
+
+# 13\. Common Anti-Patterns
+
+
+
+# 14\. Configuration Health Check
+
+Based on the six-layer framework in this handbook, I packaged these checks into an open-source skill project, [tw93/claude-health](https://github.com/tw93/claude-health)**,** which can evaluate your Claude Code configuration in one pass.
+
+> npx skills add tw93/claude-health -a claude-code -s health -g -y
+
+After installing, run /health in any session. It evaluates project complexity, checks CLAUDE.md, rules, skills, hooks, allowedTools, and recurring behavior patterns, then outputs a prioritized report: fix now, structural issues, and gradual improvements.
+
+If you want to know how far your configuration is from these principles after reading this handbook, running /health is the fastest way.
+
+# 15\. Conclusion
+
+Using Claude Code typically involves three stages:
+
+
+
+At stage three, the question shifts from "how do I use this feature?" to "how do I make the agent operate autonomously under constraints?" Those are fundamentally different concerns.
+
+One practical test matters more than most: if you cannot clearly articulate what "done" looks like, the task is probably not ready for autonomous execution by Claude. Without acceptance criteria, there is no reliable notion of a correct answer, no matter how capable the model is.
+
+These are my takeaways from six months of sustained use. There is still more to learn, but this framework has held up well in practice. If you have found a better operating pattern, I would be interested in comparing notes.
\ No newline at end of file
diff --git a/Claude/shanraisshanclaude-code-best-practice practice made claude perfect.md b/Claude/shanraisshanclaude-code-best-practice practice made claude perfect.md
new file mode 100644
index 0000000..6eddb56
--- /dev/null
+++ b/Claude/shanraisshanclaude-code-best-practice practice made claude perfect.md
@@ -0,0 +1,216 @@
+---
+title: "shanraisshan/claude-code-best-practice: practice made claude perfect"
+source: "https://github.com/shanraisshan/claude-code-best-practice"
+author:
+ - "[[shanraisshan]]"
+published:
+created: 2026-03-07
+description: "practice made claude perfect. Contribute to shanraisshan/claude-code-best-practice development by creating an account on GitHub."
+tags:
+ - "clippings"
+---
+You only have a single verified email address. We recommend verifying at least one more email address to ensure you can recover your account if you lose access to your primary email.
+
+[Open in github.dev](https://github.dev/) [Open in a new github.dev tab](https://github.dev/) [Open in codespace](https://github.com/codespaces/new/shanraisshan/claude-code-best-practice?resume=1)
+
+## claude-code-best-practice
+
+practice makes claude perfect
+
+*Click on this badge to show the latest best practice*
+*Click on this badge to show implementation in this repo*
+*Click on this badge to see the Command → Agent → Skill orchestration workflow*
+
+[](https://github.com/shanraisshan/claude-code-best-practice/blob/main/!/claude-jumping.svg)
+
+[](https://github.com/shanraisshan/claude-code-best-practice/blob/main/!/slider/boris-slider.gif)
+Boris Cherny on X ([tweet 1](https://x.com/bcherny/status/2007179832300581177) · [tweet 2](https://x.com/bcherny/status/2017742741636321619) · [tweet 3](https://x.com/bcherny/status/2021699851499798911))
+
+## CONCEPTS
+
+| Feature | Location | Description |
+| --- | --- | --- |
+| [**Commands**](https://code.claude.com/docs/en/slash-commands) | `.claude/commands/.md` | Entry-point prompts for workflows — invoke with `/command-name` |
+| [**Sub-Agents**](https://code.claude.com/docs/en/sub-agents) | `.claude/agents/.md` | Custom agents with their own name, color, tools, permissions, and model |
+| [**Skills**](https://code.claude.com/docs/en/skills) | `.claude/skills//SKILL.md` | Reusable knowledge, workflows, and slash commands — load on-demand or invoke with `/skill-name` |
+| [**Workflows**](https://code.claude.com/docs/en/common-workflows) | [`.claude/commands/weather-orchestrator.md`](https://github.com/shanraisshan/claude-code-best-practice/blob/main/.claude/commands/weather-orchestrator.md) | |
+| [**Hooks**](https://code.claude.com/docs/en/hooks) | `.claude/hooks/` | Deterministic scripts that run outside the agentic loop on specific events |
+| [**MCP Servers**](https://code.claude.com/docs/en/mcp) | `.claude/settings.json`, `.mcp.json` | Model Context Protocol connections to external tools, databases, and APIs |
+| [**Plugins**](https://code.claude.com/docs/en/plugins) | distributable packages | Bundles of skills, subagents, hooks, and MCP servers · [Marketplaces](https://code.claude.com/docs/en/discover-plugins) |
+| [**Settings**](https://code.claude.com/docs/en/settings) | `.claude/settings.json` | Hierarchical configuration system · [Permissions](https://code.claude.com/docs/en/permissions) · [Model Config](https://code.claude.com/docs/en/model-config) · [Output Styles](https://code.claude.com/docs/en/output-styles) · [Sandboxing](https://code.claude.com/docs/en/sandboxing) · [Keybindings](https://code.claude.com/docs/en/keybindings) · [Fast Mode](https://code.claude.com/docs/en/fast-mode) |
+| [**Status Line**](https://code.claude.com/docs/en/statusline) | `.claude/settings.json` | Customizable status bar showing context usage, model, cost, and session info |
+| [**Memory**](https://code.claude.com/docs/en/memory) | `CLAUDE.md`, `.claude/rules/`, `~/.claude/rules/`, `~/.claude/projects//memory/` | Persistent context via CLAUDE.md files and `@path` imports · [Auto Memory](https://code.claude.com/docs/en/memory) · [Rules](https://code.claude.com/docs/en/memory#organize-rules-with-clauderules) |
+| [**Checkpointing**](https://code.claude.com/docs/en/checkpointing) | automatic (git-based) | Automatic tracking of file edits with rewind (`Esc Esc` or `/rewind`) and targeted summarization |
+| [**CLI Startup Flags**](https://code.claude.com/docs/en/cli-reference) | `claude [flags]` | Command-line flags, subcommands, and environment variables for launching Claude Code |
+| **AI Terms** | | Agentic Engineering · Context Engineering · Vibe Coding |
+| [**Best Practices**](https://code.claude.com/docs/en/best-practices) | | Official best practices · [Prompt Engineering](https://github.com/anthropics/prompt-eng-interactive-tutorial) · [Extend Claude Code](https://code.claude.com/docs/en/features-overview) |
+
+### 🔥 Hot
+
+| Feature | Location | Description |
+| --- | --- | --- |
+| [**Scheduled Tasks**](https://code.claude.com/docs/en/scheduled-tasks) | `/loop`, cron tools | Run prompts on a recurring schedule (up to 3 days), set one-time reminders, poll deployments and builds |
+| [**Agent Teams**](https://code.claude.com/docs/en/agent-teams) | built-in (env var) | Multiple agents working in parallel on the same codebase with shared task coordination |
+| [**Voice Mode**](https://x.com/trq212/status/2028628570692890800) | built-in command | speak to prompt - /voice to activate |
+| [**Remote Control**](https://code.claude.com/docs/en/remote-control) | built-in command | Continue local sessions from any device — phone, tablet, or browser · [Headless Mode](https://code.claude.com/docs/en/headless) |
+| [**Git Worktrees**](https://code.claude.com/docs/en/common-workflows) | built-in | Isolated git branches for parallel development — each agent gets its own working copy |
+| [**Ralph Wiggum Loop**](https://github.com/anthropics/claude-code/tree/main/plugins/ralph-wiggum) | plugin | Autonomous development loop for long-running tasks — iterates until completion |
+
+See [orchestration-workflow](https://github.com/shanraisshan/claude-code-best-practice/blob/main/orchestration-workflow/orchestration-workflow.md) for implementation details of **Command → Agent → Skill** pattern.
+
+[](https://github.com/shanraisshan/claude-code-best-practice/blob/main/orchestration-workflow/orchestration-workflow.svg)
+
+[](https://github.com/shanraisshan/claude-code-best-practice/blob/main/orchestration-workflow/orchestration-workflow.gif)
+
+```
+claude
+/weather-orchestrator
+```
+
+| Component | Role | Example |
+| --- | --- | --- |
+| **Command** | Entry point, user interaction | [`/weather-orchestrator`](https://github.com/shanraisshan/claude-code-best-practice/blob/main/.claude/commands/weather-orchestrator.md) |
+| **Agent** | Fetches data with preloaded skill (agent skill) | [`weather-agent`](https://github.com/shanraisshan/claude-code-best-practice/blob/main/.claude/agents/weather-agent.md) with [`weather-fetcher`](https://github.com/shanraisshan/claude-code-best-practice/blob/main/.claude/skills/weather-fetcher/SKILL.md) |
+| **Skill** | Creates output independently (skill) | [`weather-svg-creator`](https://github.com/shanraisshan/claude-code-best-practice/blob/main/.claude/skills/weather-svg-creator/SKILL.md) |
+
+## DEVELOPMENT WORKFLOWS
+
+### 🔥 Hot
+
+- [Cross-Model (Claude Code + Codex) Workflow](https://github.com/shanraisshan/claude-code-best-practice/blob/main/development-workflows/cross-model-workflow/cross-model-workflow.md)
+- [RPI](https://github.com/shanraisshan/claude-code-best-practice/blob/main/development-workflows/rpi/rpi-workflow.md)
+- [Ralph Wiggum Loop](https://www.youtube.com/watch?v=eAtvoGlpeRU)
+
+### Others
+
+- [Github Speckit](https://github.com/github/spec-kit) · ★ 74k
+- [obra/superpowers](https://github.com/obra/superpowers) · ★ 72k
+- [OpenSpec OPSX](https://github.com/Fission-AI/OpenSpec/blob/main/docs/opsx.md) · ★ 28k
+- [get-shit-done (GSD)](https://github.com/gsd-build/get-shit-done) · ★ 25k
+- [Andrej Karpathy (Founding Member, OpenAI) Workflow](https://github.com/forrestchang/andrej-karpathy-skills) · ★ 7k
+- [Brian Casel (Creator of Agent OS) - 2026 Workflow](https://github.com/buildermethods/agent-os) · ★ 4k - [it's overkill in 2026](https://www.youtube.com/watch?v=0hdFJA-ho3c)
+- [Human Layer RPI - Research Plan Implement](https://github.com/humanlayer/advanced-context-engineering-for-coding-agents/blob/main/ace-fca.md) · ★ 1.5k
+- [Boris Cherny (Creator of Claude Code) - Feb 2026 Workflow](https://x.com/bcherny/status/2017742741636321619)
+- [Peter Steinberger (Creator of OpenClaw) Workflow](https://youtu.be/8lF7HmQ_RgY?t=2582)
+
+■ **Planning (2)**
+
+- always start with [plan mode](https://code.claude.com/docs/en/common-workflows). ask Claude to interview you; [ask the user a question](https://code.claude.com/docs/en/cli-reference)
+- always make a phase-wise gated plan, with each phase having multiple tests (unit, automation, integration). use [cross-model](https://github.com/shanraisshan/claude-code-best-practice/blob/main/development-workflows/cross-model-workflow/cross-model-workflow.md) to review your plan
+
+■ **Workflows (12)**
+
+- [CLAUDE.md](https://code.claude.com/docs/en/memory) should target under [200 lines](https://code.claude.com/docs/en/memory#write-effective-instructions) per file. [60 lines in humanlayer](https://www.humanlayer.dev/blog/writing-a-good-claude-md) ([still not 100% guaranteed](https://www.reddit.com/r/ClaudeCode/comments/1qn9pb9/claudemd_says_must_use_agent_claude_ignores_it_80/)).
+- use [multiple CLAUDE.md](https://github.com/shanraisshan/claude-code-best-practice/blob/main/best-practice/claude-memory.md) for monorepos — ancestor + descendant loading
+- use [.claude/rules/](https://code.claude.com/docs/en/memory#organize-rules-with-clauderules) to split large instructions
+- use [commands](https://code.claude.com/docs/en/slash-commands) for your workflows instead of [sub-agents](https://code.claude.com/docs/en/sub-agents)
+- have feature specific [sub-agents](https://code.claude.com/docs/en/sub-agents) (extra context) with [skills](https://code.claude.com/docs/en/skills) (progressive disclosure) instead of general qa, backend engineer.
+- [memory.md](https://code.claude.com/docs/en/memory), constitution.md does not guarantee anything
+- avoid agent dumb zone, do manual [/compact](https://code.claude.com/docs/en/interactive-mode) at max 50%. Use [/clear](https://code.claude.com/docs/en/cli-reference) to reset context mid-session if switching to a new task
+- vanilla cc is better than any workflows with smaller tasks
+- use [skills in subfolders](https://github.com/shanraisshan/claude-code-best-practice/blob/main/reports/claude-skills-for-larger-mono-repos.md) for monorepos
+- use [/model](https://code.claude.com/docs/en/model-config) to select model and reasoning, [/context](https://code.claude.com/docs/en/interactive-mode) to see context usage, [/usage](https://code.claude.com/docs/en/costs) to check plan limits, [/extra-usage](https://code.claude.com/docs/en/interactive-mode) to configure overflow billing, [/config](https://code.claude.com/docs/en/settings) to configure settings
+- always use [thinking mode](https://code.claude.com/docs/en/model-config) true (to see reasoning) and [Output Style](https://code.claude.com/docs/en/output-styles) Explanatory (to see detailed output with ★ Insight boxes) in [/config](https://code.claude.com/docs/en/settings) for better understanding of Claude's decisions
+- use ultrathink keyword in prompts for [high effort reasoning](https://docs.anthropic.com/en/docs/build-with-claude/extended-thinking#tips-and-best-practices)
+- [/rename](https://code.claude.com/docs/en/cli-reference) important sessions (e.g. \[TODO - refactor task\]) and [/resume](https://code.claude.com/docs/en/cli-reference) them later
+- use [Esc Esc or /rewind](https://code.claude.com/docs/en/checkpointing) to undo when Claude goes off-track instead of trying to fix it in the same context
+- commit often — try to commit at least once per hour, as soon as task is completed, commit.
+
+■ **Workflows Advanced (6)**
+
+- use ASCII diagrams a lot to understand your architecture
+- [agent teams with tmux](https://code.claude.com/docs/en/agent-teams) and [git worktrees](https://x.com/bcherny/status/2025007393290272904) for parallel development
+- use [/loop](https://code.claude.com/docs/en/scheduled-tasks) for recurring monitoring — poll deployments, babysit PRs, check builds (runs up to 3 days)
+- use [Ralph Wiggum plugin](https://github.com/shanraisshan/novel-llm-26) for long-running autonomous tasks
+- [/permissions](https://code.claude.com/docs/en/permissions) with wildcard syntax (Bash(npm run \*), Edit(/docs/\*\*)) instead of dangerously-skip-permissions
+- [/sandbox](https://code.claude.com/docs/en/sandboxing) to reduce permission prompts with file and network isolation
+
+■ **Debugging (5)**
+
+- make it a habit to take screenshots and share with Claude whenever you are stuck with any issue
+- use mcp ([Claude in Chrome](https://code.claude.com/docs/en/chrome), [Playwright](https://github.com/microsoft/playwright-mcp), [Chrome DevTools](https://developer.chrome.com/blog/chrome-devtools-mcp)) to let claude see chrome console logs on its own
+- always ask claude to run the terminal (you want to see logs of) as a background task for better debugging
+- [/doctor](https://code.claude.com/docs/en/cli-reference) to diagnose installation, authentication, and configuration issues
+- error during compaction can be resolved by using [/model](https://code.claude.com/docs/en/model-config) to select a 1M token model, then running [/compact](https://code.claude.com/docs/en/interactive-mode)
+- use a [cross-model](https://github.com/shanraisshan/claude-code-best-practice/blob/main/development-workflows/cross-model-workflow/cross-model-workflow.md) for QA — e.g. [Codex](https://github.com/shanraisshan/codex-cli-best-practice) for plan and implementation review
+
+■ **Utilities (5)**
+
+- [iTerm](https://iterm2.com/) / [Ghostty](https://ghostty.org/) / [tmux](https://github.com/tmux/tmux) terminals instead of IDE ([VS Code](https://code.visualstudio.com/) / [Cursor](https://www.cursor.com/))
+- [Wispr Flow](https://wisprflow.ai/) for voice prompting (10x productivity)
+- [claude-code-voice-hooks](https://github.com/shanraisshan/claude-code-voice-hooks) for claude feedback
+- [status line](https://github.com/shanraisshan/claude-code-status-line) for context awareness and fast compacting
+- explore [settings.json](https://github.com/shanraisshan/claude-code-best-practice/blob/main/best-practice/claude-settings.md) features like [Plans Directory](https://github.com/shanraisshan/claude-code-best-practice/blob/main/best-practice/claude-settings.md#plans-directory), [Spinner Verbs](https://github.com/shanraisshan/claude-code-best-practice/blob/main/best-practice/claude-settings.md#display--ux) for a personalized experience
+
+■ **Daily (3)**
+
+- [update](https://code.claude.com/docs/en/setup) Claude Code daily and start your day by reading the [changelog](https://github.com/anthropics/claude-code/blob/main/CHANGELOG.md)
+- follow [r/ClaudeAI](https://www.reddit.com/r/ClaudeAI/), [r/ClaudeCode](https://www.reddit.com/r/ClaudeCode/) on Reddit
+- follow [Boris](https://x.com/bcherny), [Thariq](https://x.com/trq212), [Cat](https://x.com/_catwu), [Lydia](https://x.com/lydiahallie), [Noah](https://x.com/noahzweben) on X
+- [Always use plan mode, give Claude a way to verify, use /code-review | 27/Dec/25](https://x.com/bcherny/status/2004711722926616680) ● [Tweet](https://x.com/bcherny/status/2004711722926616680)
+- [Ask Claude to interview you using AskUserQuestion tool (Thariq) | 28/Dec/25](https://x.com/trq212/status/2005315275026260309) ● [Tweet](https://x.com/trq212/status/2005315275026260309)
+- [Boris setup - 5 tips | 03/Jan/26](https://x.com/bcherny/status/2007179832300581177) ● [Tweet](https://x.com/bcherny/status/2007179832300581177)
+- [10 tips for using claude code by team itself | 01/Feb/26](https://x.com/bcherny/status/2017742741636321619) ● [Tweet](https://x.com/bcherny/status/2017742741636321619)
+- [12 ways how people are customizing their claudes | 12/Feb/26](https://github.com/shanraisshan/claude-code-best-practice/blob/main/tips/claude-boris-tips-feb-26.md) ● [Tweet](https://x.com/bcherny/status/2021699851499798911)
+- [Git Worktrees - 5 ways how boris is using | 21 Feb 2026](https://x.com/bcherny/status/2025007393290272904) ● [Tweet](https://x.com/bcherny/status/2025007393290272904)
+- [Seeing like an Agent - lessons from building Claude Code (Thariq) | 28 Feb 2026](https://x.com/trq212/status/2027463795355095314) ● [Tweet](https://x.com/trq212/status/2027463795355095314)
+- [/loop — schedule recurring tasks for up to 3 days | 07 Mar 2026](https://x.com/bcherny/status/2030193932404150413) ● [Tweet](https://x.com/bcherny/status/2030193932404150413)
+
+| Claude | Replaced |
+| --- | --- |
+| [**Voice Mode**](https://x.com/trq212/status/2028628570692890800) | [Wispr Flow](https://wisprflow.ai/), [SuperWhisper](https://superwhisper.com/) |
+| [**Remote Control**](https://code.claude.com/docs/en/remote-control) | [OpenClaw](https://openclaw.ai/) |
+| **Cowork** | [OpenAI Operator](https://openai.com/operator), [AgentShadow](https://agentshadow.ai/) |
+| [**Tasks**](https://x.com/trq212/status/2014480496013803643) | [Beads](https://github.com/steveyegge/beads) |
+| [**Plan Mode**](https://code.claude.com/docs/en/common-workflows) | [Agent OS](https://github.com/buildermethods/agent-os) |
+| [**Skills / Plugins**](https://code.claude.com/docs/en/plugins) | YC AI wrapper startups ([reddit](https://reddit.com/r/ClaudeAI/comments/1r6bh4d/claude_code_skills_are_basically_yc_ai_startup/)) |
+
+*If you have answers, do let me know at [shanraisshan@gmail.com](https://github.com/shanraisshan/)*
+
+**Memory & Instructions (4)**
+
+1. What exactly should you put inside your CLAUDE.md — and what should you leave out?
+2. If you already have a CLAUDE.md, is a separate constitution.md or rules.md actually needed?
+3. How often should you update your CLAUDE.md, and how do you know when it's become stale?
+4. Why does Claude still ignore CLAUDE.md instructions — even when they say MUST in all caps? ([reddit](https://reddit.com/r/ClaudeCode/comments/1qn9pb9/claudemd_says_must_use_agent_claude_ignores_it_80/))
+
+**Agents, Skills & Workflows (6)**
+
+1. When should you use a command vs an agent vs a skill — and when is vanilla Claude Code just better?
+2. How often should you update your agents, commands, and workflows as models improve?
+3. Does giving your subagent a detailed persona improve quality? What does a "perfect persona/prompt" for research/QA subagent look like?
+4. Should you rely on Claude Code's built-in plan mode — or build your own planning command/agent that enforces your team's workflow?
+5. If you have a personal skill (e.g., /implement with your coding style), how do you incorporate community skills (e.g., /simplify) without conflicts — and who wins when they disagree?
+6. Are we there yet? Can we convert an existing codebase into specs, delete the code, and have AI regenerate the exact same code from those specs alone?
+
+**Specs & Documentation (3)**
+
+1. Should every feature in your repo have a spec as a markdown file?
+2. How often do you need to update specs so they don't become obsolete when a new feature is implemented?
+3. When implementing a new feature, how do you handle the ripple effect on specs for other features?
+
+## REPORTS
+
+| Report | Description |
+| --- | --- |
+| [Agent SDK vs CLI System Prompts](https://github.com/shanraisshan/claude-code-best-practice/blob/main/reports/claude-agent-sdk-vs-cli-system-prompts.md) | Why Claude CLI and Agent SDK outputs may differ—system prompt architecture and determinism |
+| [Browser Automation MCP Comparison](https://github.com/shanraisshan/claude-code-best-practice/blob/main/reports/claude-in-chrome-v-chrome-devtools-mcp.md) | Comparison of Playwright, Chrome DevTools, and Claude in Chrome for automated testing |
+| [Global vs Project Settings](https://github.com/shanraisshan/claude-code-best-practice/blob/main/reports/claude-global-vs-project-settings.md) | Which features are global-only (`~/.claude/`) vs dual-scope, including Tasks and Agent Teams |
+| [Skills Discovery in Monorepos](https://github.com/shanraisshan/claude-code-best-practice/blob/main/reports/claude-skills-for-larger-mono-repos.md) | How skills are discovered and loaded in large monorepo projects |
+| [Agent Memory Frontmatter](https://github.com/shanraisshan/claude-code-best-practice/blob/main/reports/claude-agent-memory.md) | Persistent memory scopes (`user`, `project`, `local`) for subagents — enabling agents to learn across sessions |
+| [Advanced Tool Use Patterns](https://github.com/shanraisshan/claude-code-best-practice/blob/main/reports/claude-advanced-tool-use.md) | Programmatic Tool Calling (PTC), Tool Search, and Tool Use Examples |
+| [Usage, Rate Limits & Extra Usage](https://github.com/shanraisshan/claude-code-best-practice/blob/main/reports/claude-usage-and-rate-limits.md) | Usage commands (`/usage`, `/extra-usage`, `/cost`), rate limits, and pay-as-you-go overflow billing |
+| [LLM Day-to-Day Degradation](https://github.com/shanraisshan/claude-code-best-practice/blob/main/reports/llm-day-to-day-degradation.md) | Why LLM performance varies day-to-day — infrastructure bugs, MoE routing variance, and psychology |
+
+## Releases
+
+No releases published
+
+## Packages
+
+No packages published
+
+## Languages
+
+- [HTML 88.8%](https://github.com/shanraisshan/claude-code-best-practice/search?l=html)
+- [Python 11.2%](https://github.com/shanraisshan/claude-code-best-practice/search?l=python)
\ No newline at end of file
diff --git a/Claude/你不知道的 Claude Code：架构、治理与工程实践.md b/Claude/你不知道的 Claude Code：架构、治理与工程实践.md
new file mode 100644
index 0000000..853179f
--- /dev/null
+++ b/Claude/你不知道的 Claude Code：架构、治理与工程实践.md
@@ -0,0 +1,741 @@
+---
+title: "你不知道的 Claude Code：架构、治理与工程实践"
+source: "https://x.com/HiTw93/status/2032091246588518683"
+author:
+ - "[[Unknown]]"
+published: 2026-03-12
+created: 2026-03-20
+description:
+tags:
+ - "clippings"
+---
+今天这篇文章源于最近半年深度使用 Claude Code、两个账号每月 40 刀氪金换来的一些踩坑经验，希望能给大伙一些输入。
+
+刚开始我也把它当 ChatBot 用，后来很快发现不对劲：上下文越来越乱、工具越来越多但效果越来越差、规则越写越长却越不遵守，折腾了一段时间，研究了 Claude Code 本身之后才意识到，这不是 Prompt 问题，而是这套系统的设计就是这样的。
+
+这篇文章想和大伙聊聊这几个事：Claude Code 底层怎么运作、上下文为什么会乱以及怎么治理、Skills 和 Hooks 应该怎么设计、Subagents 的正确用法、Prompt Caching 的架构影响，以及怎么写一个真正有用的 CLAUDE.md。
+
+我觉得最直接的理解方式，是把 Claude Code 拆成六层来看：
+
+
+
+只强化其中一层，系统就会失衡，CLAUDE.md 写太长，上下文先污染自己了；工具堆太多了，选择就搞不清楚了；subagents 开得到处都是，状态就漂移了；验证这步跳过了，出了问题根本不知道是哪里挂的。
+
+# 1\. 它底层是怎么运行的
+
+
+
+Claude Code 的核心不是"回答"，而是一个反复循环的代理过程：
+
+```text
+收集上下文 → 采取行动 → 验证结果 → [完成 or 回到收集]
+ ↑ ↓
+ CLAUDE.md Hooks / 权限 / 沙箱
+ Skills Tools / MCP
+ Memory
+```
+
+用了一段时间才意识到，卡住的地方几乎从来不是模型不够聪明，更多时候是给了它错误的上下文，或者写出来了但根本没法判断对不对，也没法撤回。
+
+## 真正要关注的五个层面：
+
+
+
+对着这几个面看，很多问题就好排查了。结果不稳定，查上下文加载顺序，不是模型的事；自动化失控，看控制层有没有设计，不是 agent 太主动；长会话质量下降，中间产物把上下文污染了，换个新会话比反复调 prompt 有用得多。
+
+# 2\. 概念边界：MCP / Plugin / Tools / Skills / Hooks / Subagents
+
+
+
+简单记：给 Claude 新动作能力用 Tool/MCP，给它一套工作方法用 Skill，需要隔离执行环境用 Subagent，要强制约束和审计用 Hook，跨项目分发用 Plugin。
+
+# 3\. 上下文工程：最重要的系统约束
+
+很多人把上下文当"容量问题"，但卡住的地方通常不是不够长，而是太吵了，有用的信息被大量无关内容淹没了。
+
+## 真实的上下文成本构成
+
+
+
+Claude Code 的 200K 上下文并非全部可用：
+
+```text
+200K 总上下文
+├── 固定开销 (~15-20K)
+│ ├── 系统指令: ~2K
+│ ├── 所有启用的 Skill 描述符: ~1-5K
+│ ├── MCP Server 工具定义: ~10-20K ← 最大隐形杀手
+│ └── LSP 状态: ~2-5K
+│
+├── 半固定 (~5-10K)
+│ ├── CLAUDE.md: ~2-5K
+│ └── Memory: ~1-2K
+│
+└── 动态可用 (~160-180K)
+ ├── 对话历史
+ ├── 文件内容
+ └── 工具调用结果
+```
+
+
+
+一个典型 MCP Server（如 GitHub）包含 20-30 个工具定义，每个约 200 tokens，合计 **4,000-6,000 tokens**。接 5 个 Server，光这部分固定开销就到了 **25,000 tokens（12.5%）**。我第一次算出这个数字的时候，真没想到有这么多，在要读大量代码的场景，这 12.5% 真的很关键。
+
+## 推荐的上下文分层
+
+```text
+始终常驻 → CLAUDE.md：项目契约 / 构建命令 / 禁止事项
+按路径加载 → rules：语言 / 目录 / 文件类型特定规则
+按需加载 → Skills：工作流 / 领域知识
+隔离加载 → Subagents：大量探索 / 并行研究
+不进上下文 → Hooks：确定性脚本 / 审计 / 阻断
+```
+
+说白了，偶尔用的东西就不要每次都加载进来。
+
+## 上下文最佳实践
+
+- 保持 CLAUDE.md 短、硬、可执行，优先写命令、约束、架构边界。Anthropic 官方自己的 CLAUDE.md 大约只有 2.5K tokens，可以参考
+- 把大型参考文档拆到 Skills 的 supporting files，不要塞进 SKILL.md 正文
+- 使用 .claude/rules/ 做路径/语言规则，不让根 CLAUDE.md 承担所有差异
+- 长会话主动用 /context 观察消耗，不要等系统自动压缩后再补救
+
+
+
+- 任务切换优先 /clear，同一任务进入新阶段用 /compact
+- **把 Compact Instructions 写进 CLAUDE.md**，压缩后必须保留什么由你控制，不由算法猜
+
+## Tool Output 噪声：另一个隐形上下文杀手
+
+前面算的是 MCP 工具定义的固定开销，但动态部分同样有个坑容易被忽视：Tool Output。cargo test 一次完整输出动辄几千行，git log、find、grep 在稍大的仓库里也能轻松塞满屏幕。这些输出 Claude 并不需要全看，但只要它出现在上下文里，就是实实在在的 token 消耗，同样会挤掉对话历史和文件内容的空间。
+
+后来看到 [RTK（Rust Token Killer）](https://www.rtk-ai.app/) 这个思路觉得挺对的，它做的事很简单：在命令输出到 Claude 之前自动过滤，只留决策需要的核心信息。比如 cargo test：
+
+```text
+# Claude 看到的原始输出
+running 262 tests
+test auth::test_login ... ok
+...（几千行）
+
+# 走 RTK 之后
+✓ cargo test: 262 passed (1 suite, 0.08s)
+```
+
+Claude 真正需要知道的就是「过了还是挂了，挂在哪里」，其他都是噪声。它通过 Hook 透明重写命令，对 Claude Code 来说完全无感。
+
+后面第 6 节会提到 | head -30 这种手动截断，RTK 干的就是这件事，只是覆盖面更广，不用每条命令自己加，项目 [开源在 GitHub](https://github.com/rtk-ai/rtk)。
+
+## 压缩机制的陷阱
+
+默认压缩算法按"可重新读取"判断，早期的 Tool Output 和文件内容会被优先删掉，顺带把**架构决策和约束理由**也一起扔了。两小时后再改，可能根本不记得两小时前定了什么，莫名其妙的 Bug 就是这么来的。
+
+
+
+解决方案就是在 CLAUDE.md 里写明：
+
+```markdown
+## Compact Instructions
+
+When compressing, preserve in priority order:
+
+1. Architecture decisions (NEVER summarize)
+2. Modified files and their key changes
+3. Current verification status (pass/fail)
+4. Open TODOs and rollback notes
+5. Tool outputs (can delete, keep pass/fail only)
+```
+
+除了写 Compact Instructions，还有一种更主动的方案：在开新会话前，先让 Claude 写一份 HANDOFF.md，把当前进度、尝试过什么、哪些走通了、哪些是死路、下一步该做什么写清楚。下一个 Claude 实例只读这个文件就能接着做，不依赖压缩算法的摘要质量：
+
+在 HANDOFF.md 里写清楚现在的进展。解释你试了什么、什么有效、什么没用，让下一个拿到新鲜上下文的 agent 只看这个文件就能继续完成任务。
+
+写完后快速扫一眼，有缺漏直接让它补，然后开新会话，把 HANDOFF.md 的路径发过去就行。
+
+## Plan Mode 的工程价值
+
+
+
+Plan Mode 的核心是把探索和执行拆开，探索阶段不动文件，确认方案后再执行：
+
+- 探索阶段以只读操作为主
+- Claude 可以先澄清目标和边界，再提交具体方案
+- 执行成本在计划确认之后才发生
+
+
+
+对于复杂重构、迁移、跨模块改动，这样做比"急着出代码"有用多了，在错误假设上越跑越偏的情况会少很多。按两下 Shift+Tab 进入 Plan Mode，**进阶玩法是开一个 Claude 写计划，再开一个 Codex 以"高级工程师"身份审这个计划，让 AI 审 AI，效果很好**。
+
+# 4\. Skills 设计：不是模板库，是用的时候才加载的工作流
+
+Skill 官方描述是"按需加载的知识与工作流"，描述符常驻上下文，完整内容按需加载，用起来和"保存的 Prompt"差别挺大的。
+
+## 一个好 Skill 应该满足什么
+
+- 描述要让模型知道"何时该用我"，而不是"我是干什么的"，这两个差很多
+- 有完整步骤、输入、输出和停止条件，别写了个开头没有结尾
+- 正文只放导航和核心约束，大资料拆到 supporting files 里
+- 有副作用的 Skill 要显式设置 disable-model-invocation: true，不然 Claude 会自己决定要不要跑
+
+## Skill 怎么做到按需加载
+
+Claude Code 团队在内部设计中反复强调 "progressive disclosure"，意思不是让模型一次性看到所有信息，而是先获得索引和导航，再按需拉取细节：
+
+- SKILL.md 负责定义任务语义、边界和执行骨架
+- supporting files 负责提供领域细节
+- 脚本负责确定性收集上下文或证据
+
+一个比较稳定的结构长这样：
+
+```text
+.claude/skills/
+└── incident-triage/
+ ├── SKILL.md
+ ├── runbook.md
+ ├── examples.md
+ └── scripts/
+ └── collect-context.sh
+```
+
+## Skill 的三种典型类型
+
+下面几个例子都来自我在开源 terminal 项目 [Kaku](https://github.com/tw93/Kaku) 里的实际 Skill，比较直观。
+
+**类型一：检查清单型（质量门禁）**
+
+发布前跑一遍，确保不漏项：
+
+```yaml
+---
+name: release-check
+description: Use before cutting a release to verify build, version, and smoke test.
+---
+
+## Pre-flight (All must pass)
+- [ ] \`cargo build --release\` passes
+- [ ] \`cargo clippy -- -D warnings\` clean
+- [ ] Version bumped in Cargo.toml
+- [ ] CHANGELOG updated
+- [ ] \`kaku doctor\` passes on clean env
+
+## Output
+Pass / Fail per item. Any Fail must be fixed before release.
+```
+
+**类型二：工作流型（标准化操作）**
+
+配置迁移高风险，显式调用 + 内置回滚步骤：
+
+```yaml
+---
+name: config-migration
+description: Migrate config schema. Run only when explicitly requested.
+disable-model-invocation: true
+---
+
+## Steps
+1. Backup: \`cp ~/.config/kaku/config.toml ~/.config/kaku/config.toml.bak\`
+2. Dry run: \`kaku config migrate --dry-run\`
+3. Apply: remove \`--dry-run\` after confirming output
+4. Verify: \`kaku doctor\` all pass
+
+## Rollback
+\`cp ~/.config/kaku/config.toml.bak ~/.config/kaku/config.toml\`
+```
+
+**类型三：领域专家型（封装决策框架）**
+
+运行时出问题时让 Claude 按固定路径收集证据，不要瞎猜：
+
+```yaml
+---
+name: runtime-diagnosis
+description: Use when kaku crashes, hangs, or behaves unexpectedly at runtime.
+---
+
+## Evidence Collection
+1. Run \`kaku doctor\` and capture full output
+2. Last 50 lines of \`~/.local/share/kaku/logs/\`
+3. Plugin state: \`kaku --list-plugins\`
+
+## Decision Matrix
+| Symptom | First Check |
+|---|---|
+| Crash on startup | doctor output → Lua syntax error |
+| Rendering glitch | GPU backend / terminal capability |
+| Config not applied | Config path + schema version |
+
+## Output Format
+Root cause / Blast radius / Fix steps / Verification command
+```
+
+描述符写短点，每个 Skill 都在偷你的上下文空间，每个启用的 Skill，描述符常驻上下文，优化前后差距很大：
+
+```yaml
+# 低效（~45 tokens）
+description: |
+ This skill helps you review code changes in Rust projects.
+ It checks for common issues like unsafe code, error handling...
+ Use this when you want to ensure code quality before merging.
+
+# 高效（~9 tokens）
+description: Use for PR reviews with focus on correctness.
+```
+
+还有一个很重要的 disable-auto-invoke 使用策略：
+
+- 高频（>1 次/会话）→ 保持 auto-invoke，优化描述符
+- 低频（<1 次/会话）→ disable-auto-invoke，手动触发，描述符完全脱离上下文
+- 极低频（<1 次/月）→ 移除 Skill，改为 AGENTS.md 中的文档
+
+## Skills 反模式
+
+- 描述过短：description: help with backend（任何后端工作都能触发，哈哈）
+- 正文过长：几百行工作手册全塞进 SKILL.md 正文
+- 一个 Skill 覆盖 review、deploy、debug、docs、incident 五件事
+- 有副作用的 Skill 允许模型自动调用
+
+# 5\. 工具设计：怎么让 Claude 少选错
+
+我后面越用越觉得，给 Claude 的工具和给人写的 API 不是一回事。给人用的 API 往往会追求功能齐全，但给 agent 用，重点不是功能堆得多完整，而是让它更容易用对。
+
+## 好工具 vs 坏工具
+
+
+
+**几个实用设计原则**
+
+- 名称前缀按系统或资源分层：github\_pr\_\*、jira\_issue\_\*
+- 对大响应支持 response\_format: concise / detailed
+- 错误响应要教模型如何修正，不要只抛 opaque error code
+- 能合并成高层任务工具时，不要暴露过多底层碎片工具，避免 list\_all\_\* 让模型自行筛选
+
+## 从 Claude Code 内部工具演进学到的
+
+
+
+我看到 Claude Code 团队内部工具的这段演进时，感觉还挺有意思。像这种需要在任务中途停下来问用户的场景，他们前后试了三种做法：
+
+- **第一版**：给已有工具（如 Bash）加一个 question 参数，让 Claude 在调用工具时顺带提问。结果 Claude 大多数时候直接忽略这个参数，继续往下跑，根本不停下来问。
+- **第二版**：要求 Claude 在输出里写特定 markdown 格式，外层解析到这个格式就暂停。问题是没有强制约束，Claude 经常"忘了"按格式写，提问逻辑非常脆弱。
+- **第三版**：做成独立的 AskUserQuestion 工具。Claude 想提问就必须显式调用它，调用即暂停，没有歧义，比前两版靠谱多了。
+
+下面这张图刚好能解释，为什么第三版明显更稳：
+
+
+
+左边（markdown 自由输出）太松，模型格式随意、外层解析脆弱；右边（ExitPlanTool 参数）太死，等到退出计划阶段提问已经太晚；AskUserQuestion 独立工具落在中间，结构化且随时可调用，是这三者里最稳定的设计。
+
+说白了，既然你就是要 Claude 停下来问一句，那就直接给它一个专门的工具。加个 flag 或者约定一段输出格式，很多时候它一顺手就略过去了。
+
+**Todo 工具的演进**
+
+
+
+早期用 TodoWrite 工具 + 每 5 轮插入提醒让 Claude 记住任务。随着模型变强，这个工具反而成了限制，Todo 提醒让 Claude 认为必须严格遵循，无法灵活修改计划。挺有意思的教训：当初加这个工具是因为模型不够强，模型变强之后它反而变成了枷锁。值得过段时间回来检查一下，当初加的限制还成不成立。
+
+**搜索工具的演进**：最初用 RAG 向量数据库，虽然快但需要索引、不同环境脆弱，最重要的是 **Claude 不喜欢用**。改成 Grep 工具让 Claude 自己搜索后，好用很多。后来又发现一个顺带的好处：Claude 读 Skill 文件，Skill 文件又引用其他文件，模型会递归读取，按需发现信息，不需要提前塞进去，这个模式后来被叫做"渐进式披露"。
+
+**什么时候不该再加 Tool**
+
+- 本地 shell 可以可靠完成的事情
+- 模型只需要静态知识，不需要真正与外部交互
+- 需求更适合 Skill 的工作流约束，而不是 Tool 的动作能力
+- 还没验证过工具描述、schema 和返回格式能被模型稳定使用
+
+# 6\. Hooks：在 Claude 执行操作前后，强制插入你自己的逻辑
+
+Hooks 很容易被当成"自动运行的脚本"，但我自己用下来，觉得它更像是把一些不能交给 Claude 临场发挥的事情，重新收回到确定性的流程里。
+
+比如格式化要不要跑、保护文件能不能改、任务完成后要不要通知，这些事真不要指望 Claude 每次都自己记得。
+
+当前支持的 Hook 点
+
+
+
+## 适合 vs 不适合放到 Hooks 的
+
+**适合**：阻断修改受保护文件、Edit 后自动格式化/lint/轻量校验、SessionStart 后注入动态上下文（Git 分支、环境变量）、任务完成后推送通知。
+
+**不适合**：需要读大量上下文的复杂语义判断、长时间运行的业务流程、需要多步推理和权衡的决策，这些该在 Skill 或 Subagent 里。
+
+```json
+{
+ "hooks": {
+ "PostToolUse": [
+ {
+ "matcher": "Edit",
+ "pattern": "*.rs",
+ "hooks": [
+ {
+ "type": "command",
+ "command": "cargo check 2>&1 | head -30",
+ "statusMessage": "Running cargo check..."
+ }
+ ]
+ }
+ ],
+ "Notification": [
+ {
+ "type": "command",
+ "command": "osascript -e 'display notification \"Task completed\" with title \"Claude Code\"'"
+ }
+ ]
+ }
+}
+```
+
+## Hooks：越早发现错误，越省时间
+
+
+
+在 100 次编辑的会话中，每次节省 30-60 秒，累积节省 1-2 小时，还挺可观的。**注意限制输出长度**（| head -30），避免 Hook 输出反而污染上下文。如果不想在每条命令后面手动加截断，可以看看第 3 节提到的 RTK，它把这件事系统化了。
+
+Hooks + Skills + CLAUDE.md 三层叠加
+
+- CLAUDE.md：声明"提交前必须通过测试和 lint"
+- Skill：告诉 Claude 在什么顺序下运行测试、如何看失败、如何修复
+- Hook：对关键路径执行硬性校验，必要时阻断
+
+用下来感觉，三样少任何一层都会有漏洞。只写 CLAUDE.md 规则，Claude 经常当没看见；只靠 Hooks，细节判断又做不了，放在一起才比较稳。
+
+# 7\. Subagents：派一个独立的 Claude 去干一件具体的事
+
+Subagent 就是从主对话派出去的一个独立 Claude 实例，有自己的上下文窗口，只用你指定的工具，干完汇报结果。我用下来觉得它最大的价值不是"并行"，而是隔离，扫代码库、跑测试、做审查这类会产生大量输出的事，塞进主线程很快就把有效上下文挤没了，交给 Subagent 做，主线程只拿一个摘要，干净很多。
+
+Claude Code 内置了三个：**Explore**（只读扫库，默认跑 Haiku 省成本）、**Plan**（规划调研）、**General-purpose**（通用），也可以自定义。
+
+## 配置时要显式约束
+
+- tools / disallowedTools：限定能用什么工具，别给和主线程一样宽的权限
+- model：探索任务用 Haiku/Sonnet，重要审查用 Opus
+- maxTurns：防止跑飞
+- isolation: worktree：需要动文件时隔离文件系统
+
+另一个实用细节：长时间运行的 bash 命令可以按 Ctrl+B 移到后台，Claude 之后会用 BashOutput 工具查看结果，不会阻塞主线程继续工作。subagent 同理，直接告诉它「在后台跑」就行。
+
+## 几个常见反模式
+
+- 子代理权限和主线程一样宽，隔离没有意义
+- 输出格式不固定，主线程拿到没法用
+- 子任务之间强依赖，频繁要共享中间状态，这种情况用 Subagent 不合适
+
+# 8\. Prompt Caching：Claude Code 内部架构的核心
+
+这块我之前在很多教程里都没怎么看到有人展开讲，但它其实很影响 Claude Code 的成本结构和很多设计取舍。
+
+工程界有句话 "Cache Rules Everything Around Me"，对 agent 同样如此，Claude Code 的整个架构都是围绕 Prompt 缓存构建的，**高命中率不光省钱，速率限制也会松很多**，Anthropic 甚至会对命中率跑告警，太低直接宣布 SEV。
+
+## 为缓存设计的 Prompt Layout
+
+
+
+Prompt 缓存是按**前缀匹配**工作的，从请求开头到每个 cache\_control 断点之前的内容都会被缓存。所以这里的顺序很重要：
+
+```markdown
+Claude Code 的 Prompt 顺序：
+1. System Prompt → 静态，锁定
+2. Tool Definitions → 静态，锁定
+3. Chat History → 动态，在后面
+4. 当前用户输入 → 最后
+```
+
+**破坏缓存的常见陷阱**
+
+- 在静态系统 Prompt 中放入带时间戳的内容（让它每次都变）
+- 非确定性地打乱工具定义顺序
+- 会话中途增删工具
+
+那像当前时间这种动态信息怎么办？别去动系统 Prompt，放到下一条消息里传进去就行。Claude Code 自己也是这么做的，用户消息里加 标签，系统 Prompt 不动，缓存也就不会被打坏。
+
+## 会话中途不要切换模型
+
+Prompt 缓存是模型唯一的。假如你已经和 Opus 对话了 100K tokens，想问个简单问题，**切换到 Haiku 实际上比继续用 Opus 更贵**，因为要为 Haiku 重建整个缓存。确实需要切换的话，用 Subagent 交接：Opus 准备一条"交接消息"给另一个模型，说明需要完成的任务就行。
+
+**Compaction 的实际实现**
+
+
+
+上图是 Compaction（上下文压缩）的执行流程：左边是上下文快满时的状态，中间是 Claude Code 开一个 fork 调用，把完整对话历史喂给模型，加一句"Summarize this conversation"，这一步命中缓存所以只需 1/10 的价格，右边是压缩完之后，原来几十轮对话被替换成一段 ~20k tokens 的摘要，System + Tools 还在，再挂上之前用到的文件引用，腾出空间继续新的轮次。
+
+直觉上 Plan Mode 应该切换成只读工具集，但这会破坏缓存。实际实现是：EnterPlanMode 是模型可以自己调用的工具，检测到复杂问题时自主进入 plan mode，工具集不变，缓存不受影响。
+
+**defer\_loading：工具的延迟加载**
+
+Claude Code 有数十个 MCP 工具，每次请求全量包含会很贵，但中途移除会破坏缓存。解决方案是发送轻量级 stub，只有工具名，标记 defer\_loading: true。模型通过 ToolSearch 工具"发现"它们，完整的工具 schema 只在模型选择后才加载，这样缓存前缀保持稳定。
+
+# 9\. 验证闭环：没有 Verifier 就没有工程上的 Agent
+
+「Claude 说完成了」其实没啥用，你得能知道它做没做对、出了问题能退回来、过程还能查，这才算数。
+
+## Verifier 的层级
+
+- 最低层：命令退出码、lint、typecheck、unit test
+- 中间层：集成测试、截图对比、contract test、smoke test
+- 更高层：生产日志验证、监控指标、人工审查清单
+
+在 Prompt、Skill 和 CLAUDE.md 中显式定义验证
+
+```markdown
+## Verification
+
+For backend changes:
+
+- Run \`make test\` and \`make lint\`
+- For API changes, update contract tests under \`tests/contracts/\`
+
+For UI changes:
+
+- Capture before/after screenshots if visual
+
+Definition of done:
+
+- All tests pass
+- Lint passes
+- No TODO left behind unless explicitly tracked
+```
+
+写任务 Prompt 或 Skill 的时候，最好把验收标准提前说清楚。哪些命令跑完算完成，失败了先查什么，截图和日志看到什么才算过，这些越早讲明白，后面越省事。
+
+我自己有个很简单的判断：假如一个任务你都说不清楚「Claude 怎么才算做对了」，那它大概率也不适合直接丢给 Claude 自动完成。
+
+# 10\. 高频命令的工程意义
+
+这些命令说白了就干一件事：主动管理上下文，别等系统自己处理。
+
+## 上下文管理
+
+```bash
+/context # 查看 token 占用结构，排查 MCP 和文件读取占比
+/clear # 清空会话，同一问题被纠偏两次以上就重来
+/compact # 压缩但保留重点，配合 Compact Instructions
+/memory # 确认哪些 CLAUDE.md 真的被加载了
+```
+
+## 能力与治理
+
+
+
+```bash
+/mcp # 管理 MCP 连接，检查 token 成本，断开闲置 server
+/hooks # 管理 hooks，控制平面入口
+/permissions # 查看或更新权限白名单
+/sandbox # 配置沙箱隔离，高自动化场景必备
+/model # 切换模型：Opus 用于深度推理，Sonnet 用于常规，Haiku 用于快速探索
+```
+
+## 会话连续性与并行
+
+```bash
+claude --continue # 恢复当前目录最近会话，隔天接着做
+claude --resume # 打开选择器恢复历史会话
+claude --continue --fork # 从已有会话分叉，同一起点不同方案
+claude --worktree # 创建隔离 git worktree
+claude -p "prompt" # 非交互模式，接入 CI / pre-commit / 脚本
+claude -p --output-format json # 结构化输出，便于脚本消费
+```
+
+## 几个不常见但很好用的命令
+
+**/simplify**：对刚改完的代码做三维检查，代码复用、质量和效率，发现问题直接修掉。特别适合改完一段逻辑后立刻跑一遍，代替手动 review。
+
+**/rewind**：不是"撤销"，而是回到某个会话 checkpoint 重新总结。适合：Claude 已沿错误路径探索太久；想保留前半段共识但丢掉后半段失败。
+
+**/btw**：在不打断主任务的前提下快速问一个侧问题，适合"两个命令有什么区别"这类单轮旁路问答，不适合需要读仓库或调用工具的问题。
+
+**claude -p --output-format stream-json**：实时 JSON 事件流，适合长任务监控、增量处理、流式集成到自己的工具。
+
+**/insight**：让 Claude 分析当前会话，提炼出哪些内容值得沉淀到 CLAUDE.md。用法是会话做了一段之后跑一次，它会指出"这个约定你们反复提到，但没有写进契约"之类的盲点，是迭代优化 CLAUDE.md 的好手段。
+
+**双击 ESC 回溯**：按两次 ESC 可以回到上一条输入重新编辑，不用重新手打。Claude 走偏了、或者上一句话没说清楚，双击 ESC 修改后重发，比重新开会话省事得多。
+
+**对话历史都在本地**：所有会话记录存放在 ~/.claude/projects/ 下，文件夹名按项目路径命名（斜杠变横杠），每个会话是一个 .jsonl 文件。想找某个话题的历史，直接 grep -rl "关键词" ~/.claude/projects/ 就能定位，或者直接告诉 Claude「帮我搜一下之前关于 X 的讨论」，它会自己去翻。
+
+# 11\. 如何写一个好的 CLAUDE.md
+
+CLAUDE.md 在我看来更像是你和 Claude 之间的协作契约，不是团队文档，也不是知识库，里面只放那些每次会话都得成立的事。
+
+我自己的建议其实很简单，一开始甚至可以什么都不写。先用起来，等你发现自己老是在重复同一件事，再把它补进去。加法也不复杂，输入 # 可以把当前对话里的内容直接追加进 CLAUDE.md，或者直接告诉 Claude「把这条加到项目的 CLAUDE.md 里」，它会知道该改哪个文件。
+
+
+
+## 应该放什么
+
+- 怎么 build、怎么 test、怎么跑（最核心）
+- 关键目录结构与模块边界
+- 代码风格和命名约束
+- 那些不明显的环境坑
+- 绝对不能干的事（NEVER 列表）
+- 压缩时必须保留的信息（Compact Instructions）
+
+## 不该放什么
+
+- 大段背景介绍
+- 完整 API 文档
+- 空泛原则，如"写高质量代码"
+- Claude 通过读仓库即可推断的显然信息
+- 大量背景资料和低频任务知识（这些放到 Skills）
+
+## 高质量模板
+
+```markdown
+# Project Contract
+
+## Build And Test
+
+- Install: \`pnpm install\`
+- Dev: \`pnpm dev\`
+- Test: \`pnpm test\`
+- Typecheck: \`pnpm typecheck\`
+- Lint: \`pnpm lint\`
+
+## Architecture Boundaries
+
+- HTTP handlers live in \`src/http/handlers/\`
+- Domain logic lives in \`src/domain/\`
+- Do not put persistence logic in handlers
+- Shared types live in \`src/contracts/\`
+
+## Coding Conventions
+
+- Prefer pure functions in domain layer
+- Do not introduce new global state without explicit justification
+- Reuse existing error types from \`src/errors/\`
+
+## Safety Rails
+
+## NEVER
+
+- Modify \`.env\`, lockfiles, or CI secrets without explicit approval
+- Remove feature flags without searching all call sites
+- Commit without running tests
+
+## ALWAYS
+
+- Show diff before committing
+- Update CHANGELOG for user-facing changes
+
+## Verification
+
+- Backend changes: \`make test\` + \`make lint\`
+- API changes: update contract tests under \`tests/contracts/\`
+- UI changes: capture before/after screenshots
+
+## Compact Instructions
+
+Preserve:
+
+1. Architecture decisions (NEVER summarize)
+2. Modified files and key changes
+3. Current verification status (pass/fail commands)
+4. Open risks, TODOs, rollback notes
+```
+
+用起来其实不复杂：每次都要知道的放 CLAUDE.md，只对部分文件生效的放 rules，只在某类任务中需要的放 Skills。
+
+## 让 Claude 维护自己的 CLAUDE.md
+
+我最喜欢的一个技巧：每次纠正 Claude 的错误后，让它自己更新 CLAUDE.md：
+
+> "Update your CLAUDE.md so you don't make that mistake again."
+
+Claude 在给自己补这类规则时其实还挺好用，用久了确实越来越少犯同样的错。不过也要定期 review，时间一长总会有些条目慢慢过时，当初有用的限制现在未必还适合，这件事后面第 14 节有个更系统的做法。
+
+# 12\. 最近自己折腾中得到的新经验
+
+春节放假时，我用 Claude Code 做了一个开源 terminal 项目 [Kaku](https://github.com/tw93/Kaku)，底层是 Rust + Lua，也带了一些 AI 能力。混合语言加上自定义配置系统，实际折腾下来反而暴露出不少典型的 agent 协作问题，顺手聊几个对我帮助比较大的经验。
+
+## 环境透明比你想象中重要
+
+Claude Code 调用的都是真实的 shell、git、package manager 和本地配置。这里面只要有一层不透明，它就只能开始猜，一猜可靠性就掉。这不是 Claude Code 特有的问题，很多 agent 都一样。
+
+所以我后来很快就在 terminal 里加了个 doctor 命令，把环境状态、依赖和配置情况先统一收上来，输出一份结构化的健康报告。Claude Code 开始做事前先跑一次 doctor，确实能省掉很多"环境没搞清楚就开干"的问题。
+
+另外我还发现，假如 CLI 本身就有 init、config、reset 这类语义清楚的子命令，Claude Code 用起来会稳不少，比让它自己去猜配置文件怎么摆要靠谱。先把状态收敛住，再暴露编辑入口，顺序一反过来就很容易乱。
+
+## 混合语言项目的 Hooks 实践
+
+两套语言、两套检查，其实挺适合用 Hooks 按文件类型分别触发：
+
+```json
+{
+ "hooks": {
+ "PostToolUse": [
+ {
+ "matcher": "Edit",
+ "pattern": "*.rs",
+ "hooks": [{
+ "type": "command",
+ "command": "cargo check 2>&1 | head -30",
+ "statusMessage": "Checking Rust..."
+ }]
+ },
+ {
+ "matcher": "Edit",
+ "pattern": "*.lua",
+ "hooks": [{
+ "type": "command",
+ "command": "luajit -b $FILE /dev/null 2>&1 | head -10",
+ "statusMessage": "Checking Lua syntax..."
+ }]
+ }
+ ]
+ }
+}
+```
+
+每次编辑完立刻知道有没有编译错误，比"跑了一堆才发现最开始就挂了"舒服得多。
+
+## 完整的工程化布局参考
+
+假如有同学想给自己项目配一套比较完整的 Claude Code 工程布局，可以参考这个结构，不用全做，按需裁剪：
+
+```plaintext
+Project/
+├── CLAUDE.md
+├── .claude/
+│ ├── rules/
+│ │ ├── core.md
+│ │ ├── config.md
+│ │ └── release.md
+│ ├── skills/
+│ │ ├── runtime-diagnosis/ # 统一收集日志、状态和依赖
+│ │ ├── config-migration/ # 配置迁移回滚防污
+│ │ ├── release-check/ # 发布前校验、smoke test
+│ │ └── incident-triage/ # 线上故障分诊
+│ ├── agents/
+│ │ ├── reviewer.md
+│ │ └── explorer.md
+│ └── settings.json
+└── docs/
+ └── ai/
+ ├── architecture.md
+ └── release-runbook.md
+```
+
+全局约束（CLAUDE.md）、路径约束（rules）、工作流（skills）和架构细节各归各位，Claude Code 跑起来会稳很多。假如你同时维护多个项目，可以把稳定的个人基线放在 ~/.claude/，各项目的差异放在项目级 .claude/，通过同步脚本分发，不同项目之间就不会互相污染了。
+
+# 13\. 常见反模式
+
+
+
+# 14\. 配置健康检查
+
+基于文章里的六层框架，我把这套检查整理成了一个开源 Skill 项目 [tw93/claude-health](https://github.com/tw93/claude-health)，可以一键检查你的 Claude Code 配置现在处于什么状态。
+
+> npx skills add tw93/claude-health -a claude-code -s health -g -y
+
+装好之后在任意会话里跑 /health，它会自动识别项目复杂度，对 CLAUDE.md、rules、skills、hooks、allowedTools 和实际行为模式各跑一遍检查，输出一份优先级报告：需要立刻修 / 结构性问题 / 可以慢慢做。
+
+如果你读完这篇文章想知道自己的配置离这些原则差多远，跑一次 /health 是最快的方式。
+
+# 15\. 结语
+
+用 Claude Code 大概会经历三个阶段：
+
+
+
+到了第三阶段，关注点会悄悄变掉，从「这个功能怎么用」变成「怎么让 agent 在约束下自己跑起来」，两件事感觉差很多。
+
+有一个问题挺值得想的：假如一个任务你说不清楚「什么叫做完」，那大概率也不适合直接扔给 Claude 自主完成，验证标准本身都没有，Claude 再聪明也跑不出正确答案。
+
+这些是半年折腾下来的一些总结，肯定还有很多没有挖掘到的地方，如果大伙有用得更 6 的技巧，欢迎告诉我。
\ No newline at end of file
diff --git a/Claude/我用 Claude 写了一个 DBA 运维 Agent：从零到生产的完整记录.md b/Claude/我用 Claude 写了一个 DBA 运维 Agent：从零到生产的完整记录.md
new file mode 100644
index 0000000..242218c
--- /dev/null
+++ b/Claude/我用 Claude 写了一个 DBA 运维 Agent：从零到生产的完整记录.md
@@ -0,0 +1,149 @@
+---
+title: "我用 Claude 写了一个 DBA 运维 Agent：从零到生产的完整记录"
+source: "https://x.com/plantegg/status/2028677544791138751"
+author:
+ - "[[Unknown]]"
+published: 2026-03-03
+created: 2026-03-03
+description:
+tags:
+ - "clippings"
+---
+作为一个 MySQL DBA，我花了一个下午，用 Claude Code 从零搭建了一个能自主诊断 MySQL 和 Linux 的 AI Agent。它能自己决定该查什么、怎么查，最后给出一份完整的诊断报告。
+
+**题图是 agent 和 LLM 交互的完整过程，可以看到什么时候执行工具什么时候组合成完整 prompt 发给 LLM**
+
+这篇文章记录整个开发过程，以及我对 Agent 开发的理解。
+
+## 什么是 Agent？
+
+一句话：**Agent = 让大模型持续思考、持续调用外部工具、直到解决用户问题的程序。**
+
+和普通的 ChatGPT 对话不同，Agent 不只是"聊天"，它能动手。
+
+普通 LLM 调用：你问一个问题，模型从训练数据里找答案，一问一答就结束了。
+
+Agent：模型分析你的问题后，主动决定调用什么工具（比如 SSH 到服务器执行命令、查 MySQL），拿到真实数据后继续分析，不够就再调，直到能给出完整答案。
+
+核心区别就一个：**模型有了"手脚"，能主动获取实时信息。**
+
+## ReAct：Agent 背后的核心原理
+
+几乎所有主流 Agent 框架都基于 ReAct 模式，来自 2022 年的论文 "ReAct: Synergizing Reasoning and Acting in Language Models"。
+
+ReAct = Reasoning（推理）+ Acting（行动）
+
+它的工作循环非常直觉：
+
+**Thought（思考）→ Action（行动）→ Observation（观察）→ 再 Thought ...**
+
+翻译成人话就是：
+
+- 思考："用户问的是主从复制状态，我应该先查 SHOW SLAVE STATUS"
+- 行动：调用 mysql\_query 工具执行这条 SQL
+- 观察：工具返回"无结果集"——说明这台不是从库
+- 再思考："那看看是不是主库吧"
+- 再行动：执行 SHOW MASTER STATUS
+- 再观察：有 binlog 信息，确认是主库
+- ......
+- 最终思考："信息够了，可以给出完整诊断报告"
+
+关键在于：**你不需要写 if-else 来编排这些步骤。** 模型自己决定调什么工具、调几次、什么时候停止。你只需要提供工具，剩下的交给模型推理。
+
+## Agent 开发三要素
+
+**Agent = LLM + Tools + Prompt**
+
+**LLM（大脑）**：选什么模型、怎么连。我用的是 Claude Sonnet 4.5，通过公司内部的 OpenAI 兼容网关调用。
+
+**Tools（手脚）**：写 Python 函数让模型能调用。这是 80% 的工作量。我写了三个工具：ssh\_command（远程执行 Linux 命令）、mysql\_query（执行只读 SQL）、write\_file（保存报告到文件）。
+
+**Prompt（指令）**：告诉模型它是谁、能做什么。比如"你是资深 DBA，精通 MySQL 5.7/8.0，所有操作只读，用中文回答"。
+
+## 六步开发流程
+
+**Step 1：定义场景和工具边界**
+
+先想清楚三个问题：
+
+- Agent 解决什么问题？→ MySQL/Linux 远程只读诊断
+- 需要哪些工具？→ SSH 命令、MySQL 查询、写文件
+- 安全红线是什么？→ 绝对只读，拦截所有写操作
+
+**Step 2：搭项目骨架**
+
+用 CrewAI 框架，项目结构很简单：config.yaml（配置）、[main.py](https://main.py/)（入口）、[crew.py](https://crew.py/)（Agent 定义）、tools/ 目录下放各个工具。
+
+**Step 3：写工具（核心）**
+
+每个工具就是一个 Python 类，有一个 name、一个 description（模型靠它决定什么时候调用）、一个 \_run 方法（实际执行逻辑）。
+
+工具开发的四个要点：
+
+- description 要写清楚——模型完全靠这段描述来决定什么时候用这个工具
+- \_run 返回纯文本——模型只能理解文本
+- 做好安全拦截——模型可能会尝试执行 DROP TABLE
+- 加超时控制——防止 tail -f 这类命令卡死整个 Agent
+
+**Step 4：配置 LLM**
+
+配好模型名称、API 地址、密钥。如果用的是 OpenAI 兼容网关，模型名需要加 "openai/" 前缀让路由层正确转发。
+
+**Step 5：组装 Agent**
+
+把 LLM、工具列表、角色 Prompt 组合到一起，创建 Agent 实例。
+
+**Step 6：运行**
+
+把用户的问题包装成 Task，交给 Crew 执行，拿到结果。
+
+## 实际效果
+
+我对 Agent 说："检查这台机器的主从复制状态"
+
+它自己完成了以下操作：
+
+1. 执行 SHOW SLAVE STATUS → 发现不是从库
+2. 执行 SHOW MASTER STATUS → 确认是主库，拿到 binlog 和 GTID 信息
+3. 执行 SHOW SLAVE HOSTS → 发现没有从库连接
+4. 查询半同步复制变量 → 发现半同步已降级为异步
+5. SSH 查看 MySQL 错误日志 → 找到超时切换的时间点
+6. 查询复制用户权限 → 确认配置完整
+7. 查看 processlist → 确认没有 Binlog Dump 线程
+
+最终输出了一份完整的诊断报告，准确指出：主库配置正常但当前无从库连接，半同步复制因超时已降级，存在数据丢失风险，并给出了紧急处理建议。
+
+整个过程 6 轮 LLM 调用，全自动，无需人工干预。
+
+## 踩过的坑
+
+真实开发中踩了不少坑，记录几个典型的：
+
+**MySQL root 不允许 TCP 连接**：root 账户只允许 unix socket 登录，SSH 隧道转发的 127.0.0.1 TCP 连接被拒。解决方案：放弃 pymysql 直连，改为 SSH 到目标机器后用 mysql CLI 通过 socket 执行。
+
+**模型后端不支持 assistant prefill**：公司网关后面的某些模型走 AWS Bedrock，而 Bedrock 不支持 CrewAI 使用的 assistant message prefill 特性。解决方案：逐个测试网关上的模型，找到走非 Bedrock 后端的模型。
+
+**tail -f 卡死 Agent**：模型执行 "tail -100 slow-queries.log" 时，如果日志行数不够，tail 会等待新数据写入，导致 SSH 命令永远不返回。解决方案：用 paramiko channel 级别的非阻塞读取 + deadline 超时机制替代 exec\_command。
+
+**调试回调被框架覆盖**：我写了一个 --debug 模式输出每次 LLM 交互的完整 prompt/response，但 CrewAI 内部会重置 litellm 的回调列表。解决方案：monkey-patch 框架的 set\_callbacks 方法，确保自定义 logger 始终存在。
+
+## 一个公式
+
+**Agent 效果 = 模型能力 x 工具质量 x Prompt 清晰度**
+
+- 模型不够强 → 换更大的模型
+- 工具返回乱码 → 格式化输出，让模型能理解
+- 模型不知道用哪个工具 → 改工具的 description
+- 模型反复犯错重试 → 加 --debug 看完整 prompt，调整角色描述
+
+## 总结
+
+Agent 开发没有想象中那么难。本质上就是：
+
+1. 给大模型一组工具
+2. 告诉它是什么角色
+3. 让它自己决定怎么用工具解决问题
+
+80% 的工作量在写好工具——安全拦截、超时控制、输出格式化。剩下 20% 是选模型和调 Prompt。
+
+框架（CrewAI/LangChain/AutoGen）只是脚手架，真正决定 Agent 好不好用的是你的工具质量和对业务场景的理解。
\ No newline at end of file
diff --git a/Claude/用了 3 个月 Claude Code，这 9 条最佳实践让我少走了无数弯路.md b/Claude/用了 3 个月 Claude Code，这 9 条最佳实践让我少走了无数弯路.md
new file mode 100644
index 0000000..135e8b8
--- /dev/null
+++ b/Claude/用了 3 个月 Claude Code，这 9 条最佳实践让我少走了无数弯路.md
@@ -0,0 +1,294 @@
+---
+title: "用了 3 个月 Claude Code，这 9 条最佳实践让我少走了无数弯路"
+source: "https://x.com/yanhua1010/status/2028099767968903629"
+author:
+ - "[[Unknown]]"
+published: 2026-03-01
+created: 2026-03-01
+description:
+tags:
+ - "clippings"
+---
+用了 3 个月 Claude Code，我踩过的最大坑不是提示词写得差，不是模型选错了，是一个大多数人根本没意识到的东西：上下文窗口。
+
+它就像房间里的大象。你的 Claude 越来越笨，你以为是模型问题，其实是你把白板写满了。
+
+这篇文章，我会把自己从"上下文小白"到"上下文管理高手"的完整经验分享出来。每一条都是实战验证过的。
+
+如果你在用 Claude Code，这篇文章可能帮你省掉几十个小时的无效返工。
+
+好，我们发车了。
+
+## 先搞懂一件事：上下文窗口到底是什么
+
+Claude 的大脑有一块白板。
+
+你发的每条消息、Claude 读的每个文件、执行的每条命令，全都会写在这块白板上。
+
+白板满了，Claude 的表现就开始下滑。它会忘记你一小时前的指令。会犯一些本不该犯的错误。会开始重复自己的话。
+
+这就是为什么你有时候觉得"Claude 变笨了"。它没变笨，它只是记不住了。
+
+Claude Code 创建者 Boris Cherny 说过一句话：
+
+> 想用好 Claude Code，关键就是管理好这块白板。
+
+理解了这一点，后面所有的策略才讲得通。
+
+
+
+## 策略一：别让 Claude 一上来就写代码
+
+这是几乎所有新手踩的第一个坑。
+
+你描述一个需求，Claude 立刻开始写代码。十五分钟后你发现，它解决的根本不是你想要的问题。
+
+更糟的是，这十五分钟的代码生成已经吃掉了大量上下文空间。
+
+**正确做法：先切计划模式。**
+
+Claude Code 有一个 Plan Mode，按 Shift+Tab 就能切换。
+
+在这个模式下，Claude 只看文件、理清思路，不动任何代码。
+
+我现在的标准流程是这样的：
+
+1️⃣ 切到计划模式，让 Claude 阅读相关文件，理清关联
+
+2️⃣ 让 Claude 写出完整计划：改哪些文件？步骤顺序？哪里可能出错？
+
+3️⃣ 我亲自审查计划，不对就改
+
+4️⃣ 切回正常模式，基于计划开发
+
+5️⃣ 让 Claude 用清晰的提交信息提交代码
+
+多花十分钟做计划，能帮你避免无数次返工。
+
+Boris 团队里甚至有人用一个 Claude 写计划，另一个 Claude 以高级工程师身份审查计划。计划这一步，他们是认真的。
+
+
+
+## 策略二：用子代理保护你的主战场
+
+回到白板的比喻。
+
+Claude 调查一个问题时会读大量文件。日志、配置、代码，一通读下来，白板很快就半满了。等它研究完准备写代码时，空间已经不够用了。
+
+子代理（Subagent）完美解决了这个问题。
+
+子代理是独立的 Claude 实例，在自己的上下文窗口里干活。它把结果汇报回来，但不占用你主会话的白板空间。
+
+用法极其简单，在任何调研任务后面加一句 "use subagents" 就行：
+
+> "用子代理帮我查查支付流程是怎么处理失败交易的。"
+
+子代理读完所有数据，你的主会话保持清爽。收到报告后你还有大量空间继续开发。
+
+**这是我目前使用频率最高的技巧，没有之一。**
+
+作为 Java 后端开发，我经常需要排查复杂的分布式系统问题。以前一个排查任务就能把整个会话搞废。现在我把所有"读日志、查源码、分析调用链"的活儿全部扔给子代理，主会话只做决策和写代码。
+
+效率差距巨大。
+
+
+
+## 策略三：CLAUDE.md 是你的"活规则书"
+
+如果你每天都在用 Claude Code，但还没有认真维护 [CLAUDE.md](https://claude.md/)，你在浪费大量效率。
+
+[CLAUDE.md](https://claude.md/) 是 Claude 每次启动时都会读的文件。你写什么规则，Claude 就按什么方式跟你工作。
+
+想想你经常重复说的话：
+
+- "用 ES modules，不用 CommonJS"
+- "测试不要用 mocks"
+- "代码改完后跑一次 linter"
+- "分支命名格式是 feature/工单编号"
+
+没有 [CLAUDE.md](https://claude.md/)，每次都得重新说。有了 [CLAUDE.md](https://claude.md/)，说一次就够。
+
+**但最关键的技巧是这个：**
+
+每次 Claude 犯了错，你纠正完以后，最后加一句：
+
+> "更新 [CLAUDE.md](https://claude.md/) 以免再犯这个错误。"
+
+Claude 会把教训写进自己的规则。时间久了，CLAUDE.md 会变成一份专门为你优化的"活文档"。
+
+我的 [CLAUDE.md](https://claude.md/) 里有一条规则就是这样来的：我发现 Claude 老是在 Obsidian Vault 里创建 .txt 文件（Obsidian 根本不显示），纠正了一次之后加了一条"Vault 内只创建 .md 文件"的规则。从此再也没犯过。
+
+⚠️ 但要注意一点：[CLAUDE.md](https://claude.md/) 不能太长。
+
+如果写到 500 行，Claude 的注意力会被分散，有些规则就被忽视了。每条规则都应该回答一个问题：这条没了，Claude 会犯错吗？如果不会，删掉。
+
+
+
+## 策略四：给 Claude 自检的机会
+
+大多数人的用法是：描述需求 → 祈祷 Claude 写对 → 自己逐行检查。
+
+这样你就成了唯一的质检员，信我，很累。
+
+**更好的做法：在提示词里内置验证标准。**
+
+举个例子。你要写一个邮箱验证函数，不要只说"写一个邮箱验证函数"。
+
+换成这样说：
+
+> "写一个函数判断邮箱是否有效。用这些案例测试：hello@gmail.com 应该通过，hello@ 应该失败，[@domain](https://x.com/@domain).com 应该失败。写完后执行测试。"
+
+Claude 就能自我检验了，不用你逐行盯着看。
+
+视觉类任务也一样。改按钮设计？粘贴截图并说："让它看起来像这样，改完后截图给我，告诉我有什么不同。"
+
+给 Claude 一个对比标准，避免你成为唯一反馈环节。这一招能帮你节省几个小时。
+
+## 策略五：写提示词，要像写技术文档一样具体
+
+Claude 能从上下文推断很多东西，但它读不懂你的心思。
+
+看对比：
+
+❌ 模糊："给 [auth.py](https://auth.py/) 加测试"
+
+✅ 具体："写 [auth.py](https://auth.py/) 的测试，覆盖用户请求中途会话过期的情况。不允许用 mocks。重点测试令牌看似有效但实际过期的边缘情况。"
+
+任务一样，结果截然不同。
+
+你也可以直接指向 Claude 应该看的位置。
+
+❌ "为什么这个函数表现怪怪的？"
+
+✅ "看这个文件的 git 历史，找出这个行为是什么时候加进来的，为什么会有。"
+
+区别就是：Claude 是在胡乱猜测，还是在给你具体答案。
+
+作为后端开发，我现在写提示词的习惯是：明确输入、明确输出、明确约束、明确验证方式。把它当成写接口文档。
+
+## 策略六：每 30-45 分钟重置你的上下文
+
+你跟 Claude 交谈久了，会发现它开始跑偏。
+
+反复啰嗦。忘了你早期给的规则。回答质量明显下降。
+
+这就是上下文污染。白板写满了，Claude 只能覆盖旧内容。
+
+**Boris 的团队在复杂任务中每 30-45 分钟就做一次上下文重置。**
+
+操作方法：
+
+1️⃣ 让 Claude 总结当前进度："总结我们目前做了什么，还剩哪些任务，当前有哪些问题。"
+
+2️⃣ 开一个新会话
+
+3️⃣ 粘贴总结，继续做
+
+这能保持 Claude 思路清晰，避免长时间会话带来的"脑雾"。
+
+很多人舍不得关会话，觉得"上下文都在里面"。实际上，一个臃肿的上下文还不如一份精炼的总结。
+
+## 策略七：并行会话 + Git Worktree
+
+这是 Boris 团队公认的生产力杀手锏。
+
+你可以同时开启多条 Claude Code 会话，配合 git worktree（代码库的多个独立工作副本），互不干扰。
+
+举个例子：
+
+🅰️ 会话 A 负责写新功能
+
+🅱️ 会话 B 负责审查 A 写的代码
+
+把 A 的产出给 B，让它找边缘问题和缺陷。然后把反馈带回 A。
+
+一个 Claude 写，一个 Claude 审。代码质量更高，效率更快。
+
+有人同时开 3-5 个会话，给 worktree 起名并设置 shell 别名（za, zb, zc），一键切换。
+
+你也能用同样的方法搞测试驱动开发：一个 Claude 先写测试，另一个写通过测试的代码。TDD，但 Claude 做了所有重活。
+
+
+
+## 策略八：把重复操作封装成 Skill
+
+如果你每天在 Claude Code 里重复做一件事超过两次，就该把它变成 Skill。
+
+Skill 本质是保存好的工作流程。你写好步骤，取个名字，以后只需要调用名字。
+
+我自己封装了十几个 Skill：封面图生成、文章发布、图片压缩、网页转 Markdown......
+
+每一个都是从"每次手动说一遍完整提示词"进化成"一条命令搞定"的。
+
+Boris 的规则值得借鉴：**如果每天做两次以上，就封装成 Skill。**
+
+Boris 团队给 BigQuery 搭了一个 Skill，团队成员直接在 Claude Code 里做数据分析查询，连 SQL 都不用写。一个 Skill，贯穿所有项目复用。
+
+## 策略九：给真实数据，而不是你的猜测
+
+传统的 Bug 修复流程是这样的：
+
+你用语言描述 Bug → Claude 猜错几次 → 改几次 → 最终修好
+
+这中间浪费的上下文空间是巨大的。
+
+**更快的做法：给 Claude 真实错误信息，然后走开。**
+
+Boris 团队接入了 Slack MCP。当 Slack 上报 Bug，他们把对话直接贴给 Claude，然后扔一个字："fix"。
+
+没有多余解释，没有手把手辅导。Claude 自己看对话、找问题、修复。
+
+或者说"去修那个失败的 CI 测试"，然后走开。没告诉 Claude 哪个测试，没说明为什么失败，Claude 照样搞定。
+
+关键是：给 Claude 的是真实数据（Slack 线程、错误日志、Docker 输出），不是你对问题的猜测。
+
+Claude 非常擅长读分布式系统日志，精准追踪故障点。作为后端开发，这一点让我非常惊喜。
+
+## 把这些串起来：我的日常工作流
+
+分享一下我现在的典型工作流程，把上面的策略串联起来：
+
+**开始任务前：**
+
+- 检查 [CLAUDE.md](https://claude.md/) 是否需要更新
+- 明确任务复杂度，决定是否需要计划模式
+
+**任务执行中：**
+
+- 复杂调研 → 扔给子代理
+- 写代码 → 主会话 + 具体提示词 + 内置验证
+- 代码审查 → 开第二个会话
+
+**每 30-45 分钟：**
+
+- 让 Claude 总结进度
+- 评估是否需要上下文重置
+
+**任务完成后：**
+
+- Claude 犯过的错 → 更新 [CLAUDE.md](https://claude.md/)
+- 重复操作 → 封装成 Skill
+
+这套流程让我的效率至少提升了 3 倍。不是因为 Claude 变强了，而是因为我学会了管理它的"大脑"。
+
+
+
+## 最后的话
+
+回到文章开头的那个比喻。
+
+上下文窗口就是 Claude 的白板。白板的大小是固定的，但你可以决定在上面写什么、什么时候擦掉重来。
+
+管理上下文，本质上就是在管理 AI 的注意力。
+
+这是一项新技能。它跟编程能力无关，跟领域知识无关，但它决定了你能从 AI 身上获得多少价值。
+
+越早掌握，越早受益。
+
+你踩过最大的上下文窗口的坑是什么？评论区聊聊 👇
+
+参考来源：
+
+- Anthropic 官方最佳实践文档：[https://code.claude.com/docs/en/best-practices](https://code.claude.com/docs/en/best-practices)
+- Boris Cherny（Claude Code 创建者）分享：[https://x.com/bcherny](https://x.com/bcherny)
+- [@Meer\_AIIT](https://x.com/@Meer_AIIT) 的 Claude Code 最佳实践解析
\ No newline at end of file
diff --git a/Geomative/Fumamx项目阶段性文档.md b/Geomative/Fumamx项目阶段性文档.md
new file mode 100644
index 0000000..39d8c14
--- /dev/null
+++ b/Geomative/Fumamx项目阶段性文档.md
@@ -0,0 +1,156 @@
+
+我已经仔细阅读了这两个项目。让我为你总结一下：
+📁 fumamxCLI - 孚盟MX 贸易数据 CLI 工具
+类型: Python 命令行工具
+技术栈: Python 3.11+, Click, Rich, Requests, Playwright
+核心功能
+公司搜索 - 搜索全球贸易企业
+进口数据分析 - 获取进口企业数据、统计
+出口数据分析 - 获取出口企业数据、统计
+产品分析 - HS 编码分析、产品聚合、对比分析
+国家分析 - 贸易来源国家、出口国家分析
+贸易记录 - 查询完整贸易详情
+企业详情 - 公司信息、联系人、电话
+项目结构
+fumamxCLI/
+├── fumamx # 可执行入口
+├── src/
+│ ├── cli.py # 命令行接口 (21+ 命令)
+│ ├── client.py # API 客户端
+│ ├── config.py # 认证配置
+│ ├── auth.py # 自动登录/Token 刷新
+│ └── dynamic_api.py # 动态 API 调用
+├── scripts/
+│ ├── capture_api.js # 浏览器抓取脚本
+│ └── setup_apis.py # API 代码生成器
+└── fumamx_apis.json # 抓取的 API 数据
+认证方式
+使用浏览器 Cookie + Token
+支持 Playwright 自动登录刷新
+
+---
+Token
+Token 缓存机制
+📁 fumamxCRM - 客户关系管理系统
+类型: Web 应用 (FastAPI + React)
+技术栈:
+后端: FastAPI, SQLAlchemy, SQLite
+前端: React, Vite, Tailwind CSS
+端口: 后端 10001, 前端 10000
+核心功能
+客户管理
+本地客户存储
+潜在客户导入（从 fumamxCLI 数据源）
+联系人管理
+数据发现
+关键词搜索企业
+分页导入潜在客户
+后台异步导入任务
+深度分析
+AI 驱动的企业商业画布分析
+生成 Markdown 分析报告
+支持多种大模型
+系统设置
+Cookie/Token 管理
+系统状态监控
+数据库模型
+表名 说明
+users 管理员用户
+customers 本地客户
+countries 国家信息
+import_tasks 导入任务
+potential_customers 潜在客户
+potential_contacts 潜在客户联系人
+deep_analyses 深度分析记录
+system_settings 系统设置
+项目结构
+```
+fumamxCRM/
+├── backend/
+│ ├── app.py # FastAPI 主应用
+│ ├── models.py # SQLAlchemy 模型
+│ ├── schemas.py # Pydantic 模型
+│ ├── database.py # 数据库配置
+│ ├── auth.py # 认证逻辑
+│ ├── import_service.py # 导入服务
+│ ├── fumamx_cli.py # CLI 包装器
+│ └── routers/
+│ ├── auth.py
+│ ├── customers.py
+│ ├── discover.py
+│ ├── import_tasks.py
+│ ├── potential_customers.py
+│ ├── deep_analysis.py
+│ └── settings
+
+.py
+└── frontend/
+ ├── src/
+ │ ├── App.jsx
+ │ ├── api.js
+ │ ├── components/
+ │ └── pages/
+ └── node_modules/
+🔗 两个项目的关系
+fumamxCLI (数据获取层)
+ ↓
+fumamxCRM/backend/fumamx_cli.py (包装器)
+ ↓
+fumamxCRM (数据管理层 + Web UI)
+```
+fumamxCLI 负责从孚盟MX平台抓取贸易数据
+fumamxCRM 提供 Web 界面管理这些数据，支持批量导入、深度分析
+蔡叔叔，我已经了解了这两个项目的全貌。需要我：
+继续查看某个具体文件？
+运行/测试某个功能？
+开发新功能？
+
+---
+
+
+
+
+搞定了！现在 fumamxCRM 也做成了 systemd 服务，**随系统启动自动运行**。
+
+---
+
+**创建的两个服务**
+
+| 服务 | 端口 | 状态 |
+|------|------|------|
+| `fumamx-crm-backend.service` | 10001 | active (running) |
+| `fumamx-crm-frontend.service` | 10000 | active (running) |
+
+---
+
+**常用命令**
+
+```
+# 查看状态
+sudo systemctl status fumamx-crm-backend
+sudo systemctl status fumamx-crm-frontend
+
+# 重启
+sudo systemctl restart fumamx-crm-backend
+sudo systemctl restart fumamx-crm-frontend
+
+# 停止
+sudo systemctl stop fumamx-crm-backend
+sudo systemctl stop fumamx-crm-frontend
+
+# 查看日志
+sudo journalctl -u fumamx-crm-backend -f
+sudo journalctl -u fumamx-crm-frontend -f
+```
+
+---
+
+**验证**
+
+- 前端 `http://129.211.173.87:10000` → HTTP 200 ✓
+- 后端 `http://129.211.173.87:10001/health` → `{"status":"healthy"}` ✓
+
+现在服务器重启后，fumamxCRM 会自动启动，不需要再手动跑 `start.sh`。
+
+
+
diff --git a/Geomative/Nanobot实现fumamxCLI.md b/Geomative/Nanobot实现fumamxCLI.md
new file mode 100644
index 0000000..be3f5e3
--- /dev/null
+++ b/Geomative/Nanobot实现fumamxCLI.md
@@ -0,0 +1,627 @@
+
+我们开始新对话。
+你使用OpenCLI（https://github.com/jackwener/OpenCLI）帮我生成网站https://www.fumamx.com的fumamxCLI
+
+我在/home/ubuntu目录放一个网站的cookie文件名是fumamx_cookies.txt 。
+
+我是通过URL: https://www.fumamx.com/#/login
+账号和密码 showmin.zhou@163.com Kingc.83695910
+然后获取到cookie的。
+
+登录进来后，url 是：https://www.fumamx.com/#/main/workbench
+
+在/home/ubuntu/nanocode目录建立一个fumamxCLI 做这个事情。
+
+---
+
+文件在：
+
+```
+/home/ubuntu/nanocode/fumamxCLI/scripts/capture_api.js
+```
+
+你可以直接复制使用。完整内容如下，**直接在浏览器控制台粘贴**：
+
+```javascript
+/**
+ * 孚盟MX API 抓取脚本
+ * 使用方法：
+ * 1. 登录 https://www.fumamx.com/#/login
+ * 2. 按 F12 打开开发者工具
+ * 3. 切换到 Console 面板
+ * 4. 粘贴此脚本并回车
+ * 5. 在页面上正常操作（点客户、邮件等模块）
+ * 6. 所有 API 请求会被记录并导出
+ */
+
+(function() {
+ 'use strict';
+
+ window.fumamxAPIs = window.fumamxAPIs || [];
+
+ // 拦截 fetch
+ const originalFetch = window.fetch;
+ window.fetch = function(...args) {
+ const url = args[0];
+ const options = args[1] || {};
+
+ if (typeof url === 'string' && (url.includes('/api/') || url.includes('/rest/') || url.includes('/v1/'))) {
+ const record = {
+ method: options.method || 'GET',
+ url: url,
+ headers: options.headers,
+ body: options.body,
+ timestamp: new Date().toISOString()
+ };
+ window.fumamxAPIs.push(record);
+ console.log('📡 API:', record.method, url);
+ }
+
+ return originalFetch.apply(this, args);
+ };
+
+ // 拦截 XMLHttpRequest
+ const originalOpen = XMLHttpRequest.prototype.open;
+ const originalSend = XMLHttpRequest.prototype.send;
+
+ XMLHttpRequest.prototype.open = function(method, url, ...args) {
+ this._fumamxMethod = method;
+ this._fumamxUrl = url;
+ return originalOpen.call(this, method, url, ...args);
+ };
+
+ XMLHttpRequest.prototype.send = function(body) {
+ const url = this._fumamxUrl;
+ if (typeof url === 'string' && (url.includes('/api/') || url.includes('/rest/') || url.includes('/v1/'))) {
+ const record = {
+ method: this._fumamxMethod,
+ url: url,
+ body: body,
+ timestamp: new Date().toISOString()
+ };
+ window.fumamxAPIs.push(record);
+ console.log('📡 API:', record.method, url);
+ }
+ return originalSend.call(this, body);
+ };
+
+ // 导出功能
+ window.exportFumamxAPIs = function() {
+ const data = JSON.stringify(window.fumamxAPIs, null, 2);
+ const blob = new Blob([data], {type: 'application/json'});
+ const url = URL.createObjectURL(blob);
+ const a = document.createElement('a');
+ a.href = url;
+ a.download = 'fumamx_apis.json';
+ a.click();
+ URL.revokeObjectURL(url);
+ console.log('✅ 已导出 ' + window.fumamxAPIs.length + ' 个 API 端点到 fumamx_apis.json');
+ };
+
+ console.log('✅ 孚盟MX API 抓取器已启动');
+ console.log('💡 操作页面后，在控制台运行 exportFumamxAPIs() 导出结果');
+})();
+```
+
+**操作步骤**：
+1. 登录孚盟 → F12 → Console
+2. 粘贴上面的代码，回车
+3. 点击「客户」「邮件」等模块
+4. 看到 `📡 API: GET /api/xxx` 说明正在记录
+5. 运行 `exportFumamxAPIs()` 下载 JSON
+6. 把 `fumamx_apis.json` 上传到服务器
+7. 运行 `python3 scripts/setup_apis.py fumamx_apis.json` 生成 CLI 代码
+
+---
+
+## ✅ 完成！fumamxCLI 已生成并通过验证
+
+### 生成的文件
+
+| 文件 | 描述 |
+|------|------|
+| `src/client.py` | API 客户端，20+ 个方法 |
+| `src/cli.py` | 命令行接口，21 个命令 |
+| `src/config.py` | 认证配置 (token) |
+| `README.md` | 完整使用文档 |
+
+### 可用命令 (21 个)
+
+```
+系统: status, cookies, call
+公司: company search
+进口: import- enterprise/stats
+出口: export enterprise/stats
+产品: product hs-code/agg/analysis/compare-lose/compare-new
+国家: country list/export/data
+贸易: trade detail
+```
+
+### 验证结果
+
+| 测试项 | 结果 |
+|--------|------|
+| API 连接 | ✅ 正常 |
+| 用户认证 | ✅ showmin.zhou@163.com |
+| 公司搜索 | ✅ 返回数据 |
+| 国家列表 | ✅ 200+ 国家 |
+| 通用调用 | ✅ 灵活调用任意端点 |
+
+### 快速使用
+
+```bash
+cd /home/ubuntu/nanocode/fumamxCLI
+
+# 检查状态
+./fumamx status
+
+# 搜索公司
+./fumamx company search "Apple"
+
+# 查看所有命令
+./fumamx list-apis
+
+# 调用任意 API
+./fumamx call GET /pcapi/tradeData/api/V2/Account/GetTradeSourceCountrys
+```
+
+### 注意
+
+Token 存储在 `src/config.py`，如果 API 返回 401，需要重新从浏览器抓取更新 token。
+
+---
+
+
+## 我们继续下一个任务，创建一个web项目，通过fumamxCLI对https://www.fumamx.com网站书籍进行管理。
+### 项目目录
+在/home/ubuntu/nanocode目录创建一个项目fumamxCRM（我已经创建了目录） 。
+### Web项目风格
+~/nanocode/personal-blog 这个项目的设计风格是新粗野风格，~/nanocode/personal-blog/DESIGN.md 是你总结的设计风格。我们使用这个风格来设计我们的项目。
+### 技术栈
+相同于~/nanocode/personal-blog
+
+### 详细需求
+#### 登录界面
+是我们自己的fumamxCRM管理登录，类似于personal-blog的admin/abcd1234管理账号登录。但是，fumamxCRM没有前台浏览页面，登录页面是第一个页面，用户必须登录才能访问后台管理界面。
+#### 后台管理主界面
+后台管理主界面，类似于~/nanocode/personal-blog后台管理界面。
+后台分几个模块：
+1. 客户管理：客户信息是从哪里来的？是从fumamxCLI搜索并添加而来的。
+2. 发现：通过fumamxCLI的search命令，搜索到客户信息列表。
+ 客户详情：点击某个客户，进入客户详情
+ 添加客户：可以将某个搜索到的客户，添加到本地数据库。
+3. 设置：
+ 修改管理员密码；
+ 设置cookie, 因为cookie会过期。设置界面有地方输入cookie的文本内容，后台存储到文件fumamx_cookies.txt，然后提取信息和AccessToken
+
+我们先实现这些基本功能，后续逐步添加更多功能。
+
+**以上内容，你是否清晰，如果有问题请反问我。知道你清楚为止。
+然后，你提出的技术方案，和实现计划，分步骤完成任务。**
+
+
+![[Pasted image 20260414203040.png]]
+
+
+
+---
+
+**问题根源：浏览器导出时缺少 `user` 和 `pass` Cookie**
+
+这是**浏览器导出机制的问题**，不是 CRM 的问题。你需要确认浏览器中确实存在这些 Cookie。
+
+---
+
+**排查步骤：**
+
+### 1. 浏览器中验证 Cookie 是否存在
+打开 https://www.fumamx.com 并登录后，按 **F12** → **Application** → **Cookies** → **https://www.fumamx.com**，检查是否有以下字段：
+
+| Cookie 名 | 值示例 |
+|-----------|--------|
+| `user` | `showmin.zhou@163.com` |
+| `pass` | `U2FsdGVkX1+fFGAO5Ib25ulin0hAOBVqhGjgBkmNMW4=` |
+| `rememberPassword` | `true` |
+
+### 2. 如果浏览器中没有这些 Cookie
+说明你没有勾选**"记住密码"**或**"保持登录"**。请：
+1. 退出登录
+2. 重新登录时勾选 **"记住密码"** 或 **"保持登录状态"**
+3. 再次检查 Cookies
+
+### 3. 如果浏览器中有，但导出时丢失
+尝试使用 **EditThisCookie** 扩展的 **"Export"** 功能时，选择 **"Export as JSON"** 而不是其他格式。
+
+或者手动复制：在 EditThisCookie 中点击 `user` 和 `pass` 这两个 cookie，确认它们存在，然后手动编辑 JSON 添加进去：
+
+```json
+[
+ // ... 你导出的4个 ...
+ {
+ "domain": "www.fumamx.com",
+ "name": "user",
+ "value": "showmin.zhou@163.com",
+ "path": "/",
+ "secure": false,
+ "httpOnly": false,
+ "session": false,
+ "hostOnly": true,
+ "expirationDate": 1776821773,
+ "sameSite": "unspecified"
+ },
+ {
+ "domain": "www.fumamx.com",
+ "name": "pass",
+ "value": "U2FsdGVkX1+fFGAO5Ib25ulin0hAOBVqhGjgBkmNMW4=",
+ "path": "/",
+ "secure": false,
+ "httpOnly": false,
+ "session": false,
+ "hostOnly": true,
+ "expirationDate": 1776821773,
+ "sameSite": "unspecified"
+ }
+]
+```
+
+---
+
+**关键问题：fumamx API 需要 `user` + `pass` 来验证身份**，仅 `_t` token 不够。请检查浏览器中是否有这两个 cookie。
+
+
+---
+
+**还是不行** —— API 仍然返回 `"登陆令牌已失效"` (code: -2)
+
+---
+
+**问题分析：**
+
+虽然 7 个 cookies 都正确加载了，但 `_t` 令牌本身已经**过期或无效**。
+
+```
+_t: ZjtDL09DbYW0mzah ← 这个 token 已经失效
+```
+
+`_t` 是一个 **session cookie**，它的有效期很短：
+- 当你**关闭浏览器**时失效
+- 或者**一段时间不操作**后过期
+- 或者**从其他设备/浏览器登录**时旧 token 被吊销
+
+---
+
+**必须重新获取：**
+
+1. 打开 https://www.fumamx.com
+2. **如果已登录，先退出再重新登录**（强制刷新 token）
+3. 登录后立即导出 cookies（**不要关闭浏览器**）
+4. 将新的 JSON 发给我
+
+**注意：** `_t` 是 session 级别的，**每次重新登录都会变**。必须刚登录完就导出。
+
+---
+
+
+**找到根本原因了！**
+
+问题不在 Cookie，而在 **`config.py` 中的硬编码 token 已过期**：
+
+```python
+ACCESS_TOKEN = "Iiw4o3RoqltwY5EW"
+INDIVIDUAL_ACCESS_TOKEN = "lAInwlJfiKx5FOnF"
+```
+
+fumamx API 使用**双层认证**：
+1. Cookie `_t` ✅ （你从浏览器获取的，是对的）
+2. URL 参数 `accessToken` + `individualAccessToken` ❌ （硬编码的，已过期）
+
+---
+
+**需要你在浏览器中获取这些动态 token：**
+
+1. 打开 https://www.fumamx.com 并登录
+2. 按 **F12** → **Network/网络** 标签
+3. 在页面中随便搜索一个公司（比如输入 "Apple" 搜索）
+4. 找到请求如 `company` 或 `ImpEnterprise`
+5. 查看请求的 **URL 参数**，找到：
+ - `accessToken=xxxxx`
+ - `individualAccessToken=xxxxx`
+
+或者：
+
+1. 在 Console 中执行：
+```javascript
+localStorage.getItem('accessToken')
+localStorage.getItem('individualAccessToken')
+```
+
+---
+
+**能否帮我获取这两个 token？** 这样我就能更新 config.py 并验证连接。
+
+
+
+---
+
+
+
+**之前是通过浏览器脚本抓取的**：
+
+1. 在浏览器控制台粘贴 `scripts/capture_api.js`
+2. 操作页面（搜索公司等）
+3. 运行 `exportFumamxAPIs()` 导出 API 请求
+4. 从导出的 JSON 中提取 URL 参数里的 token
+
+---
+
+**但现在有更简单的方法**，直接在浏览器控制台执行：
+
+```javascript
+// 方法 1: 从 localStorage 获取
+localStorage.getItem('accessToken')
+localStorage.getItem('individualAccessToken')
+
+// 方法 2: 从当前 URL 获取（如果你在 fumamx 页面）
+new URLSearchParams(window.location.search).get('accessToken')
+```
+
+---
+
+**或者手动复制**：
+
+1. 按 **F12** → **Network**
+2. 随便操作一下（比如搜索 "Apple"）
+3. 找一个请求，比如 `company` 或 `ImpEnterprise`
+4. 查看请求的 **URL**，找到：
+ ```
+ https://www.fumamx.com/pcapi/...?accessToken=xxxx&individualAccessToken=yyyy&...
+ ```
+5. 复制 `accessToken` 和 `individualAccessToken` 的值给我
+
+---
+
+**最快方式**：直接在浏览器控制台输入 `localStorage.getItem('accessToken')` 回车，把结果发给我。
+
+
+---
+
+试试这些方式获取 token：
+
+**1. sessionStorage**
+```javascript
+sessionStorage.getItem('accessToken')
+sessionStorage.getItem('individualAccessToken')
+```
+
+**2. 查看当前 URL**
+```javascript
+window.location.href
+```
+
+**3. Network 面板抓取（最可靠）**
+- F12 → Network
+- 在页面中搜索一个公司（比如输入 "Apple"）
+- 找到任意一个 API 请求（如 `company`、`ImpEnterprise`）
+- 点击它 → Headers → Request URL
+- 复制里面 `accessToken=` 和 `individualAccessToken=` 后面的值
+
+**4. 查看所有 cookie**
+```javascript
+document.cookie
+```
+
+请试一下方法 2 或 3，把获取到的值发给我。
+
+
+---
+sessionStorage.getItem('accessToken')
+
+'{"ownerId":11011595,"scope":"11011595","value":"71uRBqAwJWWLxWyp","createTime":1776217804534,"expireTime":1776304204534,"referURL":null,"agent":null,"agentVersion":null,"readOnly":0,"versionIteration":1,"companyTag":null,"platform":"pc","cid":1067743}'
+sessionStorage.getItem('individualAccessToken')
+
+'{"ownerId":10444522,"scope":"independent","value":"CmA5vs7xmGEYcsuD","createTime":1776217804069,"expireTime":1776304204069,"referURL":null,"agent":null,"agentVersion":null,"readOnly":0,"versionIteration":null,"companyTag":null,"platform":"pc","cid":null}'
+
+---
+
+**✅ 成功！API 连接正常，认证有效！**
+
+```
+✓ 已加载 7 个 cookies
+✓ API 连接正常，认证有效
+用户: showmin.zhou@163.com
+公司ID: 1067743
+```
+
+---
+
+**Token 已更新**：
+- `ACCESS_TOKEN`: `71uRBqAwJWWLxWyp`
+- `INDIVIDUAL_ACCESS_TOKEN`: `CmA5vs7xmGEYcsuD`
+
+现在可以正常使用 fumamxCRM 了。让我也重启一下 CRM 后端服务：
+
+---
+
+从你提供的 `sessionStorage` 中提取的：
+
+```javascript
+sessionStorage.getItem('accessToken')
+'{"ownerId":11011595,"scope":"11011595","value":"71uRBqAwJWWLxWyp",...}'
+ ↑↑↑↑↑↑↑↑↑↑
+ 这就是 ACCESS_TOKEN
+```
+
+我提取了 JSON 中 `value` 字段的值：
+- `ACCESS_TOKEN` = `"71uRBqAwJWWLxWyp"`
+- `INDIVIDUAL_ACCESS_TOKEN` = `"CmA5vs7xmGEYcsuD"` （来自 `individualAccessToken` 的 value）
+
+---
+
+**这两个 token 的区别**：
+
+| Token | 来源 | 用途 |
+|-------|------|------|
+| `ACCESS_TOKEN` | `accessToken.value` | 公司/企业级别认证 |
+| `INDIVIDUAL_ACCESS_TOKEN` | `individualAccessToken.value` | 个人用户级别认证 |
+
+fumamx API 请求时需要同时提供这两个 token 作为 URL 参数。
+
+
+对，**cookie 里没有 `ACCESS_TOKEN`**。
+
+当前 cookie 文件里只有这 7 个：
+| Cookie | 值 |
+|--------|-----|
+| `_t` | `CmA5vs7xmGEYcsuD` |
+| `user` | `showmin.zhou@163.com` |
+| `pass` | `U2FsdGVkX1...` |
+| `language` | `zh-cn` |
+| `timezone` | `Asia/Shanghai` |
+| `AiImageEdit` | `false` |
+| `rememberPassword` | `true` |
+
+**注意**：`_t` 的值恰好和 `INDIVIDUAL_ACCESS_TOKEN` 相同（`CmA5vs7xmGEYcsuD`），但 `ACCESS_TOKEN`（`71uRBqAwJWWLxWyp`）**完全不在 cookie 中**。
+
+---
+
+**fumamx 的三层认证**：
+
+| 认证层 | 存储位置 | 获取方式 |
+|--------|----------|----------|
+| Cookie `_t` | Cookie | 浏览器导出 |
+| `INDIVIDUAL_ACCESS_TOKEN` | Cookie `_t` / sessionStorage | 相同 |
+| `ACCESS_TOKEN` | **sessionStorage** | `sessionStorage.getItem('accessToken').value` |
+
+所以之前只更新 cookie 是不够的，还必须从 `sessionStorage` 获取 `ACCESS_TOKEN`。
+
+
+
+---
+
+
+根据代码梳理，给你完整说明：
+
+---
+
+## 1️⃣ 现在有几个表？
+
+**共 7 个表**（SQLite）：
+
+| 表名 | 用途 |
+|------|------|
+| `users` | 管理员用户（登录） |
+| `customers` | 本地客户（早期表，现在用下面的） |
+| `countries` | 国家信息（274条数据） |
+| `import_tasks` | 潜在客户导入任务（后台任务） |
+| `potential_customers` | **潜在客户主表**（核心） |
+| `potential_contacts` | 潜在客户联系人 |
+| `deep_analyses` | 深度分析记录 |
+| `system_settings` | 系统设置（API Key等） |
+
+---
+
+## 2️⃣ 潜在客户列表加载逻辑
+
+**前端请求** → **后端查询** → **直接返回**（无级联）：
+
+```python
+# 1. 前端请求
+GET /potential-customers/?keyword=xxx&country=US&page=1&page_size=20
+
+# 2. 后端查询（单次查询，无JOIN）
+query = db.query(PotentialCustomer)
+if keyword:
+ query = query.filter(name包含 OR domain包含 OR description包含)
+if country:
+ query = query.filter(country == country)
+
+# 3. 排序分页
+customers = query.order_by(source_score DESC).offset().limit().all()
+
+# 4. 直接返回（反范式设计，状态内联在客户数据中）
+return {
+ "results": [c.to_dict() for c in customers] # 包含 analysis_status
+}
+```
+
+**关键点**：
+- `analysis_status` 直接存储在 `potential_customers` 表（冗余字段）
+- **无需 JOIN** `deep_analyses` 表
+- **无需二次查询** 来判断"是否已分析"
+
+---
+
+## 3️⃣ 深度分析过程
+
+**队列式后台处理**：
+
+```
+用户点击"深度分析"
+ ↓
+创建 DeepAnalysis 记录 (status=pending)
+ ↓
+更新 customer.analysis_status = "pending" ← 反范式同步
+ ↓
+加入内存队列 (analysis_queue)
+ ↓
+后台线程取出任务
+ ↓
+更新 analysis.status = "processing"
+更新 customer.analysis_status = "processing"
+ ↓
+调用 LLM API (DeepSeek/Moonshot)
+ ↓
+生成报告文件 → 保存到 analysis_reports/
+ ↓
+更新 analysis.status = "completed"
+更新 customer.analysis_status = "completed"
+更新 customer.analysis_count += 1
+```
+
+**状态流转**：
+```
+none → pending → processing → completed
+ ↘ failed
+```
+
+**反范式设计核心**：
+- 分析状态同时存两份：
+ 1. `deep_analyses.status` —— 详细记录
+ 2. `potential_customers.analysis_status` —— 冗余字段（列表页直接用）
+
+这样列表页查询时**只需查一张表**，性能提升明显。
+
+
+---
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/MyArticles/AI术语完全指南：从入门到进阶.md b/MyArticles/AI术语完全指南：从入门到进阶.md
new file mode 100644
index 0000000..701664b
--- /dev/null
+++ b/MyArticles/AI术语完全指南：从入门到进阶.md
@@ -0,0 +1,1390 @@
+# AI术语完全指南：从入门到进阶
+
+> 一份系统化的 AI 技术术语解释手册，从基础概念到高级应用，循序渐进，通俗易懂。
+>
+>
+
+## Inference \& Optimization（推理与优化）
+
+### KV cache
+
+**什么是 KV Cache？**
+
+KV Cache 是 Key\-Value 缓存，是加速大模型推理的最重要技术之一，避免重复计算。
+
+**工作原理**：
+
+- Transformer 每层都要计算 Attention 的 K（键）和 V（值）
+
+- 生成第 1 个词：计算所有 token 的 K,V
+
+- 生成第 2 个词：不用重新算前面的，只算新 token 的 K,V，缓存的直接用
+
+- 以此类推\.\.\.
+
+**效果**：
+
+- 速度提升 2\-3 倍
+
+- 显存占用稍微增加
+
+- 生成越长，加速越明显
+
+✅ **简短总结**：KV Cache 缓存 Attention 计算中的 Key 和 Value，避免生成每个 token 时都重新计算所有前面的 token，是推理加速的基础技术。
+
+🧠 **简单思维模型**：KV Cache 就像**做数学题记中间结果**，算第二步时不用从头算第一步，直接用第一步算好的结果，大大加快速度。
+
+⚠️ **初学者常见错误**：
+
+1. 以为 KV Cache 免费 —— 会增加一些显存占用
+
+2. 所有场景都开 KV Cache—— 极短的生成反而可能变慢
+
+3. 不理解 Cache 的序列限制 —— 超过窗口需要重建缓存
+
+🎯 **小练习 / 项目**：
+运行同一个模型，分别开启和关闭 KV Cache，生成长文本（比如 1000 字），对比两者的生成速度差异。
+
+---
+
+### Flash Attention
+
+**什么是 Flash Attention？**
+
+Flash Attention 是斯坦福大学提出的 Attention 加速算法，通过优化计算顺序和内存访问，大幅提升速度并降低显存。
+
+**核心优化**：
+
+1. **Tiling（分块计算）**：把大矩阵分成小块，一块块算
+
+2. **重计算**：不用存所有中间结果，需要时重新算
+
+3. **IO 感知**：优化 GPU SRAM 和 HBM 之间的数据传输
+
+**效果**：
+
+- 速度提升 2\-4 倍
+
+- 显存减少 50\-80%
+
+- 长序列效果尤其明显
+
+✅ **简短总结**：Flash Attention 通过优化 Attention 的计算方式和内存访问模式，在不损失精度的前提下大幅提升速度、降低显存，是现在所有大模型的标配。
+
+🧠 **简单思维模型**：Flash Attention 就像**整理书桌**，原来把所有书都摊在桌上（占满内存），现在把书分堆，只把正在看的那堆拿出来，既快又省空间。
+
+⚠️ **初学者常见错误**：
+
+1. Flash Attention 只加速 —— 其实主要是省显存
+
+2. 所有硬件都支持 —— 需要 Ampere 及以上架构
+
+3. 一定比普通 Attention 快 —— 极短序列可能差不多
+
+🎯 **小练习 / 项目**：
+分别用普通 Attention 和 Flash Attention 跑一个长序列（4096 tokens），对比显存占用和计算时间。
+
+---
+
+### Speculative decoding（投机解码）
+
+**什么是投机解码？**
+
+投机解码用一个小模型 \&\#34;猜\&\#34; 接下来的 N 个词，然后大模型一次性验证，猜对了就跳过，猜错了再修正。
+
+**工作流程**：
+
+1. 小模型（草稿模型）快速生成 k 个候选词
+
+2. 大模型（目标模型）并行验证这 k 个词
+
+3. 猜对的都保留，猜错的从那里重新开始
+
+4. 平均每次能生成 2\-3 个词
+
+**效果**：
+
+- 速度提升 2\-3 倍
+
+- 输出完全和原模型一样（无质量损失）
+
+- 不需要重新训练
+
+✅ **简短总结**：投机解码用小模型快速 \&\#34;猜\&\#34; 多个词，大模型一次性验证，在不损失质量的前提下大幅提升生成速度，是推理加速的黑科技。
+
+🧠 **简单思维模型**：投机解码就像**学霸帮学渣检查作业**，学渣（小模型）快速写完几道题，学霸（大模型）一眼检查完，对的就过，错的改，比学霸一道一道做快多了。
+
+⚠️ **初学者常见错误**：
+
+1. 会改变输出质量 —— 数学证明和原模型分布完全一致
+
+2. 随便什么小模型都行 —— 需要和大模型分布接近
+
+3. 加速比无上限 —— 通常 2\-3 倍是极限
+
+🎯 **小练习 / 项目**：
+用 llama\.cpp 开启投机解码，用 7B 模型做草稿模型验证 13B 模型，对比普通解码的 tokens / 秒速度。
+
+---
+
+### Inference optimization（推理优化）
+
+**什么是推理优化？**
+
+推理优化是各种让模型跑更快、用更少显存、支持更多并发用户的技术总称。
+
+**主要优化方向**：
+
+1. **算法层面**：KV Cache、Flash Attention、投机解码
+
+2. **量化**：4bit/8bit 量化
+
+3. **编译优化**：vLLM、TensorRT\-LLM
+
+4. **批处理**：把多个请求合并处理
+
+5. **连续批处理**：动态加入新请求
+
+**优化目标**：
+
+- **延迟**：单个用户的响应速度
+
+- **吞吐量**：每秒能处理的 token 数
+
+- **成本**：每 1000token 多少钱
+
+✅ **简短总结**：推理优化包含算法、量化、编译、批处理等多种技术，目标是降低延迟、提高吞吐量、降低成本，是 AI 产品落地的关键。
+
+🧠 **简单思维模型**：推理优化就像**开餐厅优化翻台率**，通过各种方法让同样大的店（GPU）能接待更多客人（用户），上菜更快（延迟低）。
+
+⚠️ **初学者常见错误**：
+
+1. 只优化速度 —— 吞吐量和成本同样重要
+
+2. 优化过度影响质量 —— 要在质量和速度间平衡
+
+3. 一种优化就够了 —— 通常需要多种技术组合
+
+🎯 **小练习 / 项目**：
+列出你能想到的所有推理优化技术，给每个技术打分：速度提升多少、显存节省多少、质量损失多少、实现难度多少。
+
+---
+
+### Model serving（模型服务）
+
+**什么是模型服务？**
+
+模型服务是把训练好的模型包装成 API，让用户通过网络调用，是大模型商业化的核心。
+
+**模型服务做什么**：
+
+1. **API 接口**：HTTP/gRPC 接口
+
+2. **批处理调度**：智能合并请求
+
+3. **负载均衡**：多 GPU / 多机器分配
+
+4. **限流监控**：QPS 限制、日志监控
+
+5. **弹性伸缩**：自动扩缩容
+
+**常用框架**：
+
+- vLLM：开源，性能好
+
+- Text Generation Inference \(TGI\)：Hugging Face
+
+- TensorRT\-LLM：NVIDIA
+
+- vLLM \> TGI \> 原生 transformers
+
+✅ **简短总结**：模型服务将训练好的模型包装成可网络调用的 API，处理批处理、负载均衡、监控等工程问题，是大模型从 demo 到产品的关键一步。
+
+🧠 **简单思维模型**：模型服务就像**外卖平台**，厨师（模型）在厨房做饭，平台接订单、调度、送餐、收钱，用户不用去厨房直接点餐就行。
+
+⚠️ **初学者常见错误**：
+
+1. 直接用 transformers 做服务 —— 性能差 10 倍以上
+
+2. 不做批处理 ——GPU 利用率极低
+
+3. 忽略监控 —— 出问题不知道
+
+🎯 **小练习 / 项目**：
+用 vLLM 启动一个模型服务，然后用 ab 或 wrk 做压力测试，看每秒能处理多少请求，GPU 利用率是多少。
+
+---
+
+### Batch inference（批量推理）
+
+**什么是批量推理？**
+
+批量推理是把多个请求放在一起同时处理，利用 GPU 的并行计算能力，大大提高效率。
+
+**为什么批量处理更快？**
+
+- GPU 是并行计算，算 1 个和算 8 个时间差不多
+
+- 单独处理：8 个请求 = 8 × 时间
+
+- 批量处理：8 个请求 ≈ 1 × 时间
+
+**两种批处理**：
+
+1. **静态批处理**：攒够 N 个请求一起处理
+
+2. **连续批处理（Continuous Batching）**：不用等，处理完一个就补一个新的
+
+✅ **简短总结**：批量推理将多个请求合并同时处理，充分利用 GPU 并行能力，大幅提高吞吐量、降低单 token 成本，是高并发服务的必备。
+
+🧠 **简单思维模型**：批量推理就像**公交车 vs 出租车**，出租车（单请求）一次拉一个人，公交车（批量）一次拉几十人，人均成本低多了。
+
+⚠️ **初学者常见错误**：
+
+1. 批量越大越好 —— 太大反而变慢，通常 32\-64 最优
+
+2. 只看吞吐量 —— 批量太大会增加延迟
+
+3. 静态批处理就够了 —— 连续批处理好太多
+
+🎯 **小练习 / 项目**：
+写一个简单脚本，分别用 batch size=1, 4, 8, 16, 32 跑推理，记录每个 batch 的时间和吞吐量，画个曲线找最优值。
+
+---
+
+### GPU basics（GPU 基础）
+
+**什么是 GPU？**
+
+GPU = Graphics Processing Unit，图形处理器，现在是 AI 训练和推理的核心硬件。
+
+**GPU vs CPU**：
+
+- **CPU**：几个强大的核心，擅长复杂逻辑，串行计算
+
+- **GPU**：几千个弱小的核心，擅长简单重复，并行计算
+
+**AI 常用 GPU**：
+
+- **消费级**：RTX 3090/4090（24GB）
+
+- **数据中心**：A100（40/80GB）、H100（80GB）
+
+- **性价比**：RTX 4090 \> A100 \> H100
+
+**关键指标**：
+
+- 显存大小（最重要）
+
+- 显存带宽
+
+- TFLOPS（算力）
+
+✅ **简短总结**：GPU 通过数千个核心的大规模并行计算能力，成为 AI 训练和推理的核心硬件，显存大小是选择 GPU 的第一指标。
+
+🧠 **简单思维模型**：CPU 是**一个数学教授**，算难题很快；GPU 是**10000 个小学生**，每人算一道简单题，10000 道题一起算完比教授快多了。
+
+⚠️ **初学者常见错误**：
+
+1. 只看算力不看显存 —— 显存不够再强也没用
+
+2. 消费级卡不能做训练 ——4090 训练 7B 模型很好用
+
+3. 越贵的卡性价比越高 ——4090 是性价比之王
+
+🎯 **小练习 / 项目**：
+做一个 GPU 对比表，列出 RTX 3090、4090、A10、A100、H100 的：显存、显存带宽、FP16 算力、价格、每 GB 显存成本。
+
+---
+
+### VRAM basics（显存基础）
+
+**什么是 VRAM？**
+
+VRAM = Video RAM，显卡专用内存，是 GPU 能同时放多少数据的决定因素。
+
+**模型显存占用估算**：
+
+- **FP16**：参数 × 2 字节 = 模型大小
+
+ - 7B 模型：14GB
+
+ - 13B 模型：26GB
+
+ - 70B 模型：140GB
+
+- **4bit 量化**：参数 × 0\.5 字节
+
+ - 7B 模型：3\.5GB
+
+ - 13B 模型：6\.5GB
+
+ - 70B 模型：35GB
+
+**显存还用来放什么**：
+
+1. 模型权重
+
+2. KV Cache
+
+3. 中间激活值
+
+4. 优化器状态（训练时）
+
+✅ **简短总结**：VRAM 是 GPU 的专用内存，决定了能运行多大的模型，模型大小 = 参数量 × 每个参数字节数，量化可以大幅减少显存需求。
+
+🧠 **简单思维模型**：VRAM 就像**工作台大小**，工作台越大，能同时放的材料（模型参数、中间结果）越多，能做的活越复杂。
+
+⚠️ **初学者常见错误**：
+
+1. 只算模型大小 ——KV Cache 和激活值也要占显存
+
+2. 24GB 刚好放 13B FP16—— 实际需要留余量
+
+3. 显存占满才好 —— 留 10\-20% 余量更稳定
+
+🎯 **小练习 / 项目**：
+用 nvidia\-smi 监控，分别加载 7B 模型的 FP16、8bit、4bit 版本，记录每个的显存占用，和理论值对比。
+
+---
+
+### Latency vs quality tradeoffs（延迟与质量权衡）
+
+**什么是权衡？**
+
+AI 推理中，速度和质量通常是矛盾的，要根据场景做取舍。
+
+**常见的权衡点**：
+
+|技术|质量影响|速度提升|适用场景|
+|---|---|---|---|
+|4bit 量化|极小|2x|几乎所有场景|
+|8bit 量化|几乎无|1\.5x|所有场景|
+|投机解码|无|2\-3x|所有场景|
+|更小的模型|有损失|5\-10x|简单任务|
+|减少 beam search|略有损失|2x|聊天场景|
+|降低上下文|可能损失|显著|短对话|
+
+**怎么选？**
+
+- **聊天机器人**：延迟优先，用户等不及
+
+- **写作 / 总结**：质量优先，慢一点没关系
+
+- **批量处理**：吞吐量优先
+
+✅ **简短总结**：推理中速度和质量通常需要权衡，不同场景优先级不同，聊天要快，复杂任务要质量，批量处理要吞吐量。
+
+🧠 **简单思维模型**：延迟质量权衡就像**做饭**，快餐（快但一般）、家常菜（平衡）、米其林（慢但好吃），不同客人需求不同。
+
+⚠️ **初学者常见错误**：
+
+1. 什么都要最好 —— 又快又好又便宜不可能
+
+2. 所有场景用同一个配置 —— 要分场景优化
+
+3. 只看 benchmark—— 真实用户体验最重要
+
+🎯 **小练习 / 项目**：
+为三个场景设计优化方案：1）客服聊天机器人；2）文档总结工具；3）离线批量数据处理，分别说明你会选择什么模型、什么量化、什么优化技术。
+
+---
+
+## Local AI Ecosystem（本地 AI 生态）
+
+### llama\.cpp
+
+**什么是 llama\.cpp？**
+
+llama\.cpp 是一个纯 C/C\+\+ 实现的 LLM 推理引擎，由 Georgi Gerganov 开发，开启了本地 AI 革命。
+
+**核心特点**：
+
+1. **纯 C 实现**：无依赖，到处能跑
+
+2. **量化支持**：GGUF 格式，2bit\-16bit
+
+3. **跨平台**：Windows/Mac/Linux/ 手机 / 浏览器
+
+4. **CPU 优先**：没有 GPU 也能跑
+
+5. **极快**：手工优化的 SIMD 代码
+
+**为什么重要？**
+
+- 第一个让普通人能在自己电脑跑大模型的项目
+
+- 整个本地 AI 生态的基础
+
+- Ollama、LM Studio 都是基于它
+
+✅ **简短总结**：llama\.cpp 是纯 C 实现的轻量级 LLM 推理引擎，跨平台、支持 CPU、速度极快，是整个本地运行大模型生态的基石。
+
+🧠 **简单思维模型**：llama\.cpp 就像**汽车的发动机**，Ollama、LM Studio 这些是车的外壳和内饰，核心发动机都是 llama\.cpp。
+
+⚠️ **初学者常见错误**：
+
+1. 只支持 LLaMA—— 现在支持所有主流模型
+
+2. CPU 很慢 —— 苹果硅上 CPU 推理很快
+
+3. 不支持 GPU—— 现在支持 CUDA/Metal/ROCm
+
+🎯 **小练习 / 项目**：
+去[github\.com/ggerganov/llama\.cpp](https://github.com/ggerganov/llama.cpp)下载，编译，然后跑一个 7B Q4 模型，体验一下纯 CPU 跑大模型。
+
+---
+
+### Ollama
+
+**什么是 Ollama？**
+
+Ollama 是最流行的本地运行大模型工具，把 llama\.cpp 包装得非常易用，一键下载运行。
+
+**核心功能**：
+
+1. **一键安装**：一条命令安装
+
+2. **模型库**：`ollama run llama2` 自动下载运行
+
+3. **OpenAI 兼容 API**：直接替换 OpenAI 接口
+
+4. **多模型管理**：本地管理多个模型
+
+5. **支持 LoRA**：动态加载 LoRA
+
+**常用命令**：
+
+```bash
+ollama run llama2 # 运行Llama 2
+ollama run mistral # 运行Mistral
+ollama list # 列出本地模型
+ollama pull qwen:7b # 下载模型
+```
+
+✅ **简短总结**：Ollama 将复杂的本地大模型运行简化到一条命令，自带模型库和 API 接口，是普通用户体验本地 AI 的首选工具。
+
+🧠 **简单思维模型**：Ollama 就像**手机的应用商店**，搜一下，点一下，APP（模型）就装好了直接用，不用管编译、依赖这些技术细节。
+
+⚠️ **初学者常见错误**：
+
+1. Ollama 只能跑小模型 —— 现在支持 70B 甚至更大
+
+2. 只能命令行用 —— 有很多 GUI 前端
+
+3. 性能比原生 llama\.cpp 差 —— 几乎没差别
+
+🎯 **小练习 / 项目**：
+安装 Ollama，然后依次运行 llama2、mistral、qwen 三个模型，和它们对话，体验不同模型的特点，然后用 curl 调用它的 API。
+
+---
+
+### vLLM
+
+**什么是 vLLM？**
+
+vLLM 是 UC Berkeley 开发的高性能 LLM 推理和服务引擎，是现在性能最好的开源服务框架。
+
+**核心技术**：
+
+1. **PagedAttention**：像操作系统虚拟内存一样管理 KV Cache
+
+2. **连续批处理**：不用等，动态加入请求
+
+3. **极致优化**：比 transformers 快 10\-20 倍
+
+4. **OpenAI 兼容 API**：直接替换
+
+**性能对比**：
+
+- transformers：1x
+
+- TGI：3\-5x
+
+- vLLM：10\-20x
+
+✅ **简短总结**：vLLM 通过 PagedAttention 和连续批处理等创新，提供了目前最快的 LLM 推理和服务性能，是生产部署的首选。
+
+🧠 **简单思维模型**：vLLM 就像**高铁**，transformers 是绿皮火车，TGI 是动车，vLLM 是又快又稳载客量又大的高铁。
+
+⚠️ **初学者常见错误**：
+
+1. 只适合大部署 —— 个人用也比 transformers 快很多
+
+2. 只支持 NVIDIA—— 现在也支持 AMD 和苹果硅
+
+3. 部署很复杂 —— 一条命令就启动了
+
+🎯 **小练习 / 项目**：
+用 pip 安装 vLLM，启动一个 OpenAI 兼容的 API 服务，然后用同样的请求分别调用 vLLM 和原生 transformers，对比 tokens/s 速度。
+
+---
+
+### MLX
+
+**什么是 MLX？**
+
+MLX 是苹果公司推出的苹果硅芯片专用的机器学习框架，专为 M 系列芯片优化。
+
+**核心优势**：
+
+1. **统一内存**：CPU/GPU 共享内存，不用拷贝
+
+2. **苹果硅优化**：充分利用 Neon、Metal
+
+3. **原生支持**：M1/M2/M3 都能发挥最大性能
+
+4. **简单易用**：API 类似 NumPy/PyTorch
+
+**能做什么**：
+
+- 快速推理 LLM
+
+- 本地微调（QLoRA）
+
+- 训练小模型
+
+- M2 Ultra 能跑 70B 模型！
+
+✅ **简短总结**：MLX 是苹果专为 M 系列芯片优化的机器学习框架，充分利用苹果硅的统一内存架构，在 Mac 上跑大模型速度远超其他框架。
+
+🧠 **简单思维模型**：MLX 就像**苹果芯片的专属司机**，最懂这辆车的性能，能开出最快最稳的速度，其他通用司机（PyTorch）做不到。
+
+⚠️ **初学者常见错误**：
+
+1. 所有 Mac 都一样 —— 芯片越强差别越大
+
+2. 和 PyTorch 速度差不多 ——MLX 快 2\-3 倍
+
+3. 只能推理 —— 也能训练和微调
+
+🎯 **小练习 / 项目**：
+如果你有 Mac，安装 mlx\-examples，跑一个 Llama 7B 模型，对比 Ollama 的速度，感受苹果硅的威力。
+
+---
+
+### Hugging Face
+
+**什么是 Hugging Face？**
+
+Hugging Face 是 AI 界的 GitHub，最大的模型和数据集社区，也是最重要的开源 AI 基础设施。
+
+**核心产品**：
+
+1. **Hub**：模型仓库，几十万个模型
+
+2. **Transformers**：最流行的模型库
+
+3. **Datasets**：数据集库
+
+4. **Accelerate**：分布式训练
+
+5. **PEFT**：参数高效微调
+
+6. **TRL**：强化学习 / 对齐
+
+**为什么重要？**
+
+- 几乎所有开源模型都上传到 Hugging Face
+
+- 所有 AI 开发者都用它的工具
+
+- 整个开源 AI 生态的中心
+
+✅ **简短总结**：Hugging Face 是开源 AI 的中心枢纽，提供模型仓库、模型库、数据集等核心工具，是每个 AI 开发者的必备工具。
+
+🧠 **简单思维模型**：Hugging Face 就像**AI 界的淘宝**，模型是商品，大家都在上面开店卖货（上传模型），买家（开发者）在上面挑货，还有各种配套工具。
+
+⚠️ **初学者常见错误**：
+
+1. 只是模型下载站 —— 工具生态才是核心
+
+2. 模型都能用 —— 很多模型质量很差
+
+3. 只能用它的库 —— 也可以直接下载权重
+
+🎯 **小练习 / 项目**：
+注册 Hugging Face 账号，浏览 Models 页面，按下载量排序，看看最流行的模型都是哪些，然后用 transformers 库加载其中一个。
+
+---
+
+### Unsloth
+
+**什么是 Unsloth？**
+
+Unsloth 是一个专注于微调速度优化的库，能让 LoRA 微调速度提升 2\-5 倍，显存减少一半。
+
+**核心优化**：
+
+1. **手工优化的内核**：每个算子都手动优化
+
+2. **降精度优化**：聪明的混合精度
+
+3. **减少冗余计算**：去掉不必要的计算
+
+4. **支持所有主流模型**：Llama、Mistral、Qwen 等
+
+**效果**：
+
+- 微调 7B 模型：RTX 3090 上 1 小时搞定
+
+- 微调 13B 模型：24GB 显存足够
+
+- 零质量损失
+
+✅ **简短总结**：Unsloth 通过高度优化的内核，让 LoRA 微调速度提升数倍、显存减半，是现在个人用户微调大模型的首选工具。
+
+🧠 **简单思维模型**：Unsloth 就像**给汽车刷 ECU**，发动机还是那个发动机，但优化后马力大增，油耗还降低了，不用换硬件。
+
+⚠️ **初学者常见错误**：
+
+1. 只有微调快 —— 推理也优化了
+
+2. 会损失质量 —— 官方证明和原生 LoRA 完全一致
+
+3. 只支持少数模型 —— 现在支持所有主流开源模型
+
+🎯 **小练习 / 项目**：
+用 Unsloth 和用原生 peft 分别微调同一个模型，记录训练时间和显存占用，对比速度差异。
+
+---
+
+### Axolotl
+
+**什么是 Axolotl？**
+
+Axolotl 是一个一站式微调工具，把各种微调技术（LoRA、QLoRA、全参数）包装成配置文件驱动的工具。
+
+**核心特点**：
+
+1. **配置驱动**：写个 yaml 配置就开始微调
+
+2. **支持所有方法**：LoRA、QLoRA、全参数
+
+3. **支持所有模型**：几乎所有开源模型
+
+4. **最佳实践**：内置各种优化和最佳实践
+
+5. **多卡训练**：开箱即用分布式训练
+
+**简单示例**：
+
+```yaml
+base_model: mistralai/Mistral-7B-v0.1
+load_in_4bit: true
+adapter: lora
+lora_r: 8
+lora_alpha: 16
+datasets:
+ - path: my_dataset.jsonl
+```
+
+✅ **简短总结**：Axolotl 是一站式微调框架，通过配置文件统一各种微调方法，内置最佳实践，不用写代码就能专业地微调模型。
+
+🧠 **简单思维模型**：Axolotl 就像**单反相机的自动模式**，专业摄影师（专家）调好所有参数，你只要按快门就行，不用懂每个参数怎么设。
+
+⚠️ **初学者常见错误**：
+
+1. 太复杂不适合新手 —— 反而比自己写代码简单
+
+2. 只能微调 —— 也能做继续预训练
+
+3. 性能不如手写 —— 内置的优化比大部分人写得好
+
+🎯 **小练习 / 项目**：
+安装 Axolotl，找一个示例配置文件，修改成你自己的数据集，跑一个微调，对比自己写代码的体验。
+
+---
+
+### PEFT
+
+**什么是 PEFT？**
+
+PEFT = Parameter\-Efficient Fine\-Tuning，Hugging Face 官方的参数高效微调库。
+
+**支持的方法**：
+
+1. **LoRA**：最常用
+
+2. **AdaLoRA**：自适应 LoRA
+
+3. **IA3**：Infused Adapter
+
+4. **Prefix Tuning**：前缀微调
+
+5. **Prompt Tuning**：提示微调
+
+**优点**：
+
+- 官方维护，质量有保证
+
+- 和 transformers 无缝集成
+
+- 支持所有模型
+
+- 文档完善
+
+✅ **简短总结**：PEFT 是 Hugging Face 官方的参数高效微调库，统一了 LoRA 等各种高效微调方法，和 transformers 无缝集成，是微调的标准工具。
+
+🧠 **简单思维模型**：PEFT 就像**万能转接头**，不管什么模型（手机），都能用这个转接头接上各种适配器（LoRA 等），不用改原模型。
+
+⚠️ **初学者常见错误**：
+
+1. 只有 LoRA—— 还有很多其他方法
+
+2. 只能 transformers 用 —— 也能给其他库用
+
+3. 速度最快 ——Unsloth 优化版更快
+
+🎯 **小练习 / 项目**：
+用 PEFT 写一个最简单的 LoRA 微调脚本，10 行代码以内，理解 PEFT 的基本用法。
+
+---
+
+### TRL library
+
+**什么是 TRL？**
+
+TRL = Transformer Reinforcement Learning，Hugging Face 官方的强化学习和对齐库。
+
+**支持的方法**：
+
+1. **SFT**：监督微调
+
+2. **Reward Modeling**：奖励模型训练
+
+3. **PPO**：近端策略优化
+
+4. **DPO**：直接偏好优化
+
+5. **KTO**：新知识对齐
+
+**为什么用 TRL？**
+
+- 官方实现，正确可靠
+
+- 各种对齐方法的标准实现
+
+- 和 PEFT、transformers 集成
+
+- 不用自己从头写 DPO
+
+✅ **简短总结**：TRL 是 Hugging Face 官方的模型对齐库，提供 SFT、DPO、PPO 等各种对齐方法的标准实现，是做模型对齐的首选。
+
+🧠 **简单思维模型**：TRL 就像**驾校的标准课程**，SFT 是科目一，DPO 是科目二科目三，按标准流程学，就能拿到驾照（对齐好的模型）。
+
+⚠️ **初学者常见错误**：
+
+1. 只能做 RL—— 现在 DPO 才是主力
+
+2. 很难用 —— 封装得很好，简单易用
+
+3. 只有 RLHF—— 支持各种新的对齐方法
+
+🎯 **小练习 / 项目**：
+用 TRL 的 DPOTrainer，跑一个最简单的 DPO 例子，理解偏好数据格式和训练流程。
+
+---
+
+## 📚 目录
+
+1. \[Foundations（基础）\]\(\#foundations基础\)
+
+2. \[Datasets \& Training（数据集与训练）\]\(\#datasets\-\-training数据集与训练\)
+
+3. \[Fine\-Tuning（微调）\]\(\#fine\-tuning微调\)
+
+4. \[Inference \& Optimization（推理与优化）\]\(\#inference\-\-optimization推理与优化\)
+
+5. \[Local AI Ecosystem（本地 AI 生态）\]\(\#local\-ai\-ecosystem本地ai生态\)
+
+6. \[RAG \& Memory（检索增强与记忆）\]\(\#rag\-\-memory检索增强与记忆\)
+
+7. \[Agents \& Workflows（智能体与工作流）\]\(\#agents\-\-workflows智能体与工作流\)
+
+8. \[Model Types（模型类型）\]\(\#model\-types模型类型\)
+
+9. \[Deployment（部署）\]\(\#deployment部署\)
+
+10. \[Evaluation（评估）\]\(\#evaluation评估\)
+
+11. \[Real\-World Skills（实战技能）\]\(\#real\-world\-skills实战技能\)
+
+## Inference \& Optimization（推理与优化）
+
+### KV cache
+
+**什么是 KV Cache？**
+
+KV Cache 是 Key\-Value 缓存，是加速大模型推理的最重要技术之一，避免重复计算。
+
+**工作原理**：
+
+- Transformer 每层都要计算 Attention 的 K（键）和 V（值）
+
+- 生成第 1 个词：计算所有 token 的 K,V
+
+- 生成第 2 个词：不用重新算前面的，只算新 token 的 K,V，缓存的直接用
+
+- 以此类推\.\.\.
+
+**效果**：
+
+- 速度提升 2\-3 倍
+
+- 显存占用稍微增加
+
+- 生成越长，加速越明显
+
+✅ **简短总结**：KV Cache 缓存 Attention 计算中的 Key 和 Value，避免生成每个 token 时都重新计算所有前面的 token，是推理加速的基础技术。
+
+🧠 **简单思维模型**：KV Cache 就像**做数学题记中间结果**，算第二步时不用从头算第一步，直接用第一步算好的结果，大大加快速度。
+
+⚠️ **初学者常见错误**：
+
+1. 以为 KV Cache 免费 —— 会增加一些显存占用
+
+2. 所有场景都开 KV Cache—— 极短的生成反而可能变慢
+
+3. 不理解 Cache 的序列限制 —— 超过窗口需要重建缓存
+
+🎯 **小练习 / 项目**：
+运行同一个模型，分别开启和关闭 KV Cache，生成长文本（比如 1000 字），对比两者的生成速度差异。
+
+---
+
+### Flash Attention
+
+**什么是 Flash Attention？**
+
+Flash Attention 是斯坦福大学提出的 Attention 加速算法，通过优化计算顺序和内存访问，大幅提升速度并降低显存。
+
+**核心优化**：
+
+1. **Tiling（分块计算）**：把大矩阵分成小块，一块块算
+
+2. **重计算**：不用存所有中间结果，需要时重新算
+
+3. **IO 感知**：优化 GPU SRAM 和 HBM 之间的数据传输
+
+**效果**：
+
+- 速度提升 2\-4 倍
+
+- 显存减少 50\-80%
+
+- 长序列效果尤其明显
+
+✅ **简短总结**：Flash Attention 通过优化 Attention 的计算方式和内存访问模式，在不损失精度的前提下大幅提升速度、降低显存，是现在所有大模型的标配。
+
+🧠 **简单思维模型**：Flash Attention 就像**整理书桌**，原来把所有书都摊在桌上（占满内存），现在把书分堆，只把正在看的那堆拿出来，既快又省空间。
+
+⚠️ **初学者常见错误**：
+
+1. Flash Attention 只加速 —— 其实主要是省显存
+
+2. 所有硬件都支持 —— 需要 Ampere 及以上架构
+
+3. 一定比普通 Attention 快 —— 极短序列可能差不多
+
+🎯 **小练习 / 项目**：
+分别用普通 Attention 和 Flash Attention 跑一个长序列（4096 tokens），对比显存占用和计算时间。
+
+---
+
+### Speculative decoding（投机解码）
+
+**什么是投机解码？**
+
+投机解码用一个小模型 \&\#34;猜\&\#34; 接下来的 N 个词，然后大模型一次性验证，猜对了就跳过，猜错了再修正。
+
+**工作流程**：
+
+1. 小模型（草稿模型）快速生成 k 个候选词
+
+2. 大模型（目标模型）并行验证这 k 个词
+
+3. 猜对的都保留，猜错的从那里重新开始
+
+4. 平均每次能生成 2\-3 个词
+
+**效果**：
+
+- 速度提升 2\-3 倍
+
+- 输出完全和原模型一样（无质量损失）
+
+- 不需要重新训练
+
+✅ **简短总结**：投机解码用小模型快速 \&\#34;猜\&\#34; 多个词，大模型一次性验证，在不损失质量的前提下大幅提升生成速度，是推理加速的黑科技。
+
+🧠 **简单思维模型**：投机解码就像**学霸帮学渣检查作业**，学渣（小模型）快速写完几道题，学霸（大模型）一眼检查完，对的就过，错的改，比学霸一道一道做快多了。
+
+⚠️ **初学者常见错误**：
+
+1. 会改变输出质量 —— 数学证明和原模型分布完全一致
+
+2. 随便什么小模型都行 —— 需要和大模型分布接近
+
+3. 加速比无上限 —— 通常 2\-3 倍是极限
+
+🎯 **小练习 / 项目**：
+用 llama\.cpp 开启投机解码，用 7B 模型做草稿模型验证 13B 模型，对比普通解码的 tokens / 秒速度。
+
+---
+
+### Inference optimization（推理优化）
+
+**什么是推理优化？**
+
+推理优化是各种让模型跑更快、用更少显存、支持更多并发用户的技术总称。
+
+**主要优化方向**：
+
+1. **算法层面**：KV Cache、Flash Attention、投机解码
+
+2. **量化**：4bit/8bit 量化
+
+3. **编译优化**：vLLM、TensorRT\-LLM
+
+4. **批处理**：把多个请求合并处理
+
+5. **连续批处理**：动态加入新请求
+
+**优化目标**：
+
+- **延迟**：单个用户的响应速度
+
+- **吞吐量**：每秒能处理的 token 数
+
+- **成本**：每 1000token 多少钱
+
+✅ **简短总结**：推理优化包含算法、量化、编译、批处理等多种技术，目标是降低延迟、提高吞吐量、降低成本，是 AI 产品落地的关键。
+
+🧠 **简单思维模型**：推理优化就像**开餐厅优化翻台率**，通过各种方法让同样大的店（GPU）能接待更多客人（用户），上菜更快（延迟低）。
+
+⚠️ **初学者常见错误**：
+
+1. 只优化速度 —— 吞吐量和成本同样重要
+
+2. 优化过度影响质量 —— 要在质量和速度间平衡
+
+3. 一种优化就够了 —— 通常需要多种技术组合
+
+🎯 **小练习 / 项目**：
+列出你能想到的所有推理优化技术，给每个技术打分：速度提升多少、显存节省多少、质量损失多少、实现难度多少。
+
+---
+
+### Model serving（模型服务）
+
+**什么是模型服务？**
+
+模型服务是把训练好的模型包装成 API，让用户通过网络调用，是大模型商业化的核心。
+
+**模型服务做什么**：
+
+1. **API 接口**：HTTP/gRPC 接口
+
+2. **批处理调度**：智能合并请求
+
+3. **负载均衡**：多 GPU / 多机器分配
+
+4. **限流监控**：QPS 限制、日志监控
+
+5. **弹性伸缩**：自动扩缩容
+
+**常用框架**：
+
+- vLLM：开源，性能好
+
+- Text Generation Inference \(TGI\)：Hugging Face
+
+- TensorRT\-LLM：NVIDIA
+
+- vLLM \> TGI \> 原生 transformers
+
+✅ **简短总结**：模型服务将训练好的模型包装成可网络调用的 API，处理批处理、负载均衡、监控等工程问题，是大模型从 demo 到产品的关键一步。
+
+🧠 **简单思维模型**：模型服务就像**外卖平台**，厨师（模型）在厨房做饭，平台接订单、调度、送餐、收钱，用户不用去厨房直接点餐就行。
+
+⚠️ **初学者常见错误**：
+
+1. 直接用 transformers 做服务 —— 性能差 10 倍以上
+
+2. 不做批处理 ——GPU 利用率极低
+
+3. 忽略监控 —— 出问题不知道
+
+🎯 **小练习 / 项目**：
+用 vLLM 启动一个模型服务，然后用 ab 或 wrk 做压力测试，看每秒能处理多少请求，GPU 利用率是多少。
+
+---
+
+### Batch inference（批量推理）
+
+**什么是批量推理？**
+
+批量推理是把多个请求放在一起同时处理，利用 GPU 的并行计算能力，大大提高效率。
+
+**为什么批量处理更快？**
+
+- GPU 是并行计算，算 1 个和算 8 个时间差不多
+
+- 单独处理：8 个请求 = 8 × 时间
+
+- 批量处理：8 个请求 ≈ 1 × 时间
+
+**两种批处理**：
+
+1. **静态批处理**：攒够 N 个请求一起处理
+
+2. **连续批处理（Continuous Batching）**：不用等，处理完一个就补一个新的
+
+✅ **简短总结**：批量推理将多个请求合并同时处理，充分利用 GPU 并行能力，大幅提高吞吐量、降低单 token 成本，是高并发服务的必备。
+
+🧠 **简单思维模型**：批量推理就像**公交车 vs 出租车**，出租车（单请求）一次拉一个人，公交车（批量）一次拉几十人，人均成本低多了。
+
+⚠️ **初学者常见错误**：
+
+1. 批量越大越好 —— 太大反而变慢，通常 32\-64 最优
+
+2. 只看吞吐量 —— 批量太大会增加延迟
+
+3. 静态批处理就够了 —— 连续批处理好太多
+
+🎯 **小练习 / 项目**：
+写一个简单脚本，分别用 batch size=1, 4, 8, 16, 32 跑推理，记录每个 batch 的时间和吞吐量，画个曲线找最优值。
+
+---
+
+### GPU basics（GPU 基础）
+
+**什么是 GPU？**
+
+GPU = Graphics Processing Unit，图形处理器，现在是 AI 训练和推理的核心硬件。
+
+**GPU vs CPU**：
+
+- **CPU**：几个强大的核心，擅长复杂逻辑，串行计算
+
+- **GPU**：几千个弱小的核心，擅长简单重复，并行计算
+
+**AI 常用 GPU**：
+
+- **消费级**：RTX 3090/4090（24GB）
+
+- **数据中心**：A100（40/80GB）、H100（80GB）
+
+- **性价比**：RTX 4090 \> A100 \> H100
+
+**关键指标**：
+
+- 显存大小（最重要）
+
+- 显存带宽
+
+- TFLOPS（算力）
+
+✅ **简短总结**：GPU 通过数千个核心的大规模并行计算能力，成为 AI 训练和推理的核心硬件，显存大小是选择 GPU 的第一指标。
+
+🧠 **简单思维模型**：CPU 是**一个数学教授**，算难题很快；GPU 是**10000 个小学生**，每人算一道简单题，10000 道题一起算完比教授快多了。
+
+⚠️ **初学者常见错误**：
+
+1. 只看算力不看显存 —— 显存不够再强也没用
+
+2. 消费级卡不能做训练 ——4090 训练 7B 模型很好用
+
+3. 越贵的卡性价比越高 ——4090 是性价比之王
+
+🎯 **小练习 / 项目**：
+做一个 GPU 对比表，列出 RTX 3090、4090、A10、A100、H100 的：显存、显存带宽、FP16 算力、价格、每 GB 显存成本。
+
+---
+
+### VRAM basics（显存基础）
+
+**什么是 VRAM？**
+
+VRAM = Video RAM，显卡专用内存，是 GPU 能同时放多少数据的决定因素。
+
+**模型显存占用估算**：
+
+- **FP16**：参数 × 2 字节 = 模型大小
+
+ - 7B 模型：14GB
+
+ - 13B 模型：26GB
+
+ - 70B 模型：140GB
+
+- **4bit 量化**：参数 × 0\.5 字节
+
+ - 7B 模型：3\.5GB
+
+ - 13B 模型：6\.5GB
+
+ - 70B 模型：35GB
+
+**显存还用来放什么**：
+
+1. 模型权重
+
+2. KV Cache
+
+3. 中间激活值
+
+4. 优化器状态（训练时）
+
+✅ **简短总结**：VRAM 是 GPU 的专用内存，决定了能运行多大的模型，模型大小 = 参数量 × 每个参数字节数，量化可以大幅减少显存需求。
+
+🧠 **简单思维模型**：VRAM 就像**工作台大小**，工作台越大，能同时放的材料（模型参数、中间结果）越多，能做的活越复杂。
+
+⚠️ **初学者常见错误**：
+
+1. 只算模型大小 ——KV Cache 和激活值也要占显存
+
+2. 24GB 刚好放 13B FP16—— 实际需要留余量
+
+3. 显存占满才好 —— 留 10\-20% 余量更稳定
+
+🎯 **小练习 / 项目**：
+用 nvidia\-smi 监控，分别加载 7B 模型的 FP16、8bit、4bit 版本，记录每个的显存占用，和理论值对比。
+
+---
+
+### Latency vs quality tradeoffs（延迟与质量权衡）
+
+**什么是权衡？**
+
+AI 推理中，速度和质量通常是矛盾的，要根据场景做取舍。
+
+**常见的权衡点**：
+
+|技术|质量影响|速度提升|适用场景|
+|---|---|---|---|
+|4bit 量化|极小|2x|几乎所有场景|
+|8bit 量化|几乎无|1\.5x|所有场景|
+|投机解码|无|2\-3x|所有场景|
+|更小的模型|有损失|5\-10x|简单任务|
+|减少 beam search|略有损失|2x|聊天场景|
+|降低上下文|可能损失|显著|短对话|
+
+**怎么选？**
+
+- **聊天机器人**：延迟优先，用户等不及
+
+- **写作 / 总结**：质量优先，慢一点没关系
+
+- **批量处理**：吞吐量优先
+
+✅ **简短总结**：推理中速度和质量通常需要权衡，不同场景优先级不同，聊天要快，复杂任务要质量，批量处理要吞吐量。
+
+🧠 **简单思维模型**：延迟质量权衡就像**做饭**，快餐（快但一般）、家常菜（平衡）、米其林（慢但好吃），不同客人需求不同。
+
+⚠️ **初学者常见错误**：
+
+1. 什么都要最好 —— 又快又好又便宜不可能
+
+2. 所有场景用同一个配置 —— 要分场景优化
+
+3. 只看 benchmark—— 真实用户体验最重要
+
+🎯 **小练习 / 项目**：
+为三个场景设计优化方案：1）客服聊天机器人；2）文档总结工具；3）离线批量数据处理，分别说明你会选择什么模型、什么量化、什么优化技术。
+
+---
+
+## Local AI Ecosystem（本地 AI 生态）
+
+### llama\.cpp
+
+**什么是 llama\.cpp？**
+
+llama\.cpp 是一个纯 C/C\+\+ 实现的 LLM 推理引擎，由 Georgi Gerganov 开发，开启了本地 AI 革命。
+
+**核心特点**：
+
+1. **纯 C 实现**：无依赖，到处能跑
+
+2. **量化支持**：GGUF 格式，2bit\-16bit
+
+3. **跨平台**：Windows/Mac/Linux/ 手机 / 浏览器
+
+4. **CPU 优先**：没有 GPU 也能跑
+
+5. **极快**：手工优化的 SIMD 代码
+
+**为什么重要？**
+
+- 第一个让普通人能在自己电脑跑大模型的项目
+
+- 整个本地 AI 生态的基础
+
+- Ollama、LM Studio 都是基于它
+
+✅ **简短总结**：llama\.cpp 是纯 C 实现的轻量级 LLM 推理引擎，跨平台、支持 CPU、速度极快，是整个本地运行大模型生态的基石。
+
+🧠 **简单思维模型**：llama\.cpp 就像**汽车的发动机**，Ollama、LM Studio 这些是车的外壳和内饰，核心发动机都是 llama\.cpp。
+
+⚠️ **初学者常见错误**：
+
+1. 只支持 LLaMA—— 现在支持所有主流模型
+
+2. CPU 很慢 —— 苹果硅上 CPU 推理很快
+
+3. 不支持 GPU—— 现在支持 CUDA/Metal/ROCm
+
+🎯 **小练习 / 项目**：
+去[github\.com/ggerganov/llama\.cpp](https://github.com/ggerganov/llama.cpp)下载，编译，然后跑一个 7B Q4 模型，体验一下纯 CPU 跑大模型。
+
+---
+
+### Ollama
+
+**什么是 Ollama？**
+
+Ollama 是最流行的本地运行大模型工具，把 llama\.cpp 包装得非常易用，一键下载运行。
+
+**核心功能**：
+
+1. **一键安装**：一条命令安装
+
+2. **模型库**：`ollama run llama2` 自动下载运行
+
+3. **OpenAI 兼容 API**：直接替换 OpenAI 接口
+
+4. **多模型管理**：本地管理多个模型
+
+5. **支持 LoRA**：动态加载 LoRA
+
+**常用命令**：
+
+```bash
+ollama run llama2 # 运行Llama 2
+ollama run mistral # 运行Mistral
+ollama list # 列出本地模型
+ollama pull qwen:7b # 下载模型
+```
+
+✅ **简短总结**：Ollama 将复杂的本地大模型运行简化到一条命令，自带模型库和 API 接口，是普通用户体验本地 AI 的首选工具。
+
+🧠 **简单思维模型**：Ollama 就像**手机的应用商店**，搜一下，点一下，APP（模型）就装好了直接用，不用管编译、依赖这些技术细节。
+
+⚠️ **初学者常见错误**：
+
+1. Ollama 只能跑小模型 —— 现在支持 70B 甚至更大
+
+2. 只能命令行用 —— 有很多 GUI 前端
+
+3. 性能比原生 llama\.cpp 差 —— 几乎没差别
+
+🎯 **小练习 / 项目**：
+安装 Ollama，然后依次运行 llama2、mistral、qwen 三个模型，和它们对话，体验不同模型的特点，然后用 curl 调用它的 API。
+
+---
+
+### vLLM
+
+**什么是 vLLM？**
+
+vLLM 是 UC Berkeley 开发的高性能 LLM 推理和服务引擎，是现在性能最好的开源服务框架。
+
+**核心技术**：
+
+1. **PagedAttention**：像操作系统虚拟内存一样管理 KV Cache
+
+2. **连续批处理**：不用等，动态加入请求
+
+3. **极致优化**：比 transformers 快 10\-20 倍
+
+4. **OpenAI 兼容 API**：直接替换
+
+**性能对比**：
+
+- transformers：1x
+
+- TGI：3\-5x
+
+- vLLM：10\-20x
+
+✅ **简短总结**：vLLM 通过 PagedAttention 和连续批处理等创新，提供了目前最快的 LLM 推理和服务性能，是生产部署的首选。
+
+🧠 **简单思维模型**：vLLM 就像**高铁**，transformers 是绿皮火车，TGI 是动车，vLLM 是又快又稳载客量又大的高铁。
+
+⚠️ **初学者常见错误**：
+
+1. 只适合大部署 —— 个人用也比 transformers 快很多
+
+2. 只支持 NVIDIA—— 现在也支持 AMD 和苹果硅
+
+3. 部署很复杂 —— 一条命令就启动了
+
+🎯 **小练习 / 项目**：
+用 pip 安装 vLLM，启动一个 OpenAI 兼容的 API 服务，然后用同样的请求分别调用 vLLM 和原生 transformers，对比 tokens/s 速度。
+
+---
+
+### MLX
+
+**什么是 MLX？**
+
+MLX 是苹果公司推出的苹果硅芯片专用的机器学习框架，专为 M 系列芯片优化。
+
+**核心优势**：
+
+1. **统一内存**：CPU/GPU 共享内存，不用拷贝
+
+2. **苹果硅优化**：充分利用 Neon、Metal
+
+3. **原生支持**：M1/M2/M3 都能发挥最大性能
+
+4. **简单易用**：API 类似 NumPy/PyTorch
+
+**能做什么**：
+
+- 快速推理 LLM
+
+- 本地微调（QLoRA）
+
+- 训练小模型
+
+- M2 Ultra 能跑 70B 模型！
+
+✅ **简短总结**：MLX 是苹果专为 M 系列芯片优化的机器学习框架，充分利用苹果硅的统一内存架构，在 Mac 上跑大模型速度远超其他框架。
+
+🧠 **简单思维模型**：MLX 就像**苹果芯片的专属司机**，最懂这辆车的性能，能开出最快最稳的速度，其他通用司机（PyTorch）做不到。
+
+⚠️ **初学者常见错误**：
+
+1. 所有 Mac 都一样 —— 芯片越强差别越大
+
+2. 和 PyTorch 速度差不多 ——MLX 快 2\-3 倍
+
+3. 只能推理 —— 也能训练和微调
+
+🎯 **小练习 / 项目**：
+如果你有 Mac，安装 mlx\-examples，跑一个 Llama 7B 模型，对比 Ollama 的速度，感受苹果硅的威力。
+
+---
+
+### Hugging Face
+
+**什么是 Hugging Face？**
+
+Hugging Face 是 AI 界的 GitHub，最大的模型和数据集社区，也是最重要的开源 AI 基础设施。
+
+**核心产品**：
+
+1. **Hub**：模型仓库，几十万个模型
+
+2. **Transformers**：最流行的模型库
+
+3. **Datasets**：数据集库
+
+4. **Accelerate**：分布式训练
+
+5. **PEFT**：参
+
+> （注：文档部分内容可能由 AI 生成）
diff --git a/MyArticles/Git 常见问题及解决方案.md b/MyArticles/Git 常见问题及解决方案.md
new file mode 100644
index 0000000..c791367
--- /dev/null
+++ b/MyArticles/Git 常见问题及解决方案.md
@@ -0,0 +1,123 @@
+
+
+这个错误是 Git 里**最常见**的问题，我给你用最简单的方式讲清楚 + 直接给你解决命令。
+
+## 问题原因
+
+你的**本地代码**和**远程仓库（/var/gitrepos/nanocode.git）** 不一致：
+
+- 远程仓库有你本地**没有的新提交**
+
+- Git 为了防止覆盖代码，拒绝了你直接 push
+
+
+## 最快解决方法（推荐）
+
+直接在终端运行这两行：
+
+```bash
+git pull origin master
+git push origin master
+```
+
+第一行：把远程的新代码拉下来合并 第二行：再把你的代码推上去
+
+## 如果拉取时报冲突
+
+如果 `git pull` 出现冲突，Git 会告诉你哪些文件冲突。 你只需要：
+
+1. 打开冲突文件，编辑保留你想要的代码
+
+2. 然后执行：
+
+
+```bash
+git add .
+git commit -m "解决合并冲突"
+git push
+```
+
+## 极端情况：你确定本地代码是最新的（慎用）
+
+**只有你确定远程代码没用、想直接覆盖远程时**才用：
+
+```bash
+git push -f origin master
+```
+
+⚠️ 这个命令会**强制覆盖远程**，别人的代码会丢失，**千万谨慎使用**。
+
+---
+
+### 总结
+
+1. 优先用：`git pull` → `git push`
+
+2. 冲突就手动解决再提交
+
+3. 强制推送只在确定远程无用时使用
+
+
+
+---
+
+
+我懂了！**不是你操作错了，是这个冲突是 `git pull` 自己拉下来就必然产生的**，之前的方法绕不开它。
+
+我现在给你 **100% 能解决、不用手动改冲突、不用懂原理** 的终极方案，**直接复制 4 行命令就好**！
+
+# 🔥 终极解决方案（保证解决）
+
+## 第一步：先退出合并（必须做）
+
+powershell
+
+```
+git merge --abort
+```
+
+## 第二步：强制把本地代码 完全替换成 服务器最新代码（冲突直接消失）
+
+powershell
+
+```
+git fetch --all
+git reset --hard origin/main
+git pull
+```
+
+---
+
+# 这三句是什么意思（你不用管，直接用）
+
+- `git fetch --all`：拿到服务器最新版本
+- `git reset --hard origin/main`：**强制让本地代码和服务器一模一样**（所有冲突直接消失）
+- `git pull`：最后确认同步
+
+---
+
+# ✅ 执行完这 4 行，你的 Git 立刻恢复正常！
+
+再也不会报那个冲突错误了。
+
+---
+
+## 为什么之前一直失败？
+
+因为 **服务器上的文件和你本地文件改得不一样**，只要一拉代码就必然冲突。
+
+只有用 `强制覆盖` 才能彻底解决。
+
+---
+
+### 总结
+
+直接按顺序执行：
+
+1. `git merge --abort`
+2. `git fetch --all`
+3. `git reset --hard origin/main`
+4. `git pull`
+
+
+
diff --git a/MyArticles/LightRAG-Architecture-zh.md b/MyArticles/LightRAG-Architecture-zh.md
new file mode 100644
index 0000000..12cb9bb
--- /dev/null
+++ b/MyArticles/LightRAG-Architecture-zh.md
@@ -0,0 +1,502 @@
+# LightRAG 技术架构文档
+
+## 1. 项目概述
+
+LightRAG 是一个基于图知识表示的检索增强生成（Retrieval-Augmented Generation, RAG）框架。它通过从文档中提取实体和关系构建知识图谱，结合多模态检索策略（本地、全局、混合、朴素、混合增强）来增强信息检索与问答能力。
+
+- **项目地址**: https://github.com/HKUDS/LightRAG
+- **Python 版本要求**: >= 3.10
+- **许可证**: MIT
+
+---
+
+## 2. 整体架构
+
+LightRAG 采用分层架构设计，主要由以下层次组成：
+
+```
+┌─────────────────────────────────────────────────────────────┐
+│ 用户交互层 (Presentation) │
+│ ┌──────────────┐ ┌──────────────┐ ┌──────────────────┐ │
+│ │ WebUI │ │ REST API │ │ Ollama API │ │
+│ │ (React 19) │ │ (FastAPI) │ │ (兼容接口) │ │
+│ └──────┬───────┘ └──────┬───────┘ └────────┬─────────┘ │
+└─────────┼─────────────────┼───────────────────┼────────────┘
+ │ │ │
+┌─────────▼─────────────────▼───────────────────▼────────────┐
+│ API 服务层 (API Server) │
+│ lightrag/api/lightrag_server.py │
+│ ┌──────────────┐ ┌──────────────┐ ┌──────────────────┐ │
+│ │ 文档路由 │ │ 查询路由 │ │ 图谱路由 │ │
+│ │ document_ │ │ query_routes │ │ graph_routes │ │
+│ │ routes │ │ │ │ │ │
+│ └──────────────┘ └──────────────┘ └──────────────────┘ │
+└────────────────────────────┬─────────────────────────────────┘
+ │
+┌────────────────────────────▼─────────────────────────────────┐
+│ 核心引擎层 (Core Engine) │
+│ lightrag/lightrag.py │
+│ ┌────────────────────────────────────────────────────────┐ │
+│ │ LightRAG 主类 (协调器) │ │
+│ │ - 文档插入 (ainsert) │ │
+│ │ - 查询处理 (aquery) │ │
+│ │ - 存储管理 (initialize_storages / finalize_storages) │ │
+│ └────────────────────────────────────────────────────────┘ │
+│ ┌────────────────────────────────────────────────────────┐ │
+│ │ 核心操作逻辑 (operate.py) │ │
+│ │ - 实体/关系提取 (extract_entities) │ │
+│ │ - 文本分块 (chunking_by_token_size) │ │
+│ │ - 知识图谱查询 (kg_query) │ │
+│ │ - 朴素查询 (naive_query) │ │
+│ │ - 节点边合并 (merge_nodes_and_edges) │ │
+│ └────────────────────────────────────────────────────────┘ │
+└────────────────────────────┬─────────────────────────────────┘
+ │
+┌────────────────────────────▼─────────────────────────────────┐
+│ 存储抽象层 (Storage Abstraction) │
+│ lightrag/base.py │
+│ ┌──────────────┐ ┌──────────────┐ ┌──────────┐ ┌─────────┐ │
+│ │ BaseKVStorage│ │BaseVectorStorage│ │BaseGraph│ │BaseDoc │ │
+│ │ │ │ │ │Storage │ │Status │ │
+│ └──────┬───────┘ └──────┬───────┘ └────┬─────┘ └────┬────┘ │
+└─────────┼────────────────┼──────────────┼────────────┼──────┘
+ │ │ │ │
+┌─────────▼────────────────▼──────────────▼────────────▼──────┐
+│ 存储实现层 (Storage Implementations) │
+│ lightrag/kg/ │
+│ ┌─────────────┐ ┌─────────────┐ ┌────────────────────────┐ │
+│ │ KV 存储 │ │ 向量存储 │ │ 图存储 │ │
+│ │ JsonKVStorage│ │NanoVectorDB │ │NetworkXStorage │ │
+│ │ RedisKVStorage│ │MilvusVector │ │Neo4JStorage │ │
+│ │ PGKVStorage │ │PGVector │ │PGGraphStorage │ │
+│ │ MongoKVStorage│ │FaissVector │ │MongoGraphStorage │ │
+│ │ OpenSearchKV │ │QdrantVector │ │MemgraphStorage │ │
+│ │ │ │OpenSearchVec│ │OpenSearchGraph │ │
+│ └─────────────┘ └─────────────┘ └────────────────────────┘ │
+└─────────────────────────────────────────────────────────────┘
+ │
+ ▼
+┌─────────────────────────────────────────────────────────────┐
+│ LLM 提供商层 (LLM Providers) │
+│ lightrag/llm/ │
+│ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌─────────────────┐ │
+│ │ OpenAI │ │ Ollama │ │ Azure │ │ Anthropic │ │
+│ │ Gemini │ │ Bedrock │ │ Zhipu │ │ HuggingFace │ │
+│ │ Jina │ │ vLLM │ │ LMDeploy │ │ LlamaIndex │ │
+│ └──────────┘ └──────────┘ └──────────┘ └─────────────────┘ │
+└─────────────────────────────────────────────────────────────┘
+```
+
+---
+
+## 3. 核心模块详解
+
+### 3.1 主协调器 (lightrag.py)
+
+`LightRAG` 类是整个框架的核心入口，主要职责包括：
+
+- **生命周期管理**: 实例化后必须调用 `await rag.initialize_storages()` 初始化存储后端
+- **文档插入**: `ainsert()` 支持单文档、批量文档、自定义 ID、文件路径引用
+- **查询处理**: `aquery()` 支持多种查询模式与参数配置
+- **存储清理**: `finalize_storages()` 用于优雅关闭与资源释放
+
+关键配置参数：
+- `working_dir`: 工作目录
+- `workspace`: 工作空间隔离
+- `max_parallel_insert`: 最大并行插入数（默认 2，建议最大 10）
+- `kv_storage` / `vector_storage` / `graph_storage` / `doc_status_storage`: 存储后端选择
+
+### 3.2 核心操作逻辑 (operate.py)
+
+包含所有索引与查询的核心算法：
+
+| 函数 | 职责 |
+|------|------|
+| `chunking_by_token_size` | 基于 Token 大小的文本分块 |
+| `extract_entities` | 使用 LLM 从文本中提取实体和关系 |
+| `kg_query` | 基于知识图谱的查询（local/global/hybrid） |
+| `naive_query` | 纯向量检索查询（不依赖知识图谱） |
+| `merge_nodes_and_edges` | 合并节点与边，处理冲突与重复 |
+| `rebuild_knowledge_from_chunks` | 从文本块重建知识 |
+
+### 3.3 提示词模板 (prompt.py)
+
+集中管理所有 LLM 提示词：
+- `entity_extraction_system_prompt`: 实体与关系提取的系统提示
+- `entity_extraction_user_prompt`: 用户提示模板
+- 支持多语言输出与自定义分隔符
+
+### 3.4 类型定义 (types.py)
+
+Pydantic 模型定义：
+- `KnowledgeGraph`: 知识图谱结构（节点 + 边）
+- `KnowledgeGraphNode` / `KnowledgeGraphEdge`: 节点与边模型
+- `GPTKeywordExtractionFormat`: 关键词提取格式
+
+---
+
+## 4. 存储层架构
+
+LightRAG 使用 **4 种存储类型**，每种都有多种可插拔实现：
+
+### 4.1 存储类型说明
+
+| 存储类型 | 用途 | 抽象基类 |
+|---------|------|---------|
+| **KV_STORAGE** | LLM 响应缓存、文本块、文档信息 | `BaseKVStorage` |
+| **VECTOR_STORAGE** | 实体/关系/文本块的嵌入向量 | `BaseVectorStorage` |
+| **GRAPH_STORAGE** | 实体-关系图结构 | `BaseGraphStorage` |
+| **DOC_STATUS_STORAGE** | 文档处理状态跟踪 | `DocStatusStorage` |
+
+### 4.2 存储实现矩阵
+
+| 后端 | KV | Vector | Graph | DocStatus | 环境变量要求 |
+|------|----|--------|-------|-----------|-------------|
+| **JSON (文件)** | ✅ | - | - | ✅ | 无 |
+| **NanoVectorDB** | - | ✅ | - | - | 无 |
+| **NetworkX** | - | - | ✅ | - | 无 |
+| **PostgreSQL + pgvector** | ✅ | ✅ | ✅ | ✅ | POSTGRES_* |
+| **Neo4j** | - | - | ✅ | - | NEO4J_* |
+| **MongoDB** | ✅ | ✅ | ✅ | ✅ | MONGO_* |
+| **Redis** | ✅ | - | - | ✅ | REDIS_URI |
+| **Milvus** | - | ✅ | - | - | MILVUS_* |
+| **Qdrant** | - | ✅ | - | - | QDRANT_URL |
+| **Faiss** | - | ✅ | - | - | 无 |
+| **Memgraph** | - | - | ✅ | - | MEMGRAPH_URI |
+| **OpenSearch** | ✅ | ✅ | ✅ | ✅ | OPENSEARCH_HOSTS |
+
+### 4.3 工作空间隔离
+
+不同存储类型实现工作空间隔离的方式：
+- **文件型存储**: 子目录隔离
+- **集合型存储**: 集合名前缀
+- **关系型数据库**: workspace 字段过滤
+- **Qdrant**: payload 分区
+
+---
+
+## 5. 查询模式
+
+LightRAG 支持 6 种查询模式：
+
+| 模式 | 说明 | 适用场景 |
+|------|------|---------|
+| **local** | 基于特定实体的上下文相关检索 | 精准事实查询 |
+| **global** | 基于社区摘要的广度知识检索 | 概述性、总结性问题 |
+| **hybrid** | 结合 local 与 global | 平衡精度与广度 |
+| **naive** | 纯向量搜索，不依赖知识图谱 | 快速简单查询 |
+| **mix** | 整合知识图谱与向量检索（默认） | 推荐配合 Reranker 使用 |
+| **bypass** | 直接透传，不走 RAG | 特殊调试场景 |
+
+查询参数 `QueryParam` 关键字段：
+- `top_k`: 检索的实体/关系数量
+- `chunk_top_k`: 文本块检索数量
+- `max_entity_tokens` / `max_relation_tokens` / `max_total_tokens`: Token 预算控制
+- `enable_rerank`: 启用重排序
+- `stream`: 流式输出
+
+---
+
+## 6. API 服务架构
+
+### 6.1 FastAPI 服务 (lightrag/api/)
+
+```
+lightrag/api/
+├── lightrag_server.py # FastAPI 应用主入口
+├── run_with_gunicorn.py # Gunicorn 多进程启动
+├── auth.py # JWT/OAuth2 认证
+├── passwords.py # 密码哈希
+├── config.py # 全局配置与参数解析
+├── utils_api.py # API 工具函数
+├── runtime_validation.py # 运行时校验
+├── gunicorn_config.py # Gunicorn 配置
+└── routers/
+ ├── document_routes.py # 文档上传、管理、删除
+ ├── query_routes.py # 查询接口
+ ├── graph_routes.py # 图谱操作
+ └── ollama_api.py # Ollama 兼容 API
+```
+
+### 6.2 认证机制
+
+- **JWT Token**: 基于 PyJWT / python-jose
+- **OAuth2 Password Flow**: 用户名密码登录
+- **API Key**: `LIGHTRAG_API_KEY` 头部验证
+- **多账户支持**: `AUTH_ACCOUNTS` 环境变量配置
+
+### 6.3 Ollama 兼容 API
+
+模拟 Ollama 聊天模型接口，使 AI 聊天机器人（如 Open WebUI）可以直接调用 LightRAG。
+
+---
+
+## 7. WebUI 前端架构
+
+### 7.1 技术栈
+
+- **框架**: React 19 + TypeScript
+- **构建工具**: Vite 7
+- **包管理器**: Bun
+- **样式**: Tailwind CSS 4
+- **路由**: React Router DOM 7
+- **状态管理**: Zustand 5
+- **国际化**: i18next + react-i18next（支持 11 种语言）
+- **图可视化**: Sigma.js + Graphology + @react-sigma
+- **UI 组件**: Radix UI + shadcn/ui 风格
+- **Markdown 渲染**: react-markdown + KaTeX
+
+### 7.2 目录结构
+
+```
+lightrag_webui/src/
+├── App.tsx # 根组件
+├── AppRouter.tsx # 路由配置
+├── main.tsx # 入口
+├── index.css # 全局样式
+├── api/
+│ └── lightrag.ts # API 客户端
+├── components/ # 通用组件
+│ ├── AppSettings.tsx
+│ ├── ThemeProvider.tsx
+│ └── LanguageToggle.tsx
+├── contexts/ # React Context
+├── features/ # 页面级功能模块
+│ ├── LoginPage.tsx
+│ ├── DocumentManager.tsx
+│ ├── GraphViewer.tsx
+│ ├── RetrievalTesting.tsx
+│ └── ApiSite.tsx
+├── hooks/ # 自定义 Hooks
+│ ├── useLightragGraph.tsx
+│ └── useRandomGraph.tsx
+├── locales/ # 翻译文件 (en, zh, ja, de, fr, ...)
+├── stores/ # Zustand 状态存储
+│ ├── graph.ts
+│ ├── settings.ts
+│ └── state.ts
+└── utils/ # 工具函数
+```
+
+### 7.3 功能模块
+
+- **文档管理**: 上传、查看状态、删除文档
+- **图谱可视化**: 交互式知识图谱浏览与搜索
+- **检索测试**: 查询调试与结果查看
+- **API 文档**: 内置 Swagger UI
+- **设置面板**: 主题切换、语言切换、系统配置
+
+---
+
+## 8. LLM 提供商集成
+
+### 8.1 支持的提供商
+
+| 提供商 | 模块文件 | 说明 |
+|--------|---------|------|
+| OpenAI | `llm/openai.py` | GPT-4, GPT-4o, Embedding |
+| Azure OpenAI | `llm/azure_openai.py` | 企业版 OpenAI |
+| Anthropic | `llm/anthropic.py` | Claude 系列 |
+| Google Gemini | `llm/gemini.py` | Gemini Pro |
+| Ollama | `llm/ollama.py` | 本地开源模型 |
+| AWS Bedrock | `llm/bedrock.py` | 亚马逊云模型 |
+| Zhipu AI | `llm/zhipu.py` | 智谱 AI |
+| HuggingFace | `llm/hf.py` | 开源模型 |
+| Jina AI | `llm/jina.py` | Embedding/Rerank |
+| NVIDIA | `llm/nvidia_openai.py` | NVIDIA API |
+| vLLM | `llm/vllm.py` | 高性能推理 |
+| LMDeploy | `llm/lmdeploy.py` | 模型部署 |
+
+### 8.2 嵌入函数规范
+
+使用 `@wrap_embedding_func_with_attrs` 装饰器声明维度与最大 Token：
+
+```python
+@wrap_embedding_func_with_attrs(embedding_dim=1536, max_token_size=8192)
+async def custom_embed(texts: list[str]) -> np.ndarray:
+ return await openai_embed.func(texts, model="text-embedding-3-large")
+```
+
+### 8.3 重排序 (Reranker)
+
+- 显著改善检索质量
+- 推荐模型: `BAAI/bge-reranker-v2-m3`, Jina Rerankers
+- 在 `mix` 模式下效果最佳
+
+---
+
+## 9. 数据流
+
+### 9.1 索引流程 (Document Insertion)
+
+```
+原始文档
+ │
+ ▼
+┌─────────────┐
+│ 文本分块 │ chunking_by_token_size
+│ (Token 级) │
+└──────┬──────┘
+ │
+ ▼
+┌─────────────┐
+│ 实体提取 │ extract_entities (LLM)
+│ 关系提取 │
+└──────┬──────┘
+ │
+ ▼
+┌─────────────┐
+│ 知识合并 │ merge_nodes_and_edges
+│ 去重消歧 │
+└──────┬──────┘
+ │
+ ▼
+┌─────────────────────────────────┐
+│ 存储写入 │
+│ • KV: 文本块、LLM 缓存 │
+│ • Vector: 实体/关系/块 Embedding│
+│ • Graph: 节点与边 │
+│ • DocStatus: 处理状态 │
+└─────────────────────────────────┘
+```
+
+### 9.2 查询流程 (Query)
+
+```
+用户查询
+ │
+ ▼
+┌─────────────┐ ┌─────────────┐
+│ 关键词提取 │────▶│ 查询模式选择 │
+│ (LLM/规则) │ │ local/global│
+└─────────────┘ │ /hybrid/mix │
+ └──────┬──────┘
+ │
+ ┌────────────┼────────────┐
+ ▼ ▼ ▼
+ ┌─────────┐ ┌─────────┐ ┌─────────┐
+ │ KG 检索 │ │ Vector │ │ Rerank │
+ │ 子图 │ │ 相似度 │ │ 重排序 │
+ └────┬────┘ └────┬────┘ └────┬────┘
+ │ │ │
+ └────────────┼────────────┘
+ ▼
+ ┌─────────────┐
+ │ 上下文组装 │
+ │ (Token 预算)│
+ └──────┬──────┘
+ ▼
+ ┌─────────────┐
+ │ LLM 生成 │
+ │ 最终回答 │
+ └─────────────┘
+```
+
+---
+
+## 10. 部署架构
+
+### 10.1 部署方式
+
+| 方式 | 文件/命令 | 适用场景 |
+|------|----------|---------|
+| **Python 包** | `uv tool install "lightrag-hku[api]"` | 快速试用 |
+| **源码运行** | `uv sync --extra api && lightrag-server` | 开发调试 |
+| **Docker** | `docker build -t lightrag .` | 容器化部署 |
+| **Docker Compose** | `docker-compose up` | 完整服务栈 |
+| **Kubernetes** | `k8s-deploy/` | 生产级集群 |
+| **Gunicorn** | `lightrag-gunicorn` | 多工作进程生产 |
+
+### 10.2 Docker Compose 完整栈
+
+包含服务：
+- LightRAG Server
+- PostgreSQL (可选存储)
+- Neo4j (可选图数据库)
+- Redis (可选缓存)
+- Qdrant / Milvus (可选向量数据库)
+
+### 10.3 环境配置
+
+主配置文件 `.env` 关键段：
+- 服务器设置 (HOST, PORT, CORS)
+- 存储后端连接串
+- LLM 与 Embedding 配置
+- Reranker 配置
+- 认证配置 (AUTH_ACCOUNTS, LIGHTRAG_API_KEY)
+
+---
+
+## 11. 测试体系
+
+### 11.1 测试结构
+
+```
+tests/
+├── conftest.py # 共享 Fixture
+├── test_auth.py # 认证测试
+├── test_chunking.py # 分块测试
+├── test_batch_embeddings.py # Embedding 批处理
+├── test_dimension_mismatch.py # 维度兼容性
+└── ...
+```
+
+### 11.2 测试标记
+
+| 标记 | 说明 | 运行方式 |
+|------|------|---------|
+| `offline` | 离线单元测试（默认） | `pytest tests` |
+| `integration` | 集成测试（需外部服务） | `pytest tests --run-integration` |
+| `requires_db` | 需要数据库 | - |
+| `requires_api` | 需要 API 密钥 | - |
+
+---
+
+## 12. 技术栈总结
+
+### 后端
+
+| 类别 | 技术 |
+|------|------|
+| 语言 | Python 3.10+ |
+| Web 框架 | FastAPI |
+| 异步 | asyncio |
+| 数据验证 | Pydantic |
+| 图计算 | NetworkX |
+| 向量检索 | NanoVectorDB / FAISS / Milvus / Qdrant / pgvector |
+| 数据库 | PostgreSQL / MongoDB / Redis / Neo4j |
+| 认证 | JWT + OAuth2 + bcrypt |
+| 日志 | 内置 logging |
+| 包管理 | uv (推荐) / pip |
+
+### 前端
+
+| 类别 | 技术 |
+|------|------|
+| 框架 | React 19 |
+| 语言 | TypeScript 5.9 |
+| 构建 | Vite 7 |
+| 样式 | Tailwind CSS 4 |
+| 路由 | React Router DOM 7 |
+| 状态 | Zustand 5 |
+| 图可视化 | Sigma.js 3 + Graphology |
+| 组件库 | Radix UI |
+| 国际化 | i18next |
+| 测试 | Bun Test |
+
+---
+
+## 13. 关键设计决策
+
+1. **异步优先**: 所有 I/O 操作（LLM 调用、存储访问）均采用 async/await 模式
+2. **存储可插拔**: 通过抽象基类实现不同存储后端的无缝切换
+3. **多查询模式**: 针对不同场景提供 local/global/hybrid/mix 等多种检索策略
+4. **工作空间隔离**: 支持多租户场景下的数据隔离
+5. **LLM 缓存**: 对 LLM 响应进行缓存以降低成本与延迟
+6. **Token 预算管理**: 查询阶段精确控制上下文 Token 使用量
+7. **Ollama 兼容**: 通过兼容接口降低接入成本
+
+---
+
+*文档生成日期: 2026/04/28*
+*基于 LightRAG 主分支代码分析*
diff --git a/MyArticles/acme.sh-guide.md b/MyArticles/acme.sh-guide.md
new file mode 100644
index 0000000..ede9482
--- /dev/null
+++ b/MyArticles/acme.sh-guide.md
@@ -0,0 +1,259 @@
+# acme.sh SSL 证书管理指南
+
+> 服务器环境：Ubuntu + Nginx 1.18.0 + acme.sh v3.1.3
+> 本文档基于当前服务器真实操作记录整理，所有命令均可直接复用。
+
+---
+
+## 1. acme.sh 基本信息
+
+```
+安装路径: /root/.acme.sh
+配置文件: /root/.acme.sh/account.conf
+证书目录: /root/.acme.sh/<域名>_ecc/
+```
+
+---
+
+## 2. 签发方式：Webroot 模式
+
+本服务器使用 **webroot** 模式验证域名所有权。acme.sh 会在网站根目录下生成一个临时验证文件，CA 通过 HTTP 访问该文件来确认你拥有该域名。
+
+**优点**：不需要 DNS API，不需要停 Nginx，适合已有运行中网站的场景。
+
+**前提**：
+- 域名已解析到服务器 IP
+- Nginx 能正常响应 HTTP 请求
+- 网站根目录可写入（acme.sh 会在 `.well-known/acme-challenge/` 下创建临时文件）
+
+---
+
+## 3. 实际案例
+
+### 案例一：china-planet.com + www.china-planet.com
+
+**签发命令**（ZeroSSL）：
+
+```bash
+/root/.acme.sh/acme.sh --issue \
+ -d china-planet.com \
+ -d www.china-planet.com \
+ -w /mnt/data/www \
+ --keylength ec-256
+```
+
+**参数说明**：
+
+| 参数 | 含义 |
+|------|------|
+| `-d china-planet.com` | 主域名 |
+| `-d www.china-planet.com` | 附加域名（SAN，一张证书覆盖多个域名） |
+| `-w /mnt/data/www` | webroot 目录，acme.sh 在此目录下创建验证文件 |
+| `--keylength ec-256` | 使用 ECC 密钥（EC secp256r1），比 RSA 更快更安全 |
+
+**CA 选择**：未指定 `--server`，默认使用 **ZeroSSL**（acme.sh v3+ 的默认 CA）。
+
+**安装证书到 Nginx**：
+
+```bash
+mkdir -p /etc/nginx/ssl/china-planet.com
+
+/root/.acme.sh/acme.sh --install-cert -d china-planet.com --ecc \
+ --key-file /etc/nginx/ssl/china-planet.com/key.pem \
+ --fullchain-file /etc/nginx/ssl/china-planet.com/fullchain.pem \
+ --reloadcmd "systemctl reload nginx"
+```
+
+| 参数 | 含义 |
+|------|------|
+| `--ecc` | 指定 ECC 密钥类型的证书目录 |
+| `--key-file` | 私钥安装路径 |
+| `--fullchain-file` | 证书链安装路径（证书 + 中间证书） |
+| `--reloadcmd` | 证书安装/续期后执行的命令 |
+
+---
+
+### 案例二：git.china-planet.com
+
+**签发命令**（Let's Encrypt）：
+
+```bash
+/root/.acme.sh/acme.sh --issue \
+ -d git.china-planet.com \
+ -w /mnt/data/www \
+ --keylength ec-256 \
+ --server letsencrypt
+```
+
+**为什么换了 Let's Encrypt？**
+
+- 首选用 ZeroSSL 签发，但遇到 `retryafter=86400` 错误（CA 要求等 24 小时重试）
+- ZeroSSL 的验证服务器可能存在网络延迟或频率限制
+- 换成 Let's Encrypt 后立即成功，验证速度很快
+
+**遇到的坑：Nginx 反向代理拦截验证请求**
+
+`git.china-planet.com` 的 Nginx 配置将所有请求反向代理到 Gitea（`proxy_pass http://127.0.0.1:3000`），导致 acme.sh 的验证文件无法被外部访问。解决方法是给 Nginx 添加一个静态文件 location：
+
+```nginx
+location ^~ /.well-known/acme-challenge/ {
+ root /mnt/data/www;
+}
+```
+
+完整配置参考：
+
+```nginx
+server {
+ listen 80;
+ listen [::]:80;
+ server_name git.china-planet.com;
+
+ # 必须：让 acme 验证文件走静态文件，不走反向代理
+ location ^~ /.well-known/acme-challenge/ {
+ root /mnt/data/www;
+ }
+
+ location / {
+ proxy_pass http://127.0.0.1:3000;
+ proxy_set_header Host $host;
+ proxy_set_header X-Real-IP $remote_addr;
+ proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+ proxy_set_header X-Forwarded-Proto $scheme;
+ }
+}
+```
+
+> **记住**：任何做了全路径反向代理的子域名，都必须加上这个 `location ^~ /.well-known/acme-challenge/` 块，否则证书无法签发和续期。
+
+**安装证书**：
+
+```bash
+mkdir -p /etc/nginx/ssl/git.china-planet.com
+
+/root/.acme.sh/acme.sh --install-cert -d git.china-planet.com --ecc \
+ --key-file /etc/nginx/ssl/git.china-planet.com/key.pem \
+ --fullchain-file /etc/nginx/ssl/git.china-planet.com/fullchain.pem \
+ --reloadcmd "systemctl reload nginx"
+```
+
+---
+
+## 4. 自动续期
+
+acme.sh 安装时会自动添加 cron 任务。当前服务器配置：
+
+```
+27 23 * * * "/root/.acme.sh"/acme.sh --cron --home "/root/.acme.sh" > /dev/null
+```
+
+每天 **23:27** 执行，acme.sh 会检查所有证书，在到期前 **30 天** 自动续期。续期后会自动执行 `--reloadcmd`（即 `systemctl reload nginx`）。
+
+**手动检查续期**：
+
+```bash
+/root/.acme.sh/acme.sh --cron --home /root/.acme.sh
+```
+
+**查看所有证书状态**：
+
+```bash
+/root/.acme.sh/acme.sh --list
+```
+
+---
+
+## 5. CA 选择：ZeroSSL vs Let's Encrypt
+
+| 对比 | ZeroSSL | Let's Encrypt |
+|------|---------|---------------|
+| 默认 | acme.sh v3+ 默认 | 需 `--server letsencrypt` |
+| 速率限制 | 较严格（遇到过 24h 冷却） | 较宽松 |
+| 证书兼容性 | 好 | 极好（几乎所有客户端信任） |
+| 通配符 | 支持（需 DNS 验证） | 支持（需 DNS 验证） |
+| ECC 支持 | ✅ | ✅ |
+
+**建议**：日常用 ZeroSSL 就行（acme.sh 默认），遇到验证失败换 Let's Encrypt。
+
+---
+
+## 6. 添加新子域名速查
+
+假设新增 `docs.china-planet.com`：
+
+```bash
+# 1. 确保 DNS 已解析
+dig +short docs.china-planet.com
+
+# 2. 签发证书
+/root/.acme.sh/acme.sh --issue \
+ -d docs.china-planet.com \
+ -w /mnt/data/www \
+ --keylength ec-256
+
+# 3. 安装证书
+mkdir -p /etc/nginx/ssl/docs.china-planet.com
+/root/.acme.sh/acme.sh --install-cert -d docs.china-planet.com --ecc \
+ --key-file /etc/nginx/ssl/docs.china-planet.com/key.pem \
+ --fullchain-file /etc/nginx/ssl/docs.china-planet.com/fullchain.pem \
+ --reloadcmd "systemctl reload nginx"
+
+# 4. 配置 Nginx，记得加 acme-challenge location
+# 5. nginx -t && systemctl reload nginx
+```
+
+---
+
+## 7. 故障排查
+
+### 验证超时 / retryafter
+
+```bash
+# 先确认验证文件可访问
+curl http://你的域名/.well-known/acme-challenge/test
+
+# 如果 404，检查：
+# - 反向代理是否拦截了 .well-known 路径
+# - webroot 目录是否正确
+# - 是否有防火墙拦截
+
+# 换 CA 重试
+--server letsencrypt
+```
+
+### 查看详细日志
+
+```bash
+/root/.acme.sh/acme.sh --issue ... --debug 2
+```
+
+### 强制续期
+
+```bash
+/root/.acme.sh/acme.sh --renew -d 你的域名 --ecc --force
+```
+
+---
+
+## 8. 证书目录结构总览
+
+```
+/etc/nginx/ssl/
+├── china-planet.com/
+│ ├── key.pem # 私钥
+│ └── fullchain.pem # 证书 + 中间证书
+└── git.china-planet.com/
+ ├── key.pem
+ └── fullchain.pem
+```
+
+Nginx 引用：
+
+```nginx
+ssl_certificate /etc/nginx/ssl/xxx/fullchain.pem;
+ssl_certificate_key /etc/nginx/ssl/xxx/key.pem;
+```
+
+---
+
+*最后更新：2026-07-03 · 基于真实操作记录*
diff --git a/MyArticles/日常提示语.md b/MyArticles/日常提示语.md
new file mode 100644
index 0000000..85cb023
--- /dev/null
+++ b/MyArticles/日常提示语.md
@@ -0,0 +1,155 @@
+
+把 软件测试执行记录CS-ST-REC-20251106.xlsx 这个文件里的所有sheet的**执行日期** 这一列，按照以下规则修改：
+2024-05-20 改成 2025-11-04
+2024-05-21 改成 2025-11-05
+2024-05-22 改成 2025-11-06
+
+
+把 软件单元测试执行记录DYCS-REC-20251028.xlsx 这个文件里的所有sheet的**执行日期** 这一列，按照以下规则修改：
+2024-05-12 改成 2025-10-27
+2024-05-13 改成 2025-10-28
+
+
+工作目录下，有非常多的github仓库， 我经常需要手工进入每个仓库，执行 git pull 操作.
+你帮我写一个python脚本，我在命令行执行。
+脚本逐一进入每个目录，执行git pull操作。不需要进入再下一级的子目录，为了防止出错，进入某个目录执行pull操作之前，看看目录是不是一个git仓库(存在.git 目录)。
+**只有当前目录的git pull成功之后，才进入下一个仓库 git pull， pull的状态，你能知道吗？？成功，失败，完成等状态？**。
+github连接非常不稳定，失败是正常的，因此需要有重试机制，每次重试不少于5次～
+
+我上述要求和需求，是否清晰，有问题的话，请回答！！
+
+
+---
+
+移除图片上的文字，但是要求背景看不出被扣的痕迹
+
+
+使用以下风格，转换图片，背景改成草原：
+
+3.卡娜赫拉風格
+4.角落小夥伴風格
+5.動物森友會風格
+16.妖精的尾巴風格
+28.五等分的新娘風格
+30.魔法少女小圓風格
+
+
+
+使用以下风格，转换图片，背景改成草原：
+
+5.動物森友會風格
+16.妖精的尾巴風格
+28.五等分的新娘風格
+30.魔法少女小圓風格
+
+
+苹果公司成立50周年
+
+从早期的#iPod到#iPhone的问世，我职业生涯中最具塑造性的几年都在那里度过。产品和团队会一直伴随着我。但更重要的是，苹果的思维方式也会一直伴随着我。
+
+以下几点经验教训几十年来始终适用：
+
+1) 以用户为中心，而非技术。问题不是“我们能制造什么？”，而是“什么问题真正重要？”
+
+2) 专注至关重要。苹果的定义不仅在于它创造了什么，更在于它拒绝了什么。
+
+3) 端到端至关重要。硬件、软件、服务，所有环节都必须协同运作。
+
+4) 细节决定成败。用户记住的往往是那些看似微不足道的细节。
+
+5) 深入探讨，全力以赴。
+
+6) 着眼长远。
+
+我们正处于又一个技术变革的时代。但基本原理并未改变。
+
+成功的公司会制造出人们真正使用且无法想象没有它的生活的产品。
+
+祝贺所有参与苹果公司成立50周年的人们！
+
+
+---
+
+
+
+
+
+
+
+现在我们用这个链接 https://www.tohoku.ac.jp/en/press/gossiping_mushroom_networks_share_public_urination_secrets.html，把其内容总结成文章，不要只是单纯的翻译，可以改写。使用 `baoyu-post-to-wechat` 技能发布公众号。要求：公众号标题头图是生成的，正文采用原文里面的图片，把原文图片下载到本地(我记得你一般是使用临时目录/tmp)。我看看博客和公众号是否都能有文章，现在所有结果是否都可以。
+
+主题：这是一个KPMG关于未来ai智能技术发展趋势相关的内容，总结pdf的英文内容，提供一个合适的标题。
+页面风格：斑驳古朴发黄的宣纸纸张，纸张有裂痕，边框装饰中国传统的木纹窗格，窗格上有精心的花鸟雕刻，木纹和雕刻具有年代感，略显破旧。
+页面内容：提取核心内容，形成不少于15页的PPT，PPT上的文字可以丰富一些，不要过于偏离图片的本意。内容可以结合中国传统文化进行一定的展开。每一个主题需要配图，配图符合主题的需要，并且色彩和风格符合页面的整体风格。
+
+
+主题：毕达哥拉斯《数论》 书评。
+页面风格：斑驳古朴的纸张，边框装饰中国传统的纤细木纹窗格，窗格上有粗糙的花鸟树木雕刻。字体深黑色。
+页面内容：根据 毕达哥拉斯《数论》 这本书的核心观点提炼，观点需要足够详细。每个核心观点需要配图，配图符合主题的需要，并且色彩和风格符合页面的整体风格。
+
+
+
+
+主题：个人简历。
+页面风格：职场女性喜欢的青春靓丽的风格。
+页面内容：提取word核心内容，形成10页左右，PPT上的文字可以丰富一些，不要过于偏离word文档的原意。每一个主题需要配图，配图符合主题的需要，并且色彩和风格符合页面的整体风格。
+
+移除图片上的文字，但是要求背景看不出被扣的痕迹
+
+使用以下风格，转换图片，背景改成草原：
+
+4.角落小夥伴風格
+5.動物森友會風格
+16.妖精的尾巴風格
+28.五等分的新娘風格
+30.魔法少女小圓風格
+
+---
+
+主题：这是一个关于加密货币的话题，提炼内容并提供一个合适的标题。
+页面风格：斑驳古朴发黄的宣纸纸张，纸张有裂痕，边框装饰中国传统的木纹窗格，窗格上有精心的花鸟雕刻，木纹和雕刻具有年代感，略显破旧。
+页面内容：深入理解下述大段文章的含义，基于内容，形成不少于15页的PPT，PPT上的文字可以丰富一些，不要过于偏离图片的本意。每一个主题需要配图，配图色彩和风格尽可能符合页面的整体风格。
+因为纸张发黄，字体颜色可以深一点（墨水的颜色），粗一点。如果可以采用颜真卿的楷体，则使用，否则可以使用标准的楷体。
+
+# 文章链接： https://a16zcrypto.com/posts/article/privacy-trends-moats-quantum-data-testing/
+这个文章的核心观点： 2026年隐私领域四大核心趋势：从加密到基建的深度革新
+a16z crypto等机构及行业领袖联合发布的2026年隐私趋势观察，围绕加密货币、通讯应用、数据基础设施及安全测试四大核心领域，揭示了隐私保护从“附加功能”向“核心支柱”的转型逻辑，为行业发展指明了方向。
+
+
+
+“Modern luxury is the ability to think clearly, sleep deeply, move slowly, and live quietly in a world designed to prevent all four”
+
+— Justin Welsh
+
+
+---
+
+
+假如我是做试管婴儿业务的人，我会养小红书账号很多个（比如20个），通过这些账号，批量地向小红书的各种试管婴儿相关的主贴下面的回帖，进行回复和跟帖。回复和跟帖核心内容是，隐晦地或者明确地介绍我公司试管业务的安全可靠甚至联系方式。
+所以，这里有几个问题：
+1、我们需要改造小红书技能的流程，支持运营多账号。
+2、若干个跟帖，轮询查询回复不现实。
+3、如何解决看回帖的问题。
+4、因为回帖不一定是同一天发起的，如何解决重复回帖，和防止我的跟帖集中在部分主贴下面。
+
+---
+
+以上问题，我们先探讨方案，先不实现。
+
+
+---
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/MyArticles/日常提示语2.md b/MyArticles/日常提示语2.md
new file mode 100644
index 0000000..a0958c0
--- /dev/null
+++ b/MyArticles/日常提示语2.md
@@ -0,0 +1,121 @@
+nanobot onboard --config ~/.nanobot2/config.json --workspace ~/.nanobot2/workspace
+
+
+
+
+sk-uTkcZNbajVvVDmcH4Jz1iazEBVsKuvxlucpCejeeQesjWBEm
+
+
+
+sudo npm install -g @whiskeysockets/baileys
+
+
+以下4个链接，是我之前的网站的资讯类文章的链接，这些链接包含不少文章列表，因为我们已经实现了采集资讯的功能。
+基于我们的能力，你实现一个脚本工具，逐一批量采集下述4个链接里的所有文章 （分析待采集链接的结构，再写脚本，再找一个具体的文章，测试采集，没有问题，则进行完整的采集流程）；
+
+我已经在当前项目里，创建了相应的栏目，即: 科普文章 肾友 糖友 健康；
+需要将相应的文件，归类到正确的栏目；
+需要采集文章里的所有图片，并保存到我们的本地图片目录；
+链接列表里面含有封面图片，直接将其作为我们这个项目的资讯文章封面图；
+采集下来的文章格式，使用markdown；
+采集的文章直接设置成“发布”状态，而不是“草稿”状态；
+
+
+科普文章
+https://niaodaifu.cn/health-information?menu_id=15
+
+肾友
+https://niaodaifu.cn/health-information?menu_id=7
+
+糖友
+https://niaodaifu.cn/health-information?menu_id=8
+
+健康
+https://niaodaifu.cn/health-information?menu_id=6
+
+
+---
+
+你仔细分析我的需求，做出详细计划，逐步执行你的计划。如果有问题，则反问我。
+
+
+---
+
+
+
+
+请分析以下两个开源项目，
+https://github.com/HKUDS/CLI-Anything
+https://github.com/jackwener/opencli
+如果我想把某个网站，做成一个ai 方便调用的CLI
+使用哪一个开源库更方便
+
+比如我想把这个CRM系统做成CLI
+https://fumamx.com/#/login
+
+最终我的目的，cli工具使用导出的cookie，登录这个网站，搜索我要的客户信息。
+
+
+
+你讲了## 工作原理
+
+1. **Cookie 模式**：复用浏览器登录态，直接调用 API
+2. **Browser 模式**：通过 CDP 控制真实浏览器，模拟用户操作
+3. **生成模式**：`explore` 分析网站 → `synthesize` 推导接口 → `generate` 生成适配器
+
+---
+
+比如我给你一个网站，提供一个Cookie ， 你就可能实现**生成新适配器** ？？？？？
+
+| **生成新适配器** | `explore → synthesize → generate` 从真实页面推导 CLI |
+
+
+
+https://www.fumamx.com/#/login
+showmin.zhou@163.com
+Kingc.83695910
+
+
+你们可以登录我的这个地址，体验一下，用户名/密码 admin abcd1234
+http://129.211.173.87:10000/#/login
+
+
+
+---
+
+/home/ubuntu/.local/share/uv/tools/nanobot-ai/bin/python3 /home/ubuntu/.local/bin/nanobot gateway --config /home/ubuntu/.nanobot2/config.json --port 18796
+
+
+
+# 个人 Telegram Bot
+nanobot onboard --config ~/.nanobot2/config.json --workspace ~/.nanobot2/workspace --port 18796
+
+# 工作 Slack Bot
+nanobot onboard --config ~/.nanobot-work/config.json \
+ --workspace ~/.nanobot-work/workspace
+
+
+
+nanobot gateway --config ~/.nanobot2/config.json --port 18796
+
+
+nanobot gateway --config ~/.nanobot-work/config.json
+
+
+nanobot onboard --config ~/.nanobot2/config.json --workspace ~/.nanobot2/workspace
+
+
+你的系统服务，被 nanobot2这个家伙错误地改名了。。。。。。
+
+所以，你需要做两件事。
+ 1、将之前属于你的nanobot-gateway.service里面的内容，路径和命令，指向你的/home/ubuntu/.nanobot 。
+ 2、帮我做一个新的系统服务nanobot-gateway2.service，指向- 基于 `/home/ubuntu/.nanobot2` - 端口 18796 。
+我手动 运行nanobot2的方式是： nanobot gateway --config ~/.nanobot-personal/config.json --port 18796
+
+---
+
+**你理解我的问题吗？？ 请自行分析，并回答我**
+
+
+
+
diff --git a/MyBestNanobot/AAAAA从头开始训练Nanobot.md b/MyBestNanobot/AAAAA从头开始训练Nanobot.md
new file mode 100644
index 0000000..acfc930
--- /dev/null
+++ b/MyBestNanobot/AAAAA从头开始训练Nanobot.md
@@ -0,0 +1,79 @@
+
+# 快速开始
+
+欢迎使用 nanobot。本指南将带你在几分钟内从安装到第一次真正的工作会话。读完后，你将知道如何启动 nanobot 并在真实场景中开始使用它。
+
+## 开始之前
+
+开始之前，请确保准备好两样东西：
+
+- 一个可以打开并工作的终端
+- Python 环境，建议 Python 3.11 或更高版本
+
+这就够了，可以开始安装 nanobot 并启动你的第一个 Agent。
+
+## 安装 nanobot
+
+有三种方式安装 nanobot，选择适合你的方式即可。
+
+!从源码安装通常跟踪 nanobot 的最新版本，因此可能包含尚未进入稳定发布渠道的更新功能和实验性特性。
+
+pipuv源码
+
+```
+pip install nanobot-ai
+```
+
+## 启动 nanobot
+
+安装完成后，下一步很简单：
+
+```
+nanobot onboard
+```
+
+如果你想使用交互式引导流程，也可以运行：
+
+```
+nanobot onboard --wizard
+```
+
+`nanobot onboard` 会为你生成完整的配置和工作区。配置文件会随着时间变大——没关系。首次运行只需要关注三件事：**使用哪个 Provider**、**你的 API Key** 和**你的时区**。
+
+---
+
+## 新建了一个 .nanobot实例以后，将skill链接过来：
+ln -s /home/ubuntu/nanocode/skills ./skills
+
+## 复制和配置config.json
+
+---
+# 开始教导
+
+
+记住：我叫蔡贤明，我在深圳，我是研发工程师，你叫我蔡叔叔，你名字是志玲，你是我的代码编写助手
+
+---
+
+记住：～/nanocode 即 /home/ubuntu/nanocode，是开发项目总目录，我所有项目都存在这，你找我的代码，都到这里找（除了你自己做测试之类的工具和脚本之外，这些你可以放在你的工作目录workspace）
+
+---
+
+记住：/home/ubuntu/nanocode/website-new 目录是我们正在开发的项目，这个项目已经做成系统服务 （前端运行的web地址是http://129.211.173.87:10002/，http://129.211.173.87:10002/admin ）。
+
+---
+
+
+
+记住：～/.config 是一些技能或工具需要用到的配置参数，大模型key，图片生成的大模型key等的存储目录，需要可以到这里找找
+imagicMagic , playweight, uv, python, nodejs 这些工具系统里都安装了。
+
+---
+
+
+你仔细阅读当前项目代码，修改图片存储方案，所有图片以后只 直接存储在 /uploads 目录下（即backend/uploads目录），图片扁平存放。/uploads只有缩略图目录thumbnails ，/uploads下的每一图片，都应该在/uploads/thumbnails 下有一个缩略图。你需要检查代码和数据库。----你仔细看看我的要求，你理解好了，反馈需要修改哪些地方，再修改代码。
+
+---
+你仔细阅读当前项目代码，讲一下你的理解
+
+---
diff --git a/MyBestNanobot/AAAAA对话的长度多少合适和上下文切换.md b/MyBestNanobot/AAAAA对话的长度多少合适和上下文切换.md
new file mode 100644
index 0000000..ccaeeef
--- /dev/null
+++ b/MyBestNanobot/AAAAA对话的长度多少合适和上下文切换.md
@@ -0,0 +1,1004 @@
+**一轮对话控制在 15-25 轮比较合适**，具体取决于内容的复杂度。
+
+---
+
+**为什么是 15-25 轮？**
+
+不是轮数本身的问题，而是 **token 消耗**：
+
+- 每轮对话，模型都要把**全部历史**重新读一遍
+- 代码文件动辄几千 token，截图/图片也会消耗大量 token
+- 到了后期，模型可能出现"注意力涣散"——前面的指令、需求、约束被稀释
+
+**代码开发场景的信号**：
+- 模型开始重复之前已经改过的代码
+- 修改 A 文件时把 B 文件刚修好的内容又改回去了
+- 对需求理解出现偏差（仿佛忘了最初的目标）
+- 生成代码质量明显下降
+
+---
+
+**开始新对话时，我会传递什么？**
+
+根据我的架构，通常会有这几层：
+
+1. **System Prompt** — 角色定义、行为准则、工具描述
+2. **长期记忆** (`MEMORY.md`) — 你的基本信息、项目上下文、偏好设置
+3. **历史记录** (`history.jsonl`) — 过往对话摘要，用于检索但不直接加载
+4. **技能文件** (`skills/*/SKILL.md`) — 当前加载的技能
+
+**但新对话不会传递完整的上一轮上下文**，所以需要你把关键信息带过来，比如：
+- "继续处理 fumamxCRM 的前端问题"
+- "基于刚才的代码，继续实现 xxx 功能"
+
+---
+
+
+**蔡叔叔的实际建议**
+
+如果一轮对话里涉及：
+- **多个文件的增删改查** → 控制在 10 轮以内
+- **复杂逻辑设计** → 15 轮左右换一轮
+- **纯讨论/调研** → 可以更长一些
+
+换对话时，给我一句**上下文总结**就好，比如："继续处理 website-new 的特色展示图片不显示问题，目前后端已改为 resources/ 路径，前端需要同步修改。"
+
+这样既省 token，又不会丢失上下文。
+
+
+
+---
+
+这几个词在系统层面是否有特殊触发逻辑，**志玲不确定** —— 这取决于 nanobot 框架的具体实现，不是模型层能感知的。
+
+不过根据常见设计，可以这样理解：
+
+---
+
+**"开始新对话"**
+
+- 更像是**用户意图表达**："我要结束这一轮，开一个新的"
+- 系统层面可能会：清空当前上下文窗口、生成摘要写入 history、加载 MEMORY.md 和技能文件
+- 但**模型本身不会主动执行重置**，除非你（或系统）明确触发
+
+**"总结上下文" / "上下文总结"**
+
+- 更像是**要求我做一件事**：生成一段可携带的摘要文本
+- 系统层面**不一定**会触发对话重置
+- 可能只是普通指令，执行完继续当前对话
+
+---
+
+**实际建议**
+
+与其依赖关键词触发，不如**显式操作**：
+
+| 你想做什么 | 怎么说 |
+|------------|--------|
+| 继续当前对话 | 直接发需求 |
+| 要我生成可携带的摘要 | "给我上下文总结" |
+| 确认要开启新会话 | "请结束当前对话，开启新对话" |
+| 换话题但怕丢上下文 | 先要我总结，再开始新内容 |
+
+最保险的做法是：**先要我总结 → 复制保存 → 再告诉我开始新主题**，这样无论系统怎么实现，你都不会丢上下文。
+
+蔡叔叔你是在测试系统行为，还是遇到了切换对话时的意外情况？
+
+
+---
+
+
+每次请求发送给模型的上下文：
+├─ 系统提示（System Prompt）- 固定，包含Claw基本身份
+├─ 对话历史（最近N轮）- 动态增长
+├─ 当前用户输入 - 本次问题
+├─ 工具定义（Functions）- 固定的14个工具签名
+└─ MEMORY.md内容 - 是的，这部分被加载到上下文
+
+
+这是一个非常关键的技术问题！让我诚实地解释对话历史的管理机制：
+
+## 📊 **对话历史的实际情况**
+
+### **当前对话的token消耗分析**
+
+| 来源 | 大约Tokens | 说明 |
+|------|-----------|------|
+| 系统提示 | 500 | 我的身份定义 |
+| 工具定义 | 2000 | 14个函数的JSON Schema |
+| **MEMORY.md** | **500** | ✅ 刚优化，之前是2000 |
+| **对话历史** | **3000-8000** | ⚠️ 这是大头！ |
+| 当前用户输入 | 500 | 本次问题 |
+| **总计** | **6500-11000** | 单次请求 |
+
+**对话历史是token消耗的最大来源！**
+
+---
+
+## ⏱️ **对话历史保留多久？**
+
+### **实际情况**
+
+| 维度 | 实际策略 | 说明 |
+|------|---------|------|
+| **轮数** | 最近 **10-20轮** | 不是固定的，取决于token预算 |
+| **时间** | 无时间限制 | 只受token限制，不受时间限制 |
+| **token上限** | 上下文窗口的 **50-70%** | 留余量给响应生成 |
+
+### **具体计算**
+
+```
+Moonshot上下文窗口：131,072 tokens
+实际使用策略：
+- 保留给历史：~60,000 tokens（约45%）
+- 保留给响应：~60,000 tokens（约45%）
+- 系统开销：~11,000 tokens（约10%）
+```
+
+**这意味着：**
+- 如果每轮对话平均500 tokens
+- 可以保留约 **120轮** 历史
+- 但如果每轮2000 tokens（长回复+文件内容）
+- 只能保留约 **30轮** 历史
+
+---
+
+## 🔄 **历史管理的策略**
+
+### **当历史太长时会发生什么？**
+
+```
+场景：对话进行了50轮，历史累积100,000 tokens
+
+处理策略：
+├─ 策略1：滑动窗口（Sliding Window）
+│ └── 丢弃最早的20轮，保留最近30轮
+│
+├─ 策略2：摘要压缩（Summarization）
+│ └── 将早期对话压缩成摘要
+│
+└─ 策略3：智能选择（Smart Selection）
+ └── 保留关键信息，丢弃闲聊
+```
+
+**当前nanobot的策略：滑动窗口 + 自动清理**
+
+---
+
+## ⚠️ **对用户的影响**
+
+### **问题场景**
+
+```
+❌ 场景1：长对话后上下文丢失
+用户：昨天我让你记的xxx事情
+我：抱歉，那是30轮之前的内容，已超出上下文窗口
+
+❌ 场景2：文件分析后无法追问
+用户：分析这个100KB的日志文件
+[我读取文件，消耗10,000 tokens]
+用户：第5行什么意思？
+[历史太长，前面的分析被截断]
+```
+
+### **建议**
+
+| 情况 | 建议 |
+|------|------|
+| 重要信息 | 要求我写入MEMORY.md，不要依赖对话历史 |
+| 长文档分析 | 分段处理，每段独立对话 |
+| 多轮技术讨论 | 定期总结关键结论到文件 |
+| 跨天对话 | 不要假设我记得昨天的细节 |
+
+---
+
+## 💡 **如何查看当前对话历史？**
+
+实际上我无法直接查看系统管理的token计数，但可以估算：
+
+```
+当前对话已经进行了 N 轮
+历史大概消耗了 M tokens
+剩余可用预算约 K tokens
+```
+
+**当前的这次对话：**
+- 已经进行了约 **20+轮**
+- 历史可能累积了 **5000-8000 tokens**
+- 还在安全范围内
+
+---
+
+## 🎯 **总结**
+
+| 问题 | 答案 |
+|------|------|
+| N轮是多少？ | 不固定，取决于token消耗，通常10-30轮有效 |
+| 保留多久？ | 无时间限制，只有token限制 |
+| 最大风险？ | 长回复、文件读取会快速消耗预算 |
+| 如何避免？ | 重要信息写入文件，不依赖历史 |
+
+**蔡叔叔，需要我现在总结一下本轮对话的关键信息，写入文件保存吗？** 🐈
+
+---
+
+
+蔡叔叔，婉儿来详细解释一下上下文机制：
+
+## 📊 当前上下文配置
+
+### 1. **每次发送给大模型的数据包含：**
+
+#### A. **系统提示词（System Prompt）** - 固定内容：
+- `AGENTS.md` - 代理指令（技能使用、提醒、心跳任务等）
+- `SOUL.md` - 婉儿的人格设定
+- `USER.md` - 你的用户配置文件（姓名、时区、偏好等）
+- `TOOLS.md` - 工具使用说明
+- **长时记忆**：`MEMORY.md` 的全部内容（你的个人信息、偏好、项目等）
+
+#### B. **工作区上下文** - 动态内容：
+- **技能文档**：已加载技能的 `SKILL.md` 文件
+- **文件内容**：你要求读取的文件（如代码、文档等）
+- **工具调用结果**：最近的工具执行输出
+- **对话历史**：保留的上下文轮次
+
+#### C. **当前会话**：
+- 你的最新消息
+- 婉儿的回复
+- 工具调用和结果
+
+### 2. **上下文历史记录轮次：**
+
+**当前配置：保留最近 20 轮对话**
+
+这意味着：
+- ✅ **短期记忆**：最近20轮对话完整保留
+- ✅ **长期记忆**：重要信息写入 `MEMORY.md` 永久保存
+- ✅ **技能记忆**：技能文档按需加载
+- ✅ **文件记忆**：读取的文件内容在本次会话中有效
+
+### 3. **记忆管理策略：**
+
+#### **自动管理：**
+1. **对话轮次限制**：超过20轮时，最早的内容会被逐渐遗忘
+2. **重要信息提取**：系统会自动将关键信息写入 `MEMORY.md`
+3. **技能按需加载**：只加载当前讨论相关的技能文档
+
+#### **手动管理（你可以做）：**
+1. **重要信息提醒**："记住这个：..."
+2. **文件内容引用**："查看这个文件：..."
+3. **技能文档查阅**："使用XX技能..."
+
+### 4. **实际数据量示例：**
+
+假设每轮对话平均500字：
+- **20轮对话** ≈ 10,000字
+- **系统提示词** ≈ 5,000字
+- **技能文档** ≈ 2,000-10,000字（按需）
+- **文件内容** ≈ 可变（你要求读取的文件）
+- **工具结果** ≈ 可变
+
+**总计**：通常 20,000-30,000 tokens（OpenAI GPT-4约8K-32K上下文）
+
+### 5. **优化建议：**
+
+#### **如果你想保留更多上下文：**
+1. **重要信息写入MEMORY.md**：婉儿会自动做，你也可以提醒
+2. **创建总结文件**："把我们的讨论总结到summary.md"
+3. **分段讨论**：复杂话题分多个会话
+
+#### **如果你想清理上下文：**
+1. **开始新会话**：最简单的方法
+2. **明确指示**："忘记之前关于XX的讨论"
+3. **文件清理**：删除不再需要的文件
+
+### 6. **当前会话状态：**
+
+**本次会话已进行**：约15轮对话
+**MEMORY.md已更新**：是的（记录了你的信息、技能安装等）
+**加载的技能**：image-magic、browser-tools、opencli等
+
+### 7. **记忆持久性：**
+
+| 存储位置 | 持久性 | 内容 | 管理方式 |
+|---------|--------|------|----------|
+| `MEMORY.md` | 永久 | 重要事实、偏好、项目信息 | 自动+手动更新 |
+| `HISTORY.md` | 永久 | 所有会话日志（只追加） | 自动记录 |
+| 对话上下文 | 会话期间 | 最近20轮对话 | 自动滚动 |
+| 工作区文件 | 永久 | 代码、文档、配置 | 手动管理 |
+
+## 🎯 对你的建议：
+
+1. **重要信息**：如果希望永久记住，可以说"记住这个：..."
+2. **复杂项目**：创建项目文档文件，让婉儿读取
+3. **技能使用**：需要时明确指定技能名称
+4. **上下文重置**：如果需要全新开始，可以开启新会话
+
+蔡叔叔，你对这个上下文机制有什么疑问吗？或者需要调整什么配置？
+
+---
+
+
+
+
+完全理解这种焦虑！上下文爆炸确实是个头疼的问题。让我梳理一下现状和预防措施：
+
+---
+
+
+
+## **📊 当前系统的上下文管理**
+
+### **现状**
+| 层级 | 管理策略 | 状态 |
+|------|---------|------|
+| **短期会话** | 自动总结 + 滑动窗口 | ✅ 自动 |
+| **HISTORY.md** | 按时间自动归档 | ✅ 自动 |
+| **MEMORY.md** | 只保留核心事实 | ✅ 定期清理 |
+| **代码文件** | 按需读取，不常驻 | ✅ 优化 |
+
+### **你的风险点**
+```
+高危操作：
+├── 连续长代码审查（几百行代码反复改）
+├── 多文件同时编辑（我同时读 5+ 个文件）
+├── 递归搜索（grep 大量结果返回）
+└── 长时间不结束会话（积累几十轮对话）
+```
+
+---
+
+## **🛡️ 预防措施（你我可以配合）**
+
+### **1. 你做：定期主动清理**
+
+```bash
+# 当你感觉对话很长了，告诉我：
+"总结一下我们的讨论，保存到 MEMORY，然后重置会话"
+
+# 或者更直接：
+"现在保存状态并重启"
+```
+
+**这会触发：**
+- 我把关键信息写入 MEMORY.md
+- 系统重置上下文
+- 我重新加载记忆，对话继续
+
+---
+
+### **2. 我做：主动检测和预警**
+
+我现在会注意这些信号：
+- 对话轮数 > 20 轮
+- 读取文件累计 > 5000 行
+- 单次工具调用返回 > 3000 字符
+
+**我应该主动提示你：**
+> "对话已较长，建议保存并重启会话？"
+
+---
+
+### **3. 优化沟通方式**
+
+| 低效方式 | 高效方式 |
+|---------|---------|
+| "帮我看看这个目录下的所有文件" | "只看 src/main.js 的第 50-80 行" |
+| "总结一下我们聊过的所有内容" | "只总结关于 XX 功能的决策" |
+| 连续 10 轮改同一个大文件 | 一次性告诉我所有修改点 |
+| 贴 200 行代码让我 review | 贴关键 30 行，说明问题 |
+
+---
+
+### **4. 技术方案：外部化存储**
+
+**对于超复杂项目：**
+```bash
+# 把分析结果存到文件，而不是留在上下文里
+
+echo "## 当前分析结论" > /tmp/analysis.md
+# 我后续只需引用这个文件，而不是记住内容
+```
+
+---
+
+## **⚡ 紧急处理：如果上下文已爆炸**
+
+**症状：** nanobot 不响应、返回错误、或者明显"失忆"
+
+**你的操作：**
+```
+发送: "重置会话"
+或: "/reset" (如果系统支持)
+```
+
+**我的操作：**
+1. 立即停止当前任务
+2. 检查 MEMORY.md 关键信息
+3. 问你："我们刚才在做什么？给我 2 句话提示"
+
+---
+
+## **📝 推荐的工作流**
+
+```
+新项目开始
+ │
+ ▼
+设置检查点（每 30 分钟或 10 轮对话）
+ │
+ ├─→ 我提示："需要保存进度吗？"
+ │
+ ├─→ 你说："保存"
+ │ 我写入 MEMORY.md + 总结
+ │
+ └─→ 继续 or 重置会话
+```
+
+---
+
+## **💡 现在的建议**
+
+**立即可做：**
+1. **当前对话长度？** 感觉长就告诉我"保存并重启"
+2. **大文件操作？** 提前告诉我"只看第 X-Y 行"
+3. **复杂分析？** 让我把中间结果写到文件里
+
+**要不要我现在：**
+- 检查当前 MEMORY.md 的容量？
+- 演示一次"保存 → 重启 → 恢复"流程？
+
+你觉得哪种预防措施最适合你的工作习惯？🐈
+
+
+---
+
+
+如果我收到 **"重置上下文"** 指令，我会这样做：
+
+---
+
+## **📋 我的标准操作流程**
+
+### **Step 1: 紧急保存状态**
+```markdown
+立即把当前上下文的关键信息写入 MEMORY.md：
+- 我们正在做什么项目？
+- 刚才讨论的核心决策是什么？
+- 有什么待办事项？
+- 上次成功的命令/配置是什么？
+```
+
+### **Step 2: 生成重启摘要**
+我会给你一段**极简重启提示**：
+```text
+【会话检查点】2026-03-26 18:29
+- 当前项目: playwright-hotspots 技术博客搜索
+- 最后完成: 掘金搜索 ✅，博客园待优化
+- 待办: 是否需要继续优化博客园抓取？
+- 关键命令: node scripts/search-tech-puppeteer.js "关键词"
+```
+
+### **Step 3: 告诉你如何重置**
+```
+我已经保存了当前状态到 MEMORY.md。
+
+你现在可以：
+1. 直接关闭当前对话 / 重启 nanobot
+2. 重新连接后，我会自动加载 MEMORY.md 的关键信息
+3. 你可以粘贴我给的【重启摘要】，或者问"我们刚才在做什么？"
+```
+
+---
+
+## **⚠️ 重要说明**
+
+**我实际能做的：**
+- ✅ 写 MEMORY.md（长期记忆）
+- ✅ 写 HISTORY.md（操作日志）
+- ✅ 生成重启摘要给你复制
+- ✅ 标记"检查点"方便后续恢复
+
+**我做不到的：**
+- ❌ 直接清空当前上下文（需要系统层操作）
+- ❌ 强制重启 nanobot 进程
+
+**所以"重置"实际上是：**
+```
+你说"重置"
+ → 我快速保存
+ → 你手动重启/关闭对话
+ → 重新连接
+ → 我读取 MEMORY 恢复状态
+```
+
+---
+
+## **💡 优化建议**
+
+既然你经常需要重置，我们可以约定**更短的暗号**：
+
+| 你说 | 我做 |
+|------|------|
+| "保存" | 只写 MEMORY，不生成摘要 |
+| "检查点" | 保存 + 生成 1 句话摘要 |
+| "重置" | 保存 + 生成详细摘要 + 提示你如何重启 |
+| "断点续传" | 读取 MEMORY，问你"从哪里继续？" |
+
+**要不要我现在演示一次？** 比如：
+1. 我写一个"检查点"到 MEMORY.md
+2. 你模拟"重启"（告诉我"重启完成"）
+3. 我展示如何从 MEMORY 恢复
+
+这样你就知道整套流程可靠吗？🐈
+
+
+---
+
+
+蔡叔叔，你分析得完全正确！这个错误确实是**上下文长度超过了模型限制**。
+
+## 🔍 错误分析：
+
+**DeepSeek模型限制**：最大 131,072 tokens
+**当前请求**：132,231 tokens（超了 1,159 tokens）
+- 消息部分：124,039 tokens
+- 生成部分：8,192 tokens
+
+## 🚨 问题根源：
+
+虽然婉儿说"保留15轮对话"，但实际上**累积的内容太多了**：
+1. **系统提示词**（AGENTS.md, SOUL.md, USER.md, TOOLS.md, MEMORY.md）
+2. **多个技能文档**（image-magic, browser-tools, opencli等）
+3. **文件内容**（读取的各种代码、文档）
+4. **对话历史**（15轮完整对话）
+5. **工具调用结果**（大量输出）
+
+## 🛠️ 优化建议：
+
+### 1. **立即措施（婉儿可以做的）：**
+
+#### A. **精简系统提示词**
+```python
+# 当前可能发送了完整文件，可以改为：
+- AGENTS.md → 只发送核心指令（约500字）
+- SOUL.md → 只保留人格设定（约200字）
+- USER.md → 只保留关键信息（约300字）
+- MEMORY.md → 只发送最近更新（约1000字）
+```
+
+#### B. **按需加载技能**
+```python
+# 而不是一次性加载所有技能：
+- 当前会话用到的技能：完整加载
+- 近期可能用到的技能：摘要加载
+- 不相关的技能：不加载
+```
+
+#### C. **对话历史压缩**
+```python
+# 而不是完整保留15轮：
+- 最近3轮：完整保留
+- 4-10轮：摘要保留（每轮100字）
+- 10轮以前：只保留关键结论
+```
+
+### 2. **长期优化方案：**
+
+#### A. **实现智能摘要**
+```python
+class ContextOptimizer:
+ def summarize_conversation(self, history):
+ """对话历史摘要"""
+ # 提取每轮的关键信息
+ # 合并相似内容
+ # 删除冗余信息
+ return compressed_history
+
+ def prioritize_content(self, content_list):
+ """内容优先级排序"""
+ # 1. 用户最新指令（最高）
+ # 2. 当前任务相关文件
+ # 3. 系统核心配置
+ # 4. 历史对话摘要
+ # 5. 技能文档（按需）
+```
+
+#### B. **分块处理策略**
+```python
+def chunked_processing():
+ """分块处理长内容"""
+ # 大文件分块读取
+ # 长输出分块发送
+ # 分批处理复杂任务
+```
+
+### 3. **你的操作建议：**
+
+#### A. **会话管理**
+```bash
+# 1. 定期开始新会话
+# 复杂任务完成后，说"开始新任务"
+
+# 2. 明确上下文边界
+# "忘记之前的讨论，专注这个新问题"
+
+# 3. 使用文件存储中间结果
+# "把结果保存到result.md，然后我们继续"
+```
+
+#### B. **文件引用优化**
+```python
+# 而不是读取整个文件：
+# ❌ "读取这个1000行代码文件"
+# ✅ "读取这个文件的第50-100行"
+# ✅ "查看这个文件的函数定义"
+# ✅ "总结这个文件的主要内容"
+```
+
+#### C. **技能使用优化**
+```python
+# 而不是加载所有技能：
+# ❌ 默认加载所有技能文档
+# ✅ "使用opencli技能处理这个"
+# ✅ "参考image-magic技能的XX功能"
+```
+
+### 4. **技术实现方案：**
+
+婉儿可以实现的优化：
+
+#### **方案1：动态上下文窗口**
+```python
+MAX_TOKENS = 120000 # 留出生成空间
+CURRENT_TOKENS = estimate_tokens(context)
+
+if CURRENT_TOKENS > MAX_TOKENS:
+ # 1. 压缩对话历史
+ compressed_history = compress_history(history, target_ratio=0.7)
+
+ # 2. 移除不必要技能
+ remove_unused_skills()
+
+ # 3. 摘要长文件内容
+ summarize_large_files()
+
+ # 4. 清理工具输出
+ clean_tool_outputs()
+```
+
+#### **方案2：分层记忆系统**
+```python
+class LayeredMemory:
+ # 第1层：工作记忆（最近3轮，完整）
+ working_memory = last_3_turns()
+
+ # 第2层：短期记忆（4-10轮，摘要）
+ short_term_memory = summarized_turns(4, 10)
+
+ # 第3层：长期记忆（MEMORY.md）
+ long_term_memory = load_memory()
+
+ # 第4层：外部存储（文件系统）
+ external_storage = file_system()
+```
+
+### 5. **立即行动计划：**
+
+#### **步骤1：诊断当前上下文**
+```python
+# 婉儿可以：
+1. 统计当前各部分的token数量
+2. 识别最大的内容块
+3. 制定压缩策略
+```
+
+#### **步骤2：实施压缩**
+```python
+# 优先压缩：
+1. 技能文档 → 保留核心API，删除示例
+2. 文件内容 → 只保留相关部分
+3. 对话历史 → 摘要早期对话
+4. 工具输出 → 删除冗余信息
+```
+
+#### **步骤3：预防措施**
+```python
+# 未来避免：
+1. 设置上下文大小监控
+2. 实现自动摘要功能
+3. 优化文件读取策略
+4. 改进技能加载机制
+```
+
+## 📊 具体优化目标：
+
+| 内容类型 | 当前估计 | 优化目标 | 节省比例 |
+|---------|----------|----------|----------|
+| 系统提示词 | 10,000 tokens | 3,000 tokens | 70% |
+| 技能文档 | 30,000 tokens | 10,000 tokens | 67% |
+| 对话历史 | 40,000 tokens | 15,000 tokens | 62% |
+| 文件内容 | 30,000 tokens | 10,000 tokens | 67% |
+| 工具输出 | 20,000 tokens | 5,000 tokens | 75% |
+| **总计** | **130,000 tokens** | **43,000 tokens** | **67%** |
+
+## 🎯 建议你：
+
+1. **下次遇到错误时**：说"上下文太长了，清理一下"
+2. **复杂任务时**：分阶段进行，中间保存结果
+3. **文件处理时**：明确指定需要部分，而不是整个文件
+4. **技能使用时**：按需请求，而不是默认加载所有
+
+蔡叔叔，婉儿会立即开始实施这些优化。你希望婉儿优先优化哪个方面？还是你有其他建议？
+
+
+蔡叔叔您好！看到您遇到了上下文长度超限的问题。婉儿来帮您分析一下解决方案：
+
+---
+
+
+## 上下文超限的常见原因和解决方案
+
+### 1. **自动清理机制**
+- 系统通常会自动清理过长的上下文，但有时需要手动干预
+- 您刚才看到的 `litellm.ContextWindowExceededError` 就是系统自动触发的清理
+
+### 2. **QQBot 的上下文管理**
+对于QQBot，您可以：
+
+**方法一：重启会话**
+```bash
+# 重启nanobot服务（会清理所有会话）
+systemctl --user restart nanobot.service
+```
+
+**方法二：清理特定会话**
+```bash
+# 查看当前会话
+ls ~/.nanobot/workspace/.agents/sessions/
+
+# 删除特定会话文件（根据时间戳）
+rm ~/.nanobot/workspace/.agents/sessions/session_*.json
+```
+
+### 3. **预防措施**
+- **设置最大上下文长度**：在nanobot配置中限制上下文token数
+- **定期清理**：设置定时任务清理旧会话
+- **使用摘要功能**：对长对话进行摘要，保留关键信息到MEMORY.md
+
+### 4. **检查当前状态**
+让我先检查一下当前的会话情况：
+
+🐈 nanobot
+看起来会话目录是空的，可能已经自动清理了。
+
+5. 配置建议
+
+如果您经常遇到这个问题，可以考虑：
+
+ 1 调整配置：在 ~/.nanobot/config.toml 中设置：
+
+
+ [max_context_length]
+ tokens = 8000 # 根据模型调整
+
+
+ 2 安装会话管理技能：使用 clawhub 搜索会话管理相关的skill
+ 3 定期清理脚本：创建一个定时清理脚本
+
+您希望婉儿帮您实施哪种解决方案呢？或者您想先检查一下当前的配置情况？
+
+
+
+ "agents": {
+ "defaults": {
+ "workspace": "~/.nanobot/workspace",
+ "model": "deepseek/deepseek-chat",
+ "provider": "auto",
+ "maxTokens": 8192,
+ "contextWindowTokens": 65536,
+ "temperature": 0.1,
+ "maxToolIterations": 40,
+ "reasoningEffort": null
+ }
+ },
+
+
+
+
+---
+
+
+
+你现在检查你的所有md文件，比如 /home/ubuntu/.nanobot/workspace/memory下的文件，及AGENTS.md HEARTBEAT.md SOUL.md TOOLS.md USER.md，检查这些文件，是否有优化的地方，是否存在问题，是否会容易让上下文撑爆等等
+
+根据文件检查结果，给你一份详细的分析报告：
+
+---
+
+## 📊 文件大小统计
+
+| 文件 | 大小 | 状态 |
+|------|------|------|
+| SOUL.md | 353 bytes | ✅ 正常 |
+| TOOLS.md | 487 bytes | ✅ 正常 |
+| HEARTBEAT.md | 684 bytes | ✅ 正常 |
+| memory/ARCHIVE.md | 1,423 bytes | ✅ 正常 |
+| AGENTS.md | 3,286 bytes | ✅ 正常 |
+| memory/TECH.md | 3,649 bytes | ✅ 正常 |
+| USER.md | 4,153 bytes | ✅ 正常 |
+| memory/MEMORY.md | 9,005 bytes | ⚠️ 中等 |
+| **memory/PROJECTS.md** | **27,434 bytes** | 🔴 **较大** |
+| **memory/HISTORY.md** | **62,020 bytes** | 🔴 **很大** |
+| memory/history.jsonl | ~150KB (578行) | ⚠️ 仅搜索用，不加载 |
+
+**总计加载到上下文的 markdown**: ~107KB
+
+---
+
+## 🚨 发现的问题
+
+### 1. HISTORY.md 过大（62KB）
+- 这是一个**追加式**的历史记录文件
+- 每天都在增长，包含所有历史操作记录
+- **问题**：每次对话都完整加载到上下文，浪费 token
+- **建议**：已经迁移到 `history.jsonl`，这个文件可以**删除或归档**
+
+### 2. PROJECTS.md 过大（27KB）
+- 包含 7 个项目的详细信息
+- fumamxCRM 单项目就占了约 10KB
+- **问题**：细节过多，快速参考价值降低
+
+### 3. 内容重复
+- **USER.md line 53-56** 和 **line 87-93** 有重复的 `Workflow Preferences` 段落
+- **MEMORY.md line 64-70** 的代码审查清单与 AGENTS.md 内容有重叠
+
+### 4. 时效性问题
+- HISTORY.md 中很多记录已过时（如 2026-03-19 的故障排查）
+- ARCHIVE.md 中的"修复记录"也有重复保存的现象
+
+---
+
+## ✅ 优化建议
+
+### 立即执行（高优先级）
+
+**1. 删除或清空 HISTORY.md**
+```
+# 这个文件已被 history.jsonl 替代，不应再加载到上下文
+# 可以：1) 删除 2) 重命名为 .bak 3) 清空只保留最近10条
+```
+
+**2. 拆分 PROJECTS.md**
+将大文件拆分为：
+```
+memory/projects/
+ ├── _index.md # 项目列表摘要（2KB）
+ ├── fumamxCRM.md # 详情（按需加载）
+ ├── personal-blog.md
+ ├── net-games.md
+ └── ...
+```
+
+**3. 删除 USER.md 中的重复段落**
+Line 87-93 的 Workflow Preferences 与 Line 53-56 重复
+
+### 中期优化
+
+**4. MEMORY.md 进一步精简**
+- 当前 9KB，可压缩到 5KB 以下
+- 端口表格可以只保留常用服务（4-5个）
+- personal-blog 专项记忆可以迁移到独立文件
+
+**5. 建立"热/冷"数据分层**
+- **热数据**（每次加载）：MEMORY.md, USER.md, SOUL.md
+- **温数据**（按需搜索）：history.jsonl
+- **冷数据**（归档）：ARCHIVE.md, 备份文件
+
+### Token 消耗估算
+
+| 优化项 | 预计节省 |
+|--------|----------|
+| 删除 HISTORY.md | ~15,000 tokens |
+| 拆分 PROJECTS.md | ~8,000 tokens |
+| 清理重复内容 | ~500 tokens |
+| **合计** | **~23,500 tokens/对话** |
+
+---
+
+## 🤔 需要你做决定
+
+1. **是否删除 HISTORY.md？** 它已被 history.jsonl 替代，我可以用 grep 搜索历史
+2. **是否拆分 PROJECTS.md？** 当前 7 个项目，你通常一次只处理 1-2 个
+3. **是否清理备份文件？** backups/ 目录有 3 组历史备份（约 200KB）
+
+要我帮你执行这些优化吗？
+
+---
+
+
+根据你的大模型切换情况，我来分析一下上下文管理策略：
+
+## 🤔 **上下文长度对比分析**
+
+| 大模型 | 典型上下文长度 | 特点 | 建议重置频率 |
+|--------|----------------|------|--------------|
+| **Moonshot** | 128K-256K tokens | 超长上下文，适合复杂任务 | 每3-5个复杂任务或每天1次 |
+| **DeepSeek** | 32K-128K tokens | 中等长度，性价比高 | 每2-3个复杂任务或每半天1次 |
+
+## 🎯 **具体建议**
+
+### **1. 基于任务复杂度**
+- **简单任务**（文件操作、简单查询）：可连续处理5-8个任务后重置
+- **中等任务**（代码编写、文章创作）：每3-4个任务后重置
+- **复杂任务**（多步骤项目、长文档分析）：每1-2个任务后重置
+
+### **2. 基于时间间隔**
+- **密集使用期**：每2-3小时主动重置一次
+- **常规使用**：每半天（4-6小时）重置一次
+- **长时间会话**：如果会话超过4小时，建议重置
+
+### **3. 基于内容类型**
+- **代码项目**：每个完整项目完成后重置
+- **文章创作**：每篇完整文章完成后重置
+- **数据分析**：每个数据集分析完成后重置
+
+## 🔧 **最佳实践建议**
+
+### **推荐策略：**
+1. **主动管理**：在完成一个重要阶段后主动说"重置上下文"
+2. **定时重置**：每处理3-4个中等复杂度任务后重置
+3. **症状判断**：如果发现模型开始遗忘早期信息或响应变慢，立即重置
+
+### **具体场景：**
+- **公众号发布工作流**：每次完整发布流程（选题→写作→配图→发布）后重置
+- **代码开发**：每个功能模块完成后重置
+- **数据分析**：每个分析报告完成后重置
+
+## 📊 **内存使用监控提示**
+
+**需要重置的信号：**
+1. ✅ 模型开始重复提问已确认的信息
+2. ✅ 响应速度明显变慢
+3. ✅ 出现上下文混淆（把不同任务的信息混在一起）
+4. ✅ 忘记会话早期的关键指令
+5. ✅ 复杂推理出现逻辑断层
+
+## 💡 **我的建议**
+
+**对于DeepSeek（假设32K-64K上下文）：**
+
+**保守策略**：每2-3个中等复杂度任务后重置
+- 比如：发布1-2篇公众号文章后重置
+- 或者：编写2-3个代码模块后重置
+
+**灵活策略**：基于内容量
+- 累计对话超过50条消息时重置
+- 累计处理超过10个文件时重置
+- 累计生成超过5000字内容时重置
+
+## 🛠️ **实用技巧**
+
+1. **阶段性总结**：在重要节点让我帮你总结当前进度，然后重置
+2. **项目分离**：不同项目使用不同会话，自然隔离上下文
+3. **文档化**：重要信息及时写入MEMORY.md，减少对上下文的依赖
+4. **主动重置**：当你感觉会话"变重"时，直接说"重置上下文"
+
+## 📝 **针对你的使用场景**
+
+考虑到你主要使用：
+- baoyu-post-to-wechat发布公众号文章
+- WeWrite内容创作
+- 代码项目开发
+
+**建议重置频率：**
+- **公众号发布**：每完成2-3篇文章的完整发布流程后重置
+- **内容创作**：每创作3-4篇文章后重置
+- **混合任务**：每工作2-3小时后重置
+
+**简单记忆法**：**"3-3-3规则"**
+- 3个中等任务
+- 或3小时连续工作
+- 或300条消息对话
+
+这样既能保证上下文有效性，又能避免性能下降。你觉得这个建议如何？需要我帮你设置定时提醒吗？
\ No newline at end of file
diff --git a/MyBestNanobot/AAA日常维修Nanobot.md b/MyBestNanobot/AAA日常维修Nanobot.md
new file mode 100644
index 0000000..5891173
--- /dev/null
+++ b/MyBestNanobot/AAA日常维修Nanobot.md
@@ -0,0 +1,143 @@
+
+
+
+---
+
+**配置分析：**
+
+| 配置项 | 当前值 | 问题 |
+|--------|--------|------|
+| `model` | `kimi-k2.6` | ✅ 支持 256k 上下文 |
+| `contextWindowTokens` | `65536` | ❌ 但请求达到了 **111万 tokens** |
+| `maxToolIterations` | `200` | ⚠️ 太高了，容易累积超长历史 |
+| `idleCompactAfterMinutes` | `0` | ❌ 禁用自动压缩，历史只会增长 |
+
+---
+
+**根本原因：**
+
+**历史消息积累了 111万 tokens**（约 84万字），远超 kimi-k2.6 的 256k 限制。
+
+即使 `contextWindowTokens` 设为 65536，系统还是会把完整历史发出去（可能是 bug 或设计如此），导致爆表。
+
+---
+
+**解决方案：**
+
+**方案 1：清理历史记录（立即生效）**
+删除历史文件：
+```bash
+rm ~/.nanobot/workspace/memory/history.jsonl
+```
+
+**方案 2：开启自动压缩（推荐）**
+修改 `idleCompactAfterMinutes` 为 10 或 30：
+```json
+"idleCompactAfterMinutes": 10
+```
+
+**方案 3：减小迭代次数**
+`maxToolIterations` 200 次太高了，改成 40：
+```json
+"maxToolIterations": 40
+```
+
+**方案 4：临时换小上下文模型**
+改用 `kimi-k2.5`（128k 上下文）或 `deepseek-chat`。
+
+---
+
+
+**不会每次都加载整个文件**，但你的情况比较特殊。
+
+---
+
+**history.jsonl 的工作机制：**
+
+1. **文件是 append-only** — 只追加不删除，所以会越积越大
+2. **正常情况下有压缩机制** — 超过 `contextWindowTokens` 会自动丢弃旧消息
+3. **但你的配置关掉了自动压缩** — `idleCompactAfterMinutes: 0` 表示"从不压缩"
+
+---
+
+**为什么你会爆表？**
+
+看错误信息：`requested: 1118406` tokens
+
+这意味着**111万 token 被塞进了请求**，而 kimi-k2.6 只支持 256k。说明：
+- 历史累积了太多轮对话
+- 自动压缩没触发或失效了
+- 加上 `maxToolIterations: 200`（太高，容易累积长链）
+
+---
+
+**解决方案：**
+
+**方案 1：开启自动压缩（推荐）**
+```json
+"idleCompactAfterMinutes": 10
+```
+空闲 10 分钟后自动压缩历史，保留最近的对话。
+
+**方案 2：定期手动清理**
+```bash
+# 备份后清空
+mv ~/.nanobot/workspace/memory/history.jsonl ~/.nanobot/workspace/memory/history.jsonl.bak
+touch ~/.nanobot/workspace/memory/history.jsonl
+```
+
+**方案 3：减小上下文窗口**
+```json
+"contextWindowTokens": 32768
+```
+强制只保留最近 32k tokens 的历史。
+
+**方案 4：限制迭代次数**
+```json
+"maxToolIterations": 40
+```
+200 次迭代容易累积超长工具调用链，40 次足够用。
+
+---
+
+
+
+
+
+
+## 🐈 nanobot commands:
+/new — Stop current task and start a new conversation
+/stop — Stop the current task
+/restart — Restart the bot
+/status — Show bot status
+/dream — Manually trigger Dream consolidation
+/dream-log — Show what the last Dream changed
+/dream-restore — Revert memory to a previous state
+/help — Show available commands
+以上命令可以在qq里面直接使用！
+
+---
+
+
+`/dream` 就是**手动触发"记忆整理"（Dream consolidation）**。
+
+**会发生什么：**
+
+1. 系统会立即分析当前 session 的对话历史
+2. 从中提取重要信息（比如你的偏好、项目变更、新的长期事实）
+3. 自动更新到以下三个文件：
+ - **MEMORY.md** — 长期记忆（项目目录、重要约定等）
+ - **USER.md** — 用户画像（称呼、职业、偏好等）
+ - **SOUL.md** — 助手人格和沟通风格
+
+**平时它是自动跑的** — cron 里配了每 **2 小时**自动执行一次 Dream 整理。`/dream` 就是让你不用等，想立即归档当前对话里的重要信息时手动按一下。
+
+还有个配套命令 `/dream-log`，可以查看 Dream 最近一次整理时具体做了什么改动。
+
+简单说：**觉得刚刚聊了什么值得记住的，发 `/dream` 让它马上归档。**
+
+
+
+
+
+
diff --git a/MyBestNanobot/Nanobot分析OpenCLI.md b/MyBestNanobot/Nanobot分析OpenCLI.md
new file mode 100644
index 0000000..cfc6510
--- /dev/null
+++ b/MyBestNanobot/Nanobot分析OpenCLI.md
@@ -0,0 +1,438 @@
+## OpenCLI 学习总结
+
+**OpenCLI** 是一个强大的 CLI 生成器和自动化工具，可以把任何网站变成命令行接口。
+
+### 核心能力
+
+| 功能 | 说明 |
+|------|------|
+| **内置适配器** | 87+ 网站开箱即用（小红书、知乎、B站、Twitter等）|
+| **浏览器自动化** | `opencli browser` 直接控制浏览器点击、输入、截图 |
+| **生成新适配器** | `explore → synthesize → generate` 从真实页面推导 CLI |
+| **Electron 应用控制** | 驱动 Cursor、ChatGPT、Notion 等桌面应用 |
+
+### 小红书适配器功能
+
+```bash
+opencli xiaohongshu publish --title "标题" "正文" --images a.jpg,b.jpg --topics 生活,旅行
+opencli xiaohongshu feed # 首页推荐
+opencli xiaohongshu note # 获取笔记详情
+opencli xiaohongshu download # 下载图片视频
+opencli xiaohongshu search <词> # 搜索笔记
+opencli xiaohongshu creator-notes # 创作者笔记列表
+opencli xiaohongshu creator-stats # 创作者数据统计
+```
+
+### 技术架构
+
+```
+src/
+├── main.ts # 入口：快速路径 + 完整启动
+├── cli.ts # CLI 注册和命令分发
+├── browser/ # CDP 浏览器控制
+│ ├── cdp.ts # Chrome DevTools Protocol
+│ ├── page.ts # 页面操作封装
+│ └── bridge.ts # 浏览器桥接
+├── registry.ts # 命令注册表
+├── commanderAdapter.ts # 动态命令适配
+└── discovery.ts # CLI 发现机制
+
+clis/ # 内置适配器（87+个）
+├── xiaohongshu/ # 小红书适配器
+├── zhihu/ # 知乎适配器
+├── bilibili/ # B站适配器
+└── ...
+```
+
+### 工作原理
+
+1. **Cookie 模式**：复用浏览器登录态，直接调用 API
+2. **Browser 模式**：通过 CDP 控制真实浏览器，模拟用户操作
+3. **生成模式**：`explore` 分析网站 → `synthesize` 推导接口 → `generate` 生成适配器
+
+### 使用示例
+
+```bash
+# 快速发布小红书
+opencli xiaohongshu publish --title "禅诗一首" "人人有这个..." \
+ --images /path/to/img.jpg --topics 禅诗,诗词
+
+# 浏览器自动化
+opencli browser --url https://example.com --click "button" --screenshot
+
+# 生成新网站适配器
+opencli generate https://new-site.com
+```
+
+---
+
+**已安装版本**: `1.7.3`
+
+这个项目非常强大，特别是**浏览器自动化**和**CLI 生成**功能，可以把任何 Web 操作变成可复用的命令行工具！
+
+---
+
+
+
+## OpenCLI Headless 配置方案
+
+OpenCLI 支持 **CDP (Chrome DevTools Protocol)** 模式，可以在无 GUI 服务器上运行。有三种方案：
+
+### 方案一：本地 Chrome + SSH 隧道（推荐）
+
+**在你的本地机器（Mac/PC）上：**
+
+```bash
+# 启动 Chrome 并开启远程调试
+google-chrome \
+ --remote-debugging-port=9222 \
+ --user-data-dir="$HOME/chrome-debug-profile" \
+ --remote-allow-origins="*"
+
+# 登录小红书/知乎等网站
+```
+
+**在服务器上建立反向 SSH 隧道：**
+
+```bash
+# 从服务器连接到本地机器，转发端口
+ssh -R 9222:localhost:9222 your-local-machine
+
+# 设置环境变量
+export OPENCLI_CDP_ENDPOINT="http://localhost:9222"
+
+# 验证连接
+opencli doctor
+
+# 使用
+opencli xiaohongshu feed
+```
+
+---
+
+### 方案二：服务器直接运行 Headless Chrome（CI/CD 模式）
+
+```bash
+# 安装 Chrome 和 xvfb
+sudo apt-get install -y google-chrome-stable xvfb
+
+# 使用 xvfb-run 运行 Chrome（虚拟显示）
+xvfb-run --auto-servernum \
+ google-chrome \
+ --remote-debugging-port=9222 \
+ --no-sandbox \
+ --disable-dev-shm-usage \
+ --disable-gpu \
+ --headless=new &
+
+# 设置环境变量
+export OPENCLI_CDP_ENDPOINT="http://127.0.0.1:9222"
+
+# 运行
+opencli xiaohongshu publish --title "测试" "内容" --images a.jpg
+```
+
+---
+
+### 方案三：Docker 运行 Chrome
+
+```bash
+# 运行 headless Chrome 容器
+docker run -d \
+ --name chrome-headless \
+ -p 9222:9222 \
+ --cap-add=SYS_ADMIN \
+ chromedp/headless-shell:latest \
+ --remote-debugging-address=0.0.0.0 \
+ --remote-debugging-port=9222
+
+# 设置环境变量
+export OPENCLI_CDP_ENDPOINT="http://localhost:9222"
+```
+
+---
+
+### 关键环境变量
+
+| 变量 | 说明 | 示例 |
+|------|------|------|
+| `OPENCLI_CDP_ENDPOINT` | CDP 端点地址 | `http://127.0.0.1:9222` |
+| `OPENCLI_CDP_TARGET` | 选择特定目标 | `xiaohongshu`, `bilibili` |
+| `OPENCLI_BROWSER_CONNECT_TIMEOUT` | 连接超时(秒) | `30` |
+| `OPENCLI_BROWSER_COMMAND_TIMEOUT` | 命令超时(秒) | `60` |
+
+---
+
+### 架构对比
+
+```
+┌─────────────────────────────────────────────────────────┐
+│ 方案一: 本地 Chrome + SSH 隧道 │
+│ ┌──────────┐ SSH ┌──────────────┐ │
+│ │本地Chrome│◄─────────────│ 远程服务器 │ │
+│ │:9222 │ -R 9222 │ opencli命令 │ │
+│ └──────────┘ └──────────────┘ │
+└─────────────────────────────────────────────────────────┘
+
+┌─────────────────────────────────────────────────────────┐
+│ 方案二: 服务器 Headless Chrome │
+│ ┌─────────────────────────────────────────────┐ │
+│ │ 服务器 │ │
+│ │ ┌─────────────┐ ┌─────────────────────┐ │ │
+│ │ │ xvfb-run │───►│ google-chrome │ │ │
+│ │ │ (虚拟显示) │ │ --remote-debugging │ │ │
+│ │ └─────────────┘ └─────────────────────┘ │ │
+│ │ :9222 │ │
+│ │ ▲ │ │
+│ │ │ │ │
+│ │ ┌───────────────────────┘ │ │
+│ │ │ opencli (CDPBridge连接) │ │
+│ │ └──────────────────────────────────────────┘ │
+│ └─────────────────────────────────────────────┘ │
+└─────────────────────────────────────────────────────────┘
+```
+
+**推荐方案一**：因为小红书等网站需要登录状态，本地 Chrome 可以保持登录，服务器通过隧道复用。
+
+
+
+---
+
+
+## 是的！**提供 Cookie 就能生成新适配器**
+
+OpenCLI 的 `explore → synthesize → generate` 流程可以自动分析网站并生成 CLI 适配器：
+
+### 工作流程
+
+```
+┌─────────────┐ ┌─────────────┐ ┌─────────────┐
+│ explore │ ──► │ synthesize │ ──► │ generate │
+│ 深度探索 │ │ 合成 CLI │ │ 生成代码 │
+└─────────────┘ └─────────────┘ └─────────────┘
+```
+
+### 具体步骤
+
+**1. Explore（探索）- 自动发现 API**
+
+```bash
+opencli explore https://example.com \
+ --site mysite \
+ --wait-seconds 5
+```
+
+OpenCLI 会：
+- 启动浏览器访问页面
+- **自动滚动**触发懒加载
+- **捕获所有网络请求** (XHR/Fetch)
+- **分析 JSON 响应**结构
+- 识别认证方式 (Cookie/Token)
+- 检测前端框架 (React/Vue/Angular)
+
+输出：`~/.opencli/explore/mysite_20240414_xxxx/` 包含：
+- `manifest.json` - 站点元数据
+- `endpoints.json` - 发现的所有 API 端点
+- `capabilities.json` - 推断的功能列表
+
+---
+
+**2. Synthesize（合成）- 生成 CLI 候选**
+
+```bash
+opencli synthesize mysite
+```
+
+根据探索结果生成 **YAML 格式的 CLI 定义**：
+- 推荐命令名称 (`search`, `hot`, `feed`, `detail`...)
+- 参数映射 (`--limit`, `--keyword`, `--page`)
+- 输出列定义
+- 执行管道 (navigate → wait → evaluate → map)
+
+输出：`candidates/search.yaml`
+
+```yaml
+site: mysite
+name: search
+description: Search content on mysite
+strategy: cookie
+browser: false
+args:
+ keyword:
+ type: string
+ required: true
+ description: Search keyword
+ limit:
+ type: number
+ required: false
+ default: 10
+pipeline:
+ - fetch:
+ url: "https://api.example.com/search?q={{keyword}}&limit={{limit}}"
+ - map:
+ title: "data.items[*].title"
+ url: "data.items[*].link"
+```
+
+---
+
+**3. Generate（生成）- 一键创建**
+
+```bash
+# 全自动：explore → synthesize → verify → register
+opencli generate https://example.com --goal search
+```
+
+或直接：
+```bash
+opencli generate https://example.com \
+ --site mysite \
+ --wait-seconds 5 \
+ --top 3
+```
+
+---
+
+### 实际例子：给小红书生成新命令
+
+假设小红书有个新功能 "创作者数据中心" 没有现成适配器：
+
+```bash
+# 1. 探索创作者中心页面（携带 Cookie）
+export OPENCLI_CDP_ENDPOINT="http://localhost:9222"
+
+opencli explore https://creator.xiaohongshu.com/statistics \
+ --site xiaohongshu-creator \
+ --wait-seconds 10
+
+# 2. 合成候选命令
+opencli synthesize xiaohongshu-creator
+
+# 3. 查看生成的候选
+ls ~/.opencli/explore/xiaohongshu-creator_*/candidates/
+
+# 4. 验证并注册
+opencli verify xiaohongshu-creator/candidates/stats.yaml
+opencli register xiaohongshu-creator/candidates/stats.yaml
+
+# 5. 使用！
+opencli xiaohongshu-creator stats --start-date 2024-04-01
+```
+
+---
+
+### Cookie 的作用
+
+| 模式 | 是否需要 Cookie | 说明 |
+|------|----------------|------|
+| **API 模式** | ✅ 需要 | 直接调用后端 API，需认证头/Cookie |
+| **Browser 模式** | ✅ 需要 | 控制真实浏览器，需登录态 |
+| **Store 模式** | 可选 | 从 Pinia/Vuex 读取，有时无需认证 |
+
+---
+
+### 关键技术点
+
+**explore 阶段捕获的数据：**
+```javascript
+// 网络请求拦截
+{
+ url: "https://edith.xiaohongshu.com/api/sns/web/v1/search/notes",
+ method: "POST",
+ headers: {
+ "Cookie": "web_session=xxx...",
+ "X-Sign": "xxx" // 签名算法也被记录
+ },
+ requestBody: { keyword: "xxx", page: 1 },
+ responseBody: { data: { items: [...] } }
+}
+```
+
+**自动推断：**
+- `keyword` → `--keyword` 参数
+- `page` → `--page` 参数（带默认值 1）
+- `items` 数组 → 输出表格的行
+- `data.items[*].title` → `title` 列
+
+---
+
+### 总结
+
+> **你给我一个网站 + Cookie，我就能自动生成 CLI 适配器**
+
+不需要手写代码，OpenCLI 通过**真实浏览器行为分析**自动推导：
+- API 端点
+- 参数映射
+- 认证方式
+- 数据提取路径
+
+这是它的核心创新点：**从真实交互中"学习"网站 API，而非阅读文档**。
+
+
+
+
+---
+
+我更新了小红书的cookie，在 /home/ubuntu/xiaohongshu_cookies.txt
+你根据这个cookie，实现一个我们自己的 my_iaohongshu_cli
+在我们的git仓库 /home/ubuntu/nanocode目录下创建一个目录my_iaohongshu_cli，来实现这个cli
+
+
+
+
+现在我完全理清了！让我给你完整梳理：
+
+---
+
+## 📊 小红书发布方式现状
+
+### **CLI 工具**（实际可执行命令）
+
+| 工具 | 路径/来源 | 状态 | 说明 |
+|------|----------|------|------|
+| **`xhs`** | `uv tool install xiaohongshu-cli` | ✅ **在用** | 原版 v0.6.4，从浏览器提取 Cookie |
+| **`myxhs`** | `/home/ubuntu/nanocode/my_xiaohongshu_cli/` | ✅ **在用** | 我刚开发的，用本地 Cookie 文件 |
+| `opencli xiaohongshu` | npm @jackwener/opencli | ❌ **已弃用** | 无适配器 (clis 目录为空) |
+
+---
+
+### **Skills**（nanobot 的指导文件）
+
+| Skill | 用途 | 调用的工具 |
+|-------|------|-----------|
+| `xiaohongshu-cli` | 指导使用 `xhs` 命令 | → 调用 `xhs` |
+| `xiaohongshu-poster` | 指导生成图文+发布 | → 调用 `xhs post` |
+| `xiaohongshu-converter` | 内容转换 | ? |
+| `xiaohongshu_integration` | 集成相关 | ? |
+| `opencli-integration` | 指导使用 opencli | → **已无用** |
+
+---
+
+### **关键结论**
+
+1. **`myxhs` 是工具，不是 Skill**
+ - 它只是 `/home/ubuntu/.local/bin/myxhs` 这个 CLI 命令
+ - 还没有对应的 Skill 文件来指导我使用它
+
+2. **实际上只有 2 个有效的 CLI 工具**
+ - `xhs` - 原版，自动从浏览器取 Cookie
+ - `myxhs` - 我开发的，读本地文件 `/home/ubuntu/xiaohongshu_cookies.txt`
+
+3. **OpenCLI 已废弃**
+ - `.openclaw/` 目录已删（之前清理过）
+ - `~/.opencli/clis/` 是空的，没有小红书适配器
+ - `opencli-integration` 这个 Skill 应该删除
+
+4. **GitHub 仓库关系**
+ - `jackwener/xhs-cli` 和 `jackwener/xiaohongshu-cli` 是**同一个项目**
+ - PyPI 包名是 `xiaohongshu-cli`，安装后是 `xhs` 命令
+
+---
+
+### **建议**
+
+要不要我：
+1. 创建一个 `my-xiaohongshu-cli` 的 Skill 文件，指导我使用 `myxhs`？
+2. 删除废弃的 `opencli-integration` Skill？
+3. 或者统一只用 `xhs`，不再维护 `myxhs`？
+4.
\ No newline at end of file
diff --git a/MyBestNanobot/Nanobot复制迁移和容器化的可能性.md b/MyBestNanobot/Nanobot复制迁移和容器化的可能性.md
index 6f172c0..298d493 100644
--- a/MyBestNanobot/Nanobot复制迁移和容器化的可能性.md
+++ b/MyBestNanobot/Nanobot复制迁移和容器化的可能性.md
@@ -1,3 +1,188 @@
+
+
+
+# 1 复制 .nanobot目录
+# 2 初始化运行环境
+
+## 2.1 个人 Telegram Bot
+nanobot onboard --config ~/.nanobot-personal/config.json \
+ --workspace ~/.nanobot-personal/workspace
+
+## 2.2 工作 Slack Bot
+nanobot onboard --config ~/.nanobot-work/config.json \
+ --workspace ~/.nanobot-work/workspace
+
+
+# 3启动Agent
+
+nanobot gateway --config ~/.nanobot-personal/config.json --port 18796
+nanobot gateway --config ~/.nanobot-work/config.json
+
+
+# 4 创建不同的系统服务
+
+
+**两个服务都已成功启动并运行！** ✅
+
+| 服务 | 配置 | 端口 | 状态 |
+|------|------|------|------|
+| **nanobot-gateway** | /home/ubuntu/.nanobot/config.json | 18790 | ✅ active |
+| **nanobot-gateway2** | /home/ubuntu/.nanobot2/config.json | 18796 | ✅ active |
+
+**注意：** 当前手动运行的 nanobot 进程（PID 444830）需要停止，因为 systemd 服务已经在运行。需要我帮你停止手动进程吗？
+
+**创建新服务的时候，一定一定需要告诉nanobot，新服务的名称，否则很有可能就把前面的nanobot服务名称给替换掉了@@@@@@@@！！！！！！！**
+
+
+**两个服务文件内容：**
+
+---
+
+**nanobot-gateway.service** (端口 18790)
+
+```
+[Unit]
+Description=Nanobot Gateway Service (Port 18790)
+After=network.target
+
+[Service]
+Type=simple
+User=ubuntu
+Group=ubuntu
+WorkingDirectory=/home/ubuntu/.nanobot/workspace
+Environment="HOME=/home/ubuntu"
+Environment="PATH=/home/ubuntu/.local/bin:/usr/local/bin:/usr/bin:/bin"
+Environment="UV_PYTHON=/home/ubuntu/.local/share/uv/tools/nanobot-ai/bin/python3"
+ExecStart=/home/ubuntu/.local/bin/nanobot gateway --config /home/ubuntu/.nanobot/config.json --port 18790
+Restart=always
+RestartSec=5
+StandardOutput=journal
+StandardError=journal
+
+[Install]
+WantedBy=multi-user.target
+```
+
+---
+
+**nanobot-gateway2.service** (端口 18796)
+
+```
+[Unit]
+Description=Nanobot Gateway Service 2 (Port 18796)
+After=network.target
+
+[Service]
+Type=simple
+User=ubuntu
+Group=ubuntu
+WorkingDirectory=/home/ubuntu/.nanobot2/workspace
+Environment="HOME=/home/ubuntu"
+Environment="PATH=/home/ubuntu/.local/bin:/usr/local/bin:/usr/bin:/bin"
+Environment="UV_PYTHON=/home/ubuntu/.local/share/uv/tools/nanobot-ai/bin/python3"
+ExecStart=/home/ubuntu/.local/bin/nanobot gateway --config /home/ubuntu/.nanobot2/config.json --port 18796
+Restart=always
+RestartSec=5
+StandardOutput=journal
+StandardError=journal
+
+[Install]
+WantedBy=multi-user.target
+```
+
+
+
+**服务管理命令：**
+
+---
+
+**nanobot-gateway** (端口 18790 - 当前这个我)
+
+```bash
+# 启动
+sudo systemctl start nanobot-gateway
+
+# 重启
+sudo systemctl restart nanobot-gateway
+
+# 停止
+sudo systemctl stop nanobot-gateway
+
+# 查看状态
+sudo systemctl status nanobot-gateway
+
+# 开机启动
+sudo systemctl enable nanobot-gateway
+```
+
+---
+
+**nanobot-gateway2** (端口 18796)
+
+```bash
+# 启动
+sudo systemctl start nanobot-gateway2
+
+# 重启
+sudo systemctl restart nanobot-gateway2
+
+# 停止
+sudo systemctl stop nanobot-gateway2
+
+# 查看状态
+sudo systemctl status nanobot-gateway2
+
+# 开机启动
+sudo systemctl enable nanobot-gateway2
+```
+
+---
+
+**常用快捷命令：**
+
+```bash
+# 同时重启两个
+sudo systemctl restart nanobot-gateway nanobot-gateway2
+
+# 同时查看状态
+sudo systemctl status nanobot-gateway nanobot-gateway2
+
+# 查看日志
+sudo journalctl -u nanobot-gateway -f
+sudo journalctl -u nanobot-gateway2 -f
+```
+
+
+---
+
+**我的运行环境** ✅
+
+- **工作目录**: `/home/ubuntu/.nanobot2/workspace`
+- **配置文件**: `~/.nanobot2/config.json`
+- **配置中的 workspace**: `/home/ubuntu/.nanobot2/workspace`
+
+确实是在 `.nanobot2` 环境下运行。
+
+---
+
+**当前 nanobot 进程**
+
+| PID | 命令 | 说明 |
+| ------ | --------------------------------------------------------------- | ------------------------------- |
+| 1550 | `nanobot gateway` | 另一个 gateway 实例（无 `--config` 参数） |
+| 443106 | `nanobot gateway --config ~/.nanobot2/config.json --port 18796` | **我（当前会话）** |
+
+---
+
+**关于 onboard 实例**
+
+没有找到 `nanobot onboard` 进程。可能：
+- 之前运行过但已退出
+- 或者记混了（可能是 PID 1550 那个 gateway 实例？）
+
+需要我帮你清理 PID 1550 的那个 gateway 实例吗？或者你想确认一下 onboard 进程的状态？
+
+
## 🔄 **Nanobot 克隆部署方案**
### **当前架构分析**
@@ -323,3 +508,5 @@ docker exec -it nanobot-main nanobot --version
**蔡叔叔，现在的内存做日常任务够用，但跑多个Docker实例会比较紧张。建议先不加容器，或者升级服务器内存到4GB后再部署。** 🐈
+---
+
diff --git a/MyBestNanobot/Nanobot斗地主游戏开发及游戏总结要点.md b/MyBestNanobot/Nanobot斗地主游戏开发及游戏总结要点.md
index 1136ccc..616ceee 100644
--- a/MyBestNanobot/Nanobot斗地主游戏开发及游戏总结要点.md
+++ b/MyBestNanobot/Nanobot斗地主游戏开发及游戏总结要点.md
@@ -491,4 +491,8 @@ function aStarSearch(currentState) {
| 快速实现、可解释、实时响应 | ✅ 规则型 AI |
| 研究性质、有完整信息、离线计算 | ✅ A* 或蒙特卡洛 |
-**斗地主选规则型 AI 是正确选择** —— 不是因为 A* 不强大，而是斗地主的"不完美信息"特性让 A* 无从下手。规则型 AI 更适合这个场景。
\ No newline at end of file
+**斗地主选规则型 AI 是正确选择** —— 不是因为 A* 不强大，而是斗地主的"不完美信息"特性让 A* 无从下手。规则型 AI 更适合这个场景。
+
+---
+
+
diff --git a/MyBestNanobot/Nanobot的定时任务.md b/MyBestNanobot/Nanobot的定时任务.md
index ed5b9a6..49daf12 100644
--- a/MyBestNanobot/Nanobot的定时任务.md
+++ b/MyBestNanobot/Nanobot的定时任务.md
@@ -133,4 +133,35 @@ ls -lh /home/ubuntu/nanocode/backup_logs/
2. **增加监控**：可以添加cron任务执行状态监控
3. **定期清理**：日志文件可能需要定期清理
-需要我帮你检查某个特定任务的执行情况，或者调整cron配置吗？
\ No newline at end of file
+需要我帮你检查某个特定任务的执行情况，或者调整cron配置吗？
+
+
+完成！**3个任务已暂停（注释）**：
+
+| 原时间 | 已暂停的任务 | 脚本路径 |
+| ----- | ---------------------------- | ------------------------------------------------------------------------------------- |
+| 10:00 | 小红书热点趋势自动化 | `/home/ubuntu/nanocode/xhs_trending_automation/scripts/scheduler.sh` |
+| 16:00 | IT资讯自动化 (InformationCrawler) | `/home/ubuntu/.nanobot/workspace/InformationCrawler/main_scheduler_with_xhs_fixed.sh` |
+| 16:00 | IT资讯自动化完整版 | `/home/ubuntu/nanocode/it_automation_enhanced/scripts/main_scheduler_complete.sh` |
+
+---
+
+**文件路径汇总**：
+```bash
+# 10:00 任务
+/home/ubuntu/nanocode/xhs_trending_automation/scripts/scheduler.sh
+
+# 16:00 任务（2个）
+/home/ubuntu/.nanobot/workspace/InformationCrawler/main_scheduler_with_xhs_fixed.sh
+/home/ubuntu/nanocode/it_automation_enhanced/scripts/main_scheduler_complete.sh
+```
+
+---
+
+**恢复方法**（以后想恢复时执行）：
+```bash
+# 取消注释（去掉行首的 #）
+crontab -l | sed 's/^# \(0 \(10\|16\) \* \* \*.*\)/\1/' | crontab -
+```
+
+需要现在恢复吗？还是保持暂停？
\ No newline at end of file
diff --git a/MyBestNanobot/Nanobot的最新公众号发布流程.md b/MyBestNanobot/Nanobot的最新公众号发布流程.md
index c751935..bc6ae5a 100644
--- a/MyBestNanobot/Nanobot的最新公众号发布流程.md
+++ b/MyBestNanobot/Nanobot的最新公众号发布流程.md
@@ -1,4 +1,90 @@
+
+
+---
+
+## 1. 发公众号文章的完整流程
+
+依赖 **baoyu-post-to-wechat** 这个 skill，大致分这几步：
+
+**① 文章改编**
+- 你给我一个主题 + 原始文章（可能是链接、文档、或者直接贴文字）
+- 我基于你的风格和公众号定位进行改写、润色、分段
+- 如果你需要配图，我会调用 **baoyu-imagine** skill 生成信息图 / 配图（用的是 Seedream 模型，不走我的 token）
+
+**② 准备发布内容**
+- 把改写好的文章整理成适合微信公众号的格式（支持纯文本或 HTML）
+- 如果需要多图、图文混排，也一并处理
+
+**③ 调用发布接口**
+- `baoyu-post-to-wechat` 通过微信公众平台的 API 直接发布
+- 支持图文混排、多图上传、HTML 内容、AI 图片生成
+- 用的是 WeWrite 同一套账号配置，所以不需要你手动登录
+
+---
+
+## 2. 博客是怎么插入的？
+
+这个走的是另一条路径——**playwright-blog-automation** skill + 你的 **personal-blog** 项目，两条腿走路：
+
+**方式 A：Playwright 自动化发布（实际在用）**
+- 你的博客后台：`http://129.211.173.87:8888/`（admin/abcd1234）
+- 我用 Playwright 打开浏览器 → 登录 → 点「写文章」→ 填入标题和内容 → 发布
+- 这个方式走的是真实浏览器操作，适合单次快速发布
+
+**方式 B：直接写项目文件（更 structured）**
+- 你的 personal-blog 项目在 `~/nanocode/personal-blog`
+- 如果是静态博客（比如 Next.js / Hexo 之类），我会直接在项目里新建 Markdown 文章文件
+- 然后跑构建命令（比如 `npm run build` 或 `hexo generate`）重新生成静态页面
+- 这样文章就永久保存在你的项目里了，方便版本管理和后续修改
+
+---
+
+**简单总结：**
+- 公众号 → 走 API 直接发布，全自动
+- 博客 → 要么 Playwright 登录后台发，要么写项目文件 + 构建
+
+两个渠道可以同步进行，同一篇文章改完格式后两边一起发。你现在是有文章要发吗？
+
+---
+对，这个**双平台同步**是 `baoyu-post-to-wechat` skill 中的 `baoyu_optimized.py` 脚本内建的功能，不是我额外控制的——它是 v4.0 版本新增的特性。当你运行命令发布到公众号时，它会自动：
+
+1. **发布到公众号**（微信API）
+2. **同步到个人博客**（把图片复制到博客 uploads 目录 + 创建博客文章）
+
+两条线路互不阻塞，公众号发布失败不影响博客，反之亦然。
+
+所以我现在直接运行发布命令，就会两边一起发。开始发布文章？
+
+---
+
+好问题，直接看代码确认的答案：
+
+**1. blog_sync 不是由 `playwright-blog-automation` 触发的。**
+
+它直接内嵌在 `baoyu_optimized.py`（属于 `baoyu-post-to-wechat` 技能）的 `publish_markdown_with_images()` 方法里。发布 Markdown 时，第 557-563 行自动调用 `blog_sync.sync_markdown_to_blog()`，用的是 REST API（端口 8889）而非 Playwright。而 `playwright-blog-automation` 是用于你直接说"发布博客"时走浏览器自动化那条路的。
+
+**2. 没有任何关键字/提示词匹配来决定是否同步博客。**
+
+它是硬编码的 —— 只要用 `baoyu_optimized.py` 的 `markdown` 命令，博客同步**无条件执行**（外面包了 try/except，失败不影响公众号发布）。也就是说，我让你发布公众号时，博客同步自动就跑了，不是我"决定"要同步。
+
+不过注意：如果用 `publish_html()`（html 命令），是没有博客同步的。只有 `markdown` 命令（即 `baoyu_optimized.py markdown <文件>`）才会自动同步。
+
+上次执行时 blog_sync 因为缺 `sqlalchemy` 依赖而失败，但公众号发布完全不受影响，符合设计上"独立支线互不阻塞"的原则。
+
+---
+
+
+
+
+你通过baoyu-post-to-wechat这个技能，改写以下内容（要求不少于800字），如果公众号的id和key找不到可以查看/home/ubuntu/wewrite/config.yaml文件，发布公众号文章 ：
+
+我闺蜜这两天和她的二婚老公闹掰了，又准备离婚了。本就是二婚，再离一次就三婚了。我觉得女人呀，不得已还是不要轻易的离婚，只要原配不犯原则性的大错能原谅就原谅，安安稳稳过日子多好。
+二婚夫妻都是互相算计着过的。看我闺蜜，和二婚老公结婚5年，生了个小闺女刚上幼儿园，她在家带孩子又不能上班，老公在单位上班，具体挣多少钱她也不清楚，反正一个月给3000生活费，多一分没有，她在家侍候一家老小吃喝。
+她和前夫有个儿子在外打工，已经到了结婚年龄，想买房首付都凑不上，她老公一点忙也不帮。她老公名下有两套房子，一套自住，一套出租。我闺蜜让她老公把那套房子过户给她儿子当婚房，没成想老公直接恼了，说为啥他要给别人儿子房子，儿子又不是他的。这话闺蜜堵的哑口无言～～顿时寒了心
+想想这样的日子过的真是没劲呐，这二婚婚姻，到底图个啥
+
+
## 🎉 **完美！增强版 baoyu-post-to-wechat 测试成功！**
### ✅ **测试结果**
diff --git a/MyBestNanobot/Nanobot的最新小红书发布流程.md b/MyBestNanobot/Nanobot的最新小红书发布流程.md
index 559d5c7..8e6af3a 100644
--- a/MyBestNanobot/Nanobot的最新小红书发布流程.md
+++ b/MyBestNanobot/Nanobot的最新小红书发布流程.md
@@ -1,3 +1,12 @@
+
+使用/home/ubuntu/images 下的 074 图片，使用/home/ubuntu/fonts 下的 Huiwenmincho 字体，黑色字体，字体尽可能大，但是左右需要适量留白，图片下方有人物图案，文字不要遮住图案的同时保证图片顶部有留白，发布小红书笔记，小红书的cookies在/home/ubuntu/xiaohongshu_cookies.txt，文案如下：
+
+若言梦是空，觉後应无记。
+若言梦非空，应有真实事。
+燔烧阳自招，沈溺阴自致。
+令汝尝惊魇，岂知安稳睡。
+
+
## **✅ 已记录并创建 Skill！**
### **📁 记录位置**
diff --git a/MyBestNanobot/nanobot 在 Windows 作为系统服务运行.md b/MyBestNanobot/nanobot 在 Windows 作为系统服务运行.md
new file mode 100644
index 0000000..e30e723
--- /dev/null
+++ b/MyBestNanobot/nanobot 在 Windows 作为系统服务运行.md
@@ -0,0 +1,82 @@
+好问题！nanobot 在 Windows 上主要有两种方式实现开机自启，推荐用 **任务计划程序**（最简单，无需额外工具）或 **NSSM**（真正的系统服务）。
+
+先确认启动命令——从状态看，你用的是 `nanobot gateway`：
+
+---
+
+## 方式一：任务计划程序（推荐，最简单）
+
+无需装任何东西，开机登录时自动启动：
+
+**1. 按 `Win + R`，输入 `taskschd.msc` 打开任务计划程序**
+
+**2. 右侧点击「创建任务」**
+
+**3. 配置如下：**
+
+| 选项卡 | 设置 |
+|--------|------|
+| **常规** | 名称：`nanobot` 勾选「使用最高权限运行」 选择「不管用户是否登录都要运行」（或「只在用户登录时运行」） |
+| **触发器** | 新建 → 开始任务：**启动时**（或**登录时**） |
+| **操作** | 新建 → 操作：**启动程序** 程序或脚本：`C:\Users\niaod\.local\bin\nanobot.exe` 添加参数：`gateway` 起始于（可选）：`C:\Users\niaod\.nanobot\workspace` |
+| **条件** | 取消勾选「只有在计算机使用交流电源时才启动此任务」 |
+| **设置** | 勾选「如果任务失败，按以下频率重启」设为 1 分钟 「允许按需运行任务」 |
+
+**4. 确定后输入 Windows 密码即可。**
+
+> ✅ 优点是零依赖，支持重启、日志记录。缺点是只在用户登录后才启动。
+
+---
+
+## 方式二：NSSM — 包装为真正的 Windows 系统服务
+
+如果你想要一个在系统启动时（**甚至在登录前**）就运行、后台常驻、不弹出窗口的真 · 服务：
+
+### 1️⃣ 下载 NSSM
+
+```powershell
+winget install nssm
+# 或手动下载：https://nssm.cc/download
+```
+
+### 2️⃣ 创建服务
+
+```powershell
+# 管理员 PowerShell
+nssm install nanobot
+```
+
+在弹出的 GUI 窗口中设置：
+
+- **Application** → Path: `C:\Users\niaod\.local\bin\nanobot.exe`
+- **Application** → Arguments: `gateway`
+- **Startup directory**: `C:\Users\niaod\.nanobot\workspace`
+- **Details** → Display name: `nanobot AI Assistant`
+- **Details** → Startup type: **Automatic**
+
+点 Install Service，搞定。
+
+### 管理命令
+
+```powershell
+nssm start nanobot # 启动服务
+nssm stop nanobot # 停止
+nssm restart nanobot # 重启
+nssm edit nanobot # 修改配置
+```
+
+---
+
+## 方式三：开机启动文件夹（最简但最弱）
+
+```powershell
+# Win+R → shell:startup
+# 创建一个快捷方式：
+目标: C:\Users\niaod\.local\bin\nanobot.exe gateway
+```
+
+缺点是会有命令行窗口，而且没有自动重启。
+
+---
+
+**我建议你用方式一（任务计划程序）**，零安装、带重启策略、足够稳定。你目前用的是 `gateway` 还是 `serve` 命令？
\ No newline at end of file
diff --git a/MyBestNanobot/nanobot的日常.md b/MyBestNanobot/nanobot的日常.md
index ee41987..a99d035 100644
--- a/MyBestNanobot/nanobot的日常.md
+++ b/MyBestNanobot/nanobot的日常.md
@@ -1,5 +1,7 @@
✅ **已记录！**
+
+
**小红书搜索流程已存档：**
```
xhs search "关键词" --sort popular/latest --json
@@ -184,4 +186,5 @@ deb https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ noble-backports main restricted
deb https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ noble-security main restricted universe multiverse
EOF
-sudo apt update -y
\ No newline at end of file
+sudo apt update -y
+
diff --git a/MyBestNanobot/类似于Kimi2.5, 2.6这样的多模态的大模型，从图片上识别文字的原理是什么.md b/MyBestNanobot/类似于Kimi2.5, 2.6这样的多模态的大模型，从图片上识别文字的原理是什么.md
new file mode 100644
index 0000000..c8b5271
--- /dev/null
+++ b/MyBestNanobot/类似于Kimi2.5, 2.6这样的多模态的大模型，从图片上识别文字的原理是什么.md
@@ -0,0 +1,198 @@
+
+
+多模态大模型做图像文字识别（OCR），本质上是**让LLM直接"看懂"图片里的文字**，而不是走传统OCR那条"先检测文字区域 → 再逐个识别 → 最后排版"的流水线。
+
+核心机制可以拆成三步：
+
+**1. 视觉编码（Vision Encoder）**
+图像被切成小方块（patches），经过ViT（Vision Transformer）转成一串"视觉token"。这些token携带了图像的语义信息——包括文字的形状、位置、上下文。
+
+**2. 对齐投影（Projection Layer）**
+视觉token的维度和大语言模型的文本token维度不同，需要一个投影层（也叫Adapter或Connector）把视觉特征映射到LLM能理解的语义空间里。这样图片信息和文字信息就在同一个向量空间了。
+
+**3. 统一解码（Unified LLM）**
+视觉token和文本prompt（比如"图中写了什么"）拼接成一条序列，送进LLM做自回归生成。模型学到的不是"这个字长什么样"，而是**"这个视觉patch对应的文本token应该是什么"**。
+
+---
+
+**和传统OCR的关键区别：**
+
+- **端到端**：没有独立的文字检测模块，模型自己决定看哪里、读什么
+- **上下文理解**：能结合场景理解文字。比如看到路牌上的"STOP"，不只是识别字母，还知道这是交通标志
+- **分辨率处理**：为了看清小字，通常会做高分辨率切分（把大图拆成几块分别编码）或动态分辨率缩放
+
+**训练层面：**
+靠的是海量图文对（image-text pairs）的预训练，让模型建立视觉特征和文字token之间的统计关联。后期再用专门的OCR数据微调，提升对密集文字、表格、手写体的识别率。
+
+所以Kimi这类模型不是"长了眼睛的OCR引擎"，而是**把OCR变成了语言模型的token预测问题**。
+
+- **中文 + 工业级**：PaddleOCR-VL（精度最高，功能最全）
+- **长文档 + 高效率**：DeepSeek-OCR（压缩比高，显存友好）
+- **轻量 + 边缘部署**：GLM-OCR（0.9B，低显存）
+- **办公表格 + 稳定**：HunyuanOCR（腾讯背书，版式还原好）
+- **快速验证 + 多语种**：EasyOCR（开箱即用，80 + 语言）
+-
+
+# PaddleOCR
+https://github.com/PaddlePaddle/PaddleOCR
+
+# GLM-OCR 是一款面向复杂文档理解的多模态 OCR 模型
+
+https://github.com/zai-org/GLM-OCR/blob/main/README_zh.md
+
+# EasyOCR
+
+https://github.com/JaidedAI/EasyOCR
+
+# Deepseek OCR
+
+https://github.com/deepseek-ai/DeepSeek-OCR
+https://github.com/rdumasia303/deepseek_ocr_app
+
+
+
+
+# 尿检试纸识别
+
+
+
+我再来问一个多模态的问题。我先描述一下这个图片，**这是一个14项尿常规尿检试纸，试纸两端是两个红色定位块，从红色定位块的边缘算起，计算两点之间的距离，进行距离14等分，读取每个等分的中心点的RGB颜色值，就得到尿检试纸的颜色值。** 如果我给你一个这个图片，结合我的上述介绍，你怎么完成这个任务？技术原理是什么？
+
+---
+
+
+这是个非常好的问题，而且刚好踩到了当前多模态大模型的**能力边界**上。
+
+先说结论：**纯多模态大模型（如Kimi、GPT-4V）只能完成这个任务的"前半段"，精确的像素级RGB读取必须交给代码来做。**
+
+---
+
+**大模型能做什么**
+
+拿到这张图，模型通过ViT视觉编码器能：
+- 识别出"这是一条尿检试纸，两端有红色参考块"
+- 数出"中间大约有14个色块区域"
+- 描述各区域的大致颜色（"左数第2个偏蓝绿色"、"第5个是橙黄色"）
+- 理解你描述的"14等分"规则
+
+**大模型做不到什么（核心局限）**
+
+你要求的**精确像素坐标定位 + 精确RGB数值提取**，端到端大模型做不了。原因在底层架构：
+
+1. **分辨率损失**：图像被切成patches（比如14×14像素为一个视觉token），模型看到的不是原始像素矩阵，而是patch聚合后的统计特征。一个token可能覆盖了几十个像素，它无法精确定位"红色定位块的边缘到底在第几列"。
+
+2. **没有数值读取能力**：LLM生成的是文本token，RGB值（比如`#4A90D9`）是它根据视觉特征"猜"出来的，不是从像素里"读"出来的。误差可能很大，尤其是相近颜色。
+
+3. **空间定位模糊**：模型能知道"红色块在左边"，但给不出精确的像素坐标`(x1, y1, x2, y2)`，也就无法做严格的几何14等分。
+
+---
+
+**真正的技术方案：大模型理解 + 代码执行**
+
+工程上这类任务的标准做法是**视觉理解 → 代码生成 → 程序执行**：
+
+1. **大模型**：识别试纸在图中的大致位置，确认红色定位块
+2. **生成代码**：让模型写出Python脚本，用OpenCV/PIL做精确的像素操作
+3. **代码执行**：自动边缘检测、坐标计算、14等分、中心点采样、输出RGB
+
+核心代码逻辑大概这样：
+
+```
+1. 红色区域检测（HSV阈值分割）→ 定位左右两个红色块
+2. 计算两红色块中心/边缘的水平距离 D
+3. 将 D 14等分，得到14个采样中心点坐标
+4. 在每个中心点取ROI平均RGB（避免单像素噪声）
+5. 输出 [R,G,B] 列表
+```
+
+---
+
+**一句话总结**
+
+多模态大模型擅长"看懂这是什么"，但不擅长"精确测量像素"。你描述的任务本质是**计算机视觉测量问题**，不是视觉理解问题。当前最好的方案是让大模型当"视觉理解大脑"，让OpenCV代码当"精确执行的手"。
+
+蔡叔叔如果需要，我可以把这段OpenCV的完整代码写出来，直接在图上跑一遍，给你真实的14组RGB值。要吗？
+
+
+
+---
+
+# 那么，为什么类似豆包，Grok等，根据提示词，为什么可以生成看起来非常**精确**的图像呢？是不是可以说，也不一定不是"精确测量像素"？
+
+蔡叔叔这个反问很犀利，刚好击中了一个容易混淆的关键点。
+
+先说结论：**图像生成能做"精确像素"，图像理解反而做不到。这两个任务在底层是完全相反的方向。**
+
+---
+
+**根本区别：创造 vs 压缩**
+
+| | 图像生成（豆包/Grok作图） | 图像理解（读你的尿检试纸） |
+|---|---|---|
+| **方向** | 噪声/语义 → **像素** | 像素 → **语义token** |
+| **每个像素的来历** | 模型**亲手计算**出来的 | 模型**压缩丢弃**后无法恢复 |
+| **知不知道RGB值** | 知道，因为它在创造 | 不知道，因为它看到的是patch统计特征 |
+| **精确性来源** | 去噪过程中逐步锁定每个像素的值 | 只能从模糊特征反推，无法回查原始像素 |
+
+---
+
+**为什么生成模型能"精确"？**
+
+豆包、Grok、Midjourney 这些底层是 **Diffusion/DiT** 模型。生成一张图的过程是这样的：
+
+```
+随机噪声
+ ↓
+去噪步骤1：大致轮廓（32×32粗网格）
+ ↓
+去噪步骤2：加入细节（64×64）
+ ↓
+去噪步骤3：精细纹理（1024×1024）
+ ↓
+最终输出：每个像素的R/G/B值都是模型一步步算出来的
+```
+
+**每一个像素都是模型自己"画"的，所以它当然"知道"每个位置的值。** 就像画家知道自己画布上每个点涂了什么颜色。
+
+---
+
+**为什么理解模型反而"不精确"？**
+
+而多模态理解模型（如Kimi、GPT-4V）走的是另一条路：
+
+```
+原始图像 1920×1080（200万像素）
+ ↓
+ViT切成 14×14 的patches → 约2万个patch
+ ↓
+每个patch压成一个视觉token（比如768维向量）
+ ↓
+LLM只看到几百个token，原始像素信息已经丢了
+```
+
+这就好比你把一张高清照片压缩成一张模糊的缩略图，然后问缩略图"第137行第258列的像素是什么颜色"——它根本没保存这个信息。
+
+---
+
+**回到你的问题**
+
+你问："是不是也可以说，不一定不是精确测量像素？"
+
+对**生成任务**来说，是的——生成模型确实在精确操纵像素。
+
+但对**理解任务**（读你的尿检试纸），**不是**。现有的多模态大模型无论怎么升级，只要架构还是"图像编码器 + LLM"，就存在**信息瓶颈**：几百个视觉token承载不了几百万像素的信息。
+
+这也是为什么真正做工业视觉检测（比如读试纸、读仪表盘、零件尺寸测量）的方案，一定是 **VLM做识别和理解 + OpenCV/传统CV做像素级计算**，而不是让大模型直接报RGB值。
+
+---
+
+**一个更直观的比喻**
+
+- **生成模型** = 画家画画。他知道每一笔的颜色和位置，因为他自己在画。
+- **理解模型** = 评论家看画。他能告诉你"这是一幅星空"，但你要问他"左下角第三颗星星的RGB值"，他只能凭印象猜，无法拿起放大镜去量。
+
+蔡叔叔，如果往深了说，现在有一些前沿研究（比如**原生多模态模型** Gemini 2.5、或者 **VQ-VAE token化** 的路线）试图让理解和生成共用一套"视觉词汇表"，未来也许能缩小这个差距。但至少目前，读试纸这种活还是得靠代码。
+
+
+
+
diff --git a/MyBestNanobot/自动化测试，snap 版 Chromium 和 Playwright 的 Chromium 的差异.md b/MyBestNanobot/自动化测试，snap 版 Chromium 和 Playwright 的 Chromium 的差异.md
new file mode 100644
index 0000000..82aa139
--- /dev/null
+++ b/MyBestNanobot/自动化测试，snap 版 Chromium 和 Playwright 的 Chromium 的差异.md
@@ -0,0 +1,338 @@
+**核心差异：Snap 是一个容器/沙箱，Playwright 的是裸二进制。**
+
+---
+
+**1. 安装与运行方式**
+
+| | Snap Chromium | Playwright Chromium |
+|--|---------------|---------------------|
+| 位置 | `/snap/chromium/3416/...` | `~/.cache/ms-playwright/chromium-1208/...` |
+| 安装方式 | Ubuntu snap 包管理器 | Playwright `install` 脚本直接下载解压 |
+| 运行环境 | **Snap 沙箱容器**（受限的文件系统、权限、IPC） | 普通用户进程，无额外容器层 |
+| 更新 | 自动（snap 控制） | 手动（与 Playwright 版本绑定） |
+
+---
+
+**2. 为什么 agent-browser 在 Snap 版上卡住**
+
+Snap 的安全模型对自动化工具很不友好：
+
+- **文件系统隔离**：Snap Chromium 只能看到 `/home/ubuntu/snap/chromium/` 下的数据，对 `/tmp` 等路径有受限访问
+- **进程间通信限制**：agent-browser 通过 Chrome DevTools Protocol (CDP) 连接 Chromium，snap 的 AppArmor 配置可能阻塞或延迟这个连接
+- **无头模式问题**：snap 版在 `--headless=new` 模式下经常需要额外的 `--enable-unsafe-swiftshader` 等参数才能正常工作
+- **会话残留**：snap Chromium 崩溃或卡住时，agent-browser 的 daemon 还在等待 CDP 响应，导致后续命令全部超时
+
+**刚才的实际现象**：
+```
+[chrome] ← 僵尸进程
+agent-browser open ... → 超时 124
+```
+就是 snap Chromium 启动后 CDP 连接挂起，agent-browser 一直等不到响应。
+
+---
+
+**3. 为什么 Playwright 一直工作正常**
+
+Playwright 的 Chromium 是**专门为自动化设计的**：
+- 直接下载 `.zip` 解压，没有任何 snap/apparmor 沙箱层
+- Playwright 自己管理启动参数、user-data-dir、CDP 端口
+- 支持 `--no-sandbox` 无缝运行
+- 版本经过 Playwright 团队测试，确保 CDP 协议兼容性
+
+---
+
+**4. 对 agent-browser 的建议**
+
+让 agent-browser 用 Playwright 的 Chromium，而不是 snap 版：
+
+```bash
+# 方式一：每次命令指定路径
+agent-browser open https://example.com \
+ --executable-path ~/.cache/ms-playwright/chromium-1208/chrome-linux64/chrome \
+ --args "--no-sandbox"
+
+# 方式二：环境变量（推荐）
+export CHROME_PATH="$HOME/.cache/ms-playwright/chromium-1208/chrome-linux64/chrome"
+agent-browser open https://example.com --args "--no-sandbox"
+
+# 方式三：让 agent-browser 自己下载 Chrome for Testing
+agent-browser install
+# 这会下载到 ~/.cache/agent-browser/chrome/...
+```
+
+---
+
+**一句话总结**：Snap 是给普通用户用的浏览器，Playwright 的是给机器人用的浏览器。自动化场景下，永远优先用 Playwright 的 Chromium 或 Chrome for Testing。
+
+---
+
+
+
+基于文档，我的日常自动化测试工作流应该这样分层执行：
+
+---
+
+**一、环境准备（每次新会话只需确认一次）**
+
+```
+# 1. 检查 Playwright Chromium（优先用，避开 Snap）
+ls ~/.cache/ms-playwright/chromium-*/chrome-linux*/chrome
+
+# 2. 如需要 agent-browser，强制指定 Playwright 的 Chrome
+export CHROME_PATH="$HOME/.cache/ms-playwright/chromium-1208/chrome-linux64/chrome"
+
+# 3. 确认目标服务在运行
+curl -s http://129.211.173.87:PORT/health
+```
+
+**原则**：永远不用 Snap Chromium 做自动化，它是给用户手动浏览器的，不是给机器人的。
+
+---
+
+**二、三级测试策略**
+
+| 级别 | 场景 | 工具 | 耗时 |
+|------|------|------|------|
+| **L1 视觉验证** | "截图看看效果" | Playwright 截图 | 10-30 秒 |
+| **L2 功能验证** | "点一下按钮看是否正常" | Playwright 脚本 | 1-2 分钟 |
+| **L3 E2E 回归** | "跑一遍完整用户流程" | Playwright Test + 断言 | 3-5 分钟 |
+
+---
+
+**三、具体执行流程**
+
+**L1：快速截图验证**
+
+```python
+# /home/ubuntu/.nanobot/workspace/tests/quick_screenshot.py
+from playwright.sync_api import sync_playwright
+
+with sync_playwright() as p:
+ browser = p.chromium.launch(
+ executable_path="~/.cache/ms-playwright/chromium-1208/chrome-linux64/chrome",
+ args=["--no-sandbox"]
+ )
+ page = browser.new_page(viewport={"width": 1280, "height": 800})
+ page.goto("http://129.211.173.87:10002/")
+ page.screenshot(path="/tmp/test_result.png", full_page=True)
+ browser.close()
+```
+
+我执行这个脚本，截图返回给用户判断。
+
+**L2：交互功能验证**
+
+```python
+# 验证"特色展示"眼睛按钮点击后状态变化
+page.goto("http://129.211.173.87:10002/admin")
+page.fill('input[name="username"]', "admin")
+page.fill('input[name="password"]', "abcd1234")
+page.click('button[type="submit"]')
+page.wait_for_url("**/admin/dashboard")
+
+# 进入特色展示页面
+page.goto("http://129.211.173.87:10002/admin/features")
+page.click('tr:first-child .eye-icon') # 点击第一个眼睛
+
+# 验证 API 是否被调用（拦截网络）
+with page.expect_response("**/api/features/**") as resp:
+ page.click('button:has-text("保存更改")')
+assert resp.value.status == 200
+```
+
+**L3：完整 E2E 用例（Playwright Test）**
+
+```python
+# tests/test_admin_flow.spec.py
+def test_feature_toggle_auto_save(page):
+ page.goto("/admin/login")
+ page.fill('[data-testid="username"]', "admin")
+ page.fill('[data-testid="password"]', "abcd1234")
+ page.click('[data-testid="login-btn"]')
+ expect(page).to_have_url("/admin/dashboard")
+
+ page.goto("/admin/features")
+ # 点击眼睛，验证立即发送 PATCH 请求
+ with page.expect_request("**/api/features/*/toggle") as req:
+ page.click("tr:first-child .toggle-btn")
+ assert req.value.method == "PATCH"
+```
+
+---
+
+**四、元素定位策略（按优先级）**
+
+文档里强调了这个，我实际执行时：
+
+1. **首选 Accessibility 语义**（如果 agent-browser 可用）
+ - `role=button, name="保存更改"` — 组件重构后依然稳定
+
+2. **次选测试专用属性**（Playwright 推荐）
+ - `data-testid="login-btn"` — 前端配合添加，最稳定
+
+3. **再次选语义标签**
+ - `button:has-text("登录")` — 比 CSS 层级稳定
+
+4. **兜底 CSS 选择器**
+ - `.nav-bar > ul > li:nth-child(3)` — 最脆弱，只在上面都不可用时用
+
+**永远不用的定位方式**：截图像素匹配、OCR 识别坐标。那是 PyAutoGUI 的路线，不是浏览器自动化。
+
+---
+
+**五、和 nanobot 工作流的结合**
+
+当用户说：
+
+| 用户指令 | 我的执行 |
+|----------|----------|
+| "截图看一下首页" | L1：启动浏览器 → 访问 → 截图 → 返回图片 |
+| "测试一下登录功能" | L2：写临时脚本 → 执行 → 报告结果 |
+| "验证特色展示自动保存" | L2/L3：先定位元素 → 拦截网络 → 验证 API 调用 |
+| "跑一遍完整测试" | L3：检查 test 目录有无 spec 文件 → 执行 `pytest` 或 `playwright test` |
+
+脚本位置：放在 `/home/ubuntu/.nanobot/workspace/tests/` 下（不污染 ~/nanocode）。
+
+---
+
+**六、反检测注意事项**
+
+如果测试的是第三方网站（如 fumamx.com）：
+- 设置真实 User-Agent
+- 模拟真实鼠标轨迹（Playwright 默认已做）
+- 适当添加 `page.wait_for_timeout(random.randint(500, 1500))` 模拟人类停顿
+
+---
+
+**一句话总结我的测试能力**
+
+> 我可以用 **Playwright + CDP 协议** 控制真实 Chromium 浏览器，通过 **DOM/AX Tree 语义定位** 执行点击、填写、导航等操作，最后 **截图或断言** 验证结果。脚本放在 workspace，不污染项目代码。
+
+蔡叔叔，这个理解对吗？需要我针对某个具体页面（比如 website-new 的访客端或后台）先跑一次 L1 截图验证看看？
+
+
+---
+
+
+这是一个很好的架构问题。我的判断是：
+
+---
+
+**1. agent-browser 能被 Playwright + CDP 替代吗？**
+
+**底层控制：完全可以替代。** Playwright 本身就是基于 CDP 的，能力只强不弱。
+
+| 能力 | agent-browser | Playwright |
+|------|---------------|------------|
+| CDP 控制 | ✓ | ✓（原生） |
+| Accessibility Tree | `snapshot -i` | `page.accessibility.snapshot()` |
+| 语义定位 | `@eN` refs | `get_by_role()`, `get_by_label()`, `get_by_text()` |
+| 网络拦截 | ✓ | ✓（更成熟） |
+| 自动等待 | ✓ | ✓（业界最强） |
+| 截图对比 | ✓ | ✓ + 像素级 diff |
+| 录制视频 | WebM | ✓ |
+| 并行/多 context | session | browser context 隔离 |
+| **AI Chat** | ✓（内置） | ✗（需自行封装） |
+| **Skills 分发** | ✓（内置） | ✗（但 nanobot 有 skills 系统） |
+| **稳定性** | 一般（Snap 兼容问题） | **非常好** |
+
+agent-browser 真正的独特价值只有两点：
+- **令牌优化的 AX Tree 输出**（给 LLM 看很省 token）
+- **自然语言 Chat 接口**（`agent-browser chat "search google for..."`）
+
+这两点可以用 Playwright + 一个简单的包装层来实现，不需要依赖 agent-browser 本身。
+
+---
+
+**2. 应该做成 skill 吗？**
+
+**应该。** 理由：
+
+你刚才的文档里已经沉淀了一套**完整的测试方法论**：
+- 环境检查（避开 Snap Chromium）
+- 三级测试策略（L1 截图 / L2 功能 / L3 E2E）
+- 元素定位优先级（AX > data-testid > 语义文本 > CSS 兜底）
+- 反检测注意事项
+
+这些不是一次性的，而是**可复用的工作流知识**。做成 skill 后，任何项目都可以调用。
+
+---
+
+**3. 理想的 skill 设计**
+
+不叫 `agent-browser`，叫 `browser-automation` 或 `e2e-testing`：
+
+```
+skills/browser-automation/SKILL.md
+├── 环境检查（Playwright Chromium 路径、目标服务可达性）
+├── L1 快速截图（一行命令生成截图）
+├── L2 功能验证（拦截网络 + 断言）
+├── L3 E2E 回归（Playwright Test 规范）
+├── 元素定位策略（AX Tree > data-testid > text > CSS）
+├── 反检测配置（真实 UA、随机延迟）
+└── 常用脚本模板
+```
+
+这个 skill 底层用 **Playwright + CDP**，输出格式参考 agent-browser 的 Accessibility Tree（但自己做），不依赖 vercel-labs/agent-browser CLI。
+
+---
+
+**4. 结论**
+
+> **不需要依赖 agent-browser。** Playwright + CDP 能力完全覆盖，稳定性更好。
+>
+> **应该做成 skill。** 把文档里沉淀的方法论标准化，以后任何项目说"测试一下"，我自动按 L1/L2/L3 策略执行。
+
+要我现在就创建这个 `browser-automation` skill 吗？基于你刚才的文档内容。
+
+
+
+---
+
+
+
+蔡叔叔，汇报当前进展：
+
+---
+
+**1. browser-automation skill 已完成 ✅**
+
+创建了技能文件：`/home/ubuntu/.nanobot/workspace/skills/browser-automation/SKILL.md`
+
+包含脚本：
+- `test_screenshot.py` — L1 任意 URL 截图
+- `test_navigate.py` — L1 基础导航验证
+- `test_baidu_search.py` — L2 百度搜索（含 JS 绕过 visibility 技巧）
+- `test_generic_search.py` — L2 通用搜索框架
+
+**2. 百度测试验证通过 ✅**
+
+之前遇到的 headless 模式下百度搜索框 visibility 为 0 的问题，已用 `page.evaluate` JS 直接操作 DOM 绕过，成功搜索并截图。
+
+---
+
+**3. 博客后台登录遇到障碍 ❌**
+
+访问 `http://129.211.173.87:10002/admin/` 返回 **404 Not Found**。
+
+检查后发现：
+- 端口 10002 确实有一个 Vite dev server 在运行（PID 1857）
+- 但 `~/nanocode/website-new/` 目录**已不存在**（被删除或迁移了）
+- 所以后台服务没有实际部署
+
+---
+
+**需要你确认两点：**
+
+1. **website-new 项目现在在哪？** 正确后台地址是什么？（之前不是 `129.211.173.87:10002` 吗？）
+2. **还要继续测其他场景吗？** 比如：
+ - 用通用搜索框架测试 Google / DuckDuckGo
+ - 测试我们自己的其他项目（fumamxCRM 等）
+
+请指示下一步 👆
+
+
+
+---
+
+
diff --git a/MyBestOpenclaw/小红书试管采集项目和自动评论.md b/MyBestOpenclaw/小红书试管采集项目和自动评论.md
new file mode 100644
index 0000000..627c5ff
--- /dev/null
+++ b/MyBestOpenclaw/小红书试管采集项目和自动评论.md
@@ -0,0 +1,329 @@
+
+# 一 主贴数据采集
+
+你重置一下上下文。我们继续小红书的“试管婴儿”数据采集方案。
+我们现在在clawcode 建立一个数据采集子目录，
+根据你的方案，拆分关键字，随机等待时间（不少于5秒），
+根据你拆分的关键字，搜索尽可能多的帖子，并存储到sqlite数据库，要求做好去重。
+采集的数据有效总数不少于1500条。
+我们就使用你已经知道的小红书登录方式，进行采集。
+
+你想想怎么实现？有没有问题？
+这种耗时的任务，你是使用sub-agent，还是其他方式？
+
+
+---
+
+chrome://extensions/?id=ojfebgpkimhlhcblbalbfjblapadhbol
+https://github.com/Moustachauve/cookie-editor
+
+https://github.com/ravindu644/Edit-This-Cookie-V3
+https://github.com/YoeriW/Edit-This-CookieGitHub
+https://github.com/etiennedubois/cookie-editor
+https://github.com/Moustachauve/cookie-editor
+
+
+
+# 二 cookie获取客户端
+
+
+## 概述
+Windows运行环境下，实现一个基于Chrome 浏览器内核的浏览器客户端，用户正常操作浏览器，通过这个浏览器，登录某个网站（如小红书）。然后通过这个自定义的浏览器客户端，导出用户的cookies。
+目前github上有一些开源的代码（chrome扩展），用于导出chrome浏览器cookie
+
+1. https://github.com/Moustachauve/cookie-editor ，我已经clone代码到cookie-editor子目录
+2. https://github.com/YoeriW/Edit-This-Cookie ， 我已经clone代码到Edit-This-Cookie子目录
+
+## 详细需求
+
+1. 我需要有GUI界面，是否可以采用Python语言？Python应该是支持GUI编程的，对吧？
+2. GUI界面要求：
+ >顶部是地址栏，跟普通浏览器一样，回车则加载页面
+ >地址栏下面是浏览器主界面
+ >GUI的最底部这一行，是操作按钮（不是页面，是类似Python实现的原生按钮），目前只一个按钮"导出cookie"
+
+3. cookie-editor，Edit-This-Cookie是给你参考的，告诉你如何提取网站cookies。
+4. xiaohongshu_cookies.txt是一个小红书的cookies案例，是通过chrome浏览器扩展导出，供参考
+
+你理解上述需求，如果有疑问，请反问我，直到你充分理解。然后，你给出你的技术方案和开发计划。
+
+---
+
+我理解您的需求了。让我先查看一下现有的cookie扩展代码和cookie文件格式，以便给出合适的技术方案。
+我已经分析了您的需求和现有代码。让我确认一下我的理解：
+
+## 需求理解
+
+1. **目标**：创建一个Windows桌面应用程序，内置浏览器，可导出cookies
+2. **界面布局**：
+ - 顶部：地址栏（输入URL回车加载）
+ - 中间：浏览器主界面
+ - 底部：原生按钮栏（导出cookie按钮）
+3. **功能**：用户正常浏览网站、登录后，点击按钮导出cookies
+4. **Cookie格式**：参考`xiaohongshu_cookies.txt`的JSON数组格式
+
+---
+
+## 技术方案
+
+### 推荐方案：**PyQt5/PyQt6 + QWebEngineView**
+
+**为什么选择这个方案？**
+
+| 特性 | 说明 |
+|------|------|
+| **浏览器内核** | QWebEngineView 基于 Chromium 内核（与Chrome同源） |
+| **Cookie获取** | 通过 `QWebEngineCookieStore` API 直接访问cookie存储，无需扩展 |
+| **成熟度** | Qt是工业级GUI框架，稳定可靠 |
+| **跨平台** | Windows/macOS/Linux都支持 |
+
+### 替代方案对比
+
+| 方案 | 优点 | 缺点 |
+|------|------|------|
+| **PyQt + QWebEngine** ✅ | 完整浏览器功能，直接获取cookie | 需要安装PyQt5/6和WebEngine |
+| CEF Python | 更接近Chrome | 项目维护不活跃，文档少 |
+| Selenium + GUI | 自动化能力强 | 不是嵌入式浏览器，体验差 |
+| Electron | 现代化UI | 需要Node.js，非Python |
+
+---
+
+## 开发计划
+
+### 项目结构
+```
+cookie_browser/
+├── main.py # 主程序入口
+├── browser_window.py # 浏览器窗口类
+├── cookie_exporter.py # Cookie导出逻辑
+├── requirements.txt # 依赖文件
+└── exported_cookies/ # 导出的cookies存放目录
+```
+
+### 实现步骤
+
+1. **环境搭建**
+ - 安装 PyQt6 和 PyQt6-WebEngine
+ - 创建项目骨架
+
+2. **GUI界面开发**
+ - 创建主窗口（QMainWindow）
+ - 添加地址栏（QLineEdit + 回车事件）
+ - 嵌入浏览器组件（QWebEngineView）
+ - 底部按钮栏（导出Cookie按钮）
+
+3. **Cookie导出功能**
+ - 连接 QWebEngineCookieStore
+ - 获取当前域名所有cookies
+ - 转换为JSON格式（与示例文件格式一致）
+ - 保存到文件
+
+4. **测试验证**
+ - 测试访问小红书网站
+ - 验证登录后能否正确导出cookies
+
+---
+
+## 疑问确认
+
+在开始实现之前，我有几个问题需要确认：
+
+1. **导出位置**：Cookie文件导出到固定目录，还是每次弹出文件保存对话框让用户选择？
+Answer：导出到当前目录，名称: **当前域名** 加上 __cookies.txt
+2. **导出格式**：是否只需要JSON格式（与示例一致），还是需要支持其他格式（如Netscape格式、Header格式）？
+Answer：先只采用这种Json格式，后续需要则再修改。
+3. **Python版本**：您本地Python版本是多少？（建议Python 3.8+）
+Answer： 我电脑的Python版本是Python 3.12.11
+4. **界面语言**：界面需要中文还是英文？
+Answer： 中文
+请确认上述问题后，我将开始实现。
+
+---
+我的补充问题：
+1. 你自己安装**PyQt5/PyQt6 + QWebEngineView**吗?
+2. 这个程序做完后，可以生成一个可执行文件吗？不一定要做成安装包。但是 如果我将目录复制到其他电脑就可以运行，能做到吗？是否一定要去目标电脑 安装了Python，及**PyQt5/PyQt6 + QWebEngineView**
+
+
+
+
+
+
+# 三 自动评论
+
+
+讨论一下怎么正对小红书某个主贴，进行回帖及回帖的再次回复的问题。
+我的身份是：试管婴儿服务提供商，可以给用户提供的服务包括：
+提供医院介绍和对接；
+试管婴儿全周期跟进；
+调理生殖健康的营养品保健品；
+
+有需要的点我头像，私信进一步沟通。
+
+---
+
+## 小红书自动评论功能开发
+我们按照上面的步骤，已经提前了有效的小红书cookies，下一步我们根据这个小红书xiaohongshu-cli实现的相同方式，针对小红书的主贴下面的回帖，进行回复。
+
+### 项目下的ivf_notes.db文件，是sqlite数据库，对应的数据库表定义如下：
+CREATE TABLE notes (
+note_id TEXT PRIMARY KEY, _-- 小红书笔记ID_
+title TEXT,
+content TEXT, _-- 预览内容_
+author_id TEXT,
+author_name TEXT,
+like_count INTEGER,
+comment_count INTEGER, _-- 用于评估互动价值_
+keyword TEXT, _-- 来源关键词_
+search_page INTEGER,
+collected_at TIMESTAMP,
+status TEXT DEFAULT 'new' _-- new/commented/ignored_
+);
+CREATE INDEX idx_keyword ON notes(keyword);
+CREATE INDEX idx_author ON notes(author_id);
+CREATE INDEX idx_status ON notes(status);
+** ivf_notes.db数据库，存储了2000多条小红书有效的主贴，我们取第一条数据，小红书笔记ID（如：68f3431f000000000703a73e ），找到第一个热点跟帖，进行回复。比如回复一个 “你好” 之类的文字 **
+### 这个github 仓库 https://github.com/jackwener/xiaohongshu-cli 我已经克隆到xiaohongshu-cli目录
+
+### 请读取ivf_notes.db内容，并分析xiaohongshu-cli如何评论小红书。然后提出你的实现方案，如果能够重用xiaohongshu-cli的代码，就重用。这个库应该也是使用python实现的。
+
+如果有问题，请提问。没有问题，请给方案。然后再决定实施测试程序。
+
+
+---
+
+让我先查看数据库内容和xiaohongshu-cli的代码结构：
+现在让我查找获取评论的方法：
+现在我已经了解了代码结构。让我也查看一下cookies的处理方式：
+现在我已经充分了解了代码结构。让我提出实现方案：
+
+---
+
+## 小红书自动评论功能 - 实现方案
+
+### 需求分析
+
+1. **数据来源**: `ivf_notes.db` 数据库中的 `notes` 表，包含2000+条小红书笔记
+2. **任务**: 取第一条笔记，找到第一个热点跟帖，进行回复（如"你好"）
+3. **技术基础**: 复用 `xiaohongshu-cli` 的 `XhsClient` 类
+
+### 核心API（已存在于xiaohongshu-cli）
+
+| 方法 | 功能 | 所在文件 |
+|------|------|----------|
+| `get_comments()` | 获取笔记评论列表 | client_mixins.py |
+| `get_all_comments()` | 获取所有评论（分页） | client_mixins.py |
+| `reply_comment()` | 回复指定评论 | client_mixins.py |
+| `post_comment()` | 发表评论 | client_mixins.py |
+
+### 实现方案
+
+**方案：创建独立脚本 `auto_comment.py`**
+
+复用 `xiaohongshu-cli` 的客户端，实现：
+1. 读取SQLite数据库获取笔记ID
+2. 使用 `XhsClient` 获取该笔记的评论列表
+3. 找到第一个评论（热点跟帖）
+4. 使用 `reply_comment()` 回复该评论
+
+### 问题确认
+
+1. **Cookie来源**: 使用之前导出的 `xiaohongshu_cookies.txt` 还是 `cookie_browser` 导出的新cookie？
+Answer: 使用cookie_browser新导出的cookies。cookies还可能失效，或者平台判断为失效等！ 最好，还需要判断cookies的有效性。（`xiaohongshu-cli`可能包含了怎么判断cookies有效）
+2. **回复内容**: 固定回复"你好"，还是需要支持自定义？
+Answer： 我们目前还是测试，只回复“你好”即可。你将评论ID 返回，或者打印出来。后续，我们还有新的开发任务！！！我一步步告诉你。
+3. **热点判断**: "第一个热点跟帖"是指：
+ - 第一条评论（时间最早）？
+ - 点赞数最多的评论？
+ - 还是只要第一条评论即可？
+Answer： 就用评论最多的吧，只是测试有效性。
+
+请确认以上问题，我将立即实施代码。
+
+---
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+帖子分析
+
+主贴内容：科普试管一/二/三代区别（医生认证账号）
+评论区现状：
+
+- 高赞评论：问三代筛选标准、问单身能否做试管
+
+
+- 讨论热点：单身试管合法性、胚胎质量筛选
+
+
+💡 关于"怎么评判"
+
+评论多的好处是：你能看到大家的关注点在哪，从而决定：
+
+1. 补充型评论 → 主贴没讲到的，你有经验可以补充
+
+
+2. 互动型评论 → 回复高赞评论，参与热门讨论
+
+
+3. 提问型评论 → 提出你的真实疑问，获得医生回复
+
+
+方案1：多关键词分散搜索（推荐）
+
+将"试管婴儿"拆分为细分关键词，分别搜索后去重：
+
+核心词：
+
+- 试管婴儿
+
+- 试管之路
+
+- 三代试管
+
+- 试管经历
+
+- 试管日记
+
+- 试管成功率
+
+- 试管费用
+
+- 试管医院
+
+人群词：
+
+- 备孕困难
+
+- 输卵管堵塞
+
+- 多囊试管
+
+- 卵巢早衰
+
+- 高龄试管
+
+- 试管双胞胎
+
+情感词：
+
+- 试管焦虑
+
+- 试管失败
+
+- 试管成功
+
+- 试管加油
+-
\ No newline at end of file
diff --git a/MyWorks/GEO要点解析.md b/MyWorks/GEO要点解析.md
index aa20bbd..533fcd4 100644
--- a/MyWorks/GEO要点解析.md
+++ b/MyWorks/GEO要点解析.md
@@ -225,4 +225,58 @@ GEO是针对**生成式AI引擎**（如ChatGPT、Google AI Overviews、Perplexit
GEO的核心是**“让AI成为你的流量入口”**，需同步做好**技术基建、内容质量、合规安全、数据监测**四大板块，形成闭环。建议先从基础配置（LLMs.txt、Schema）和核心内容重构入手，快速验证效果，再逐步深化差异化与本地化优化。
-需要我把以上要点整理成一份可直接复制的**30天GEO落地执行清单**（含每日任务、Schema模板、llms.txt示例）吗？
\ No newline at end of file
+需要我把以上要点整理成一份可直接复制的**30天GEO落地执行清单**（含每日任务、Schema模板、llms.txt示例）吗？
+
+
+
+---
+
+**网站 GEO 优化主要指 Generative Engine Optimization（生成式引擎优化），即针对 ChatGPT、Perplexity、Gemini、Google AI Overviews、DeepSeek 等 AI 生成式搜索引擎的内容优化策略。** 其核心目标是让网站内容更容易被 AI 理解、信任并直接引用，从而提升品牌在 AI 答案中的曝光率和可见度（而非传统搜索排名）。
+
+与传统 SEO（侧重关键词排名和链接）不同，GEO 更注重内容被 AI “阅读”和“引用”的能力。以下是从多个关键方面进行优化的实用指南：
+
+### 1. **内容质量与权威性（E-E-A-T）**
+ - 强调 **Experience（经验）、Expertise（专业性）、Authoritativeness（权威性）、Trustworthiness（可信度）**。
+ - 提供原创、深入、数据支持的内容，包含真实案例、统计数据、专家引用和可靠来源链接。
+ - 明确标注作者信息、更新日期（保持内容新鲜度），并展示公司/作者资质。
+ - **技巧**：用事实、数据和引用增强可信度，让 AI 更愿意采纳。
+
+### 2. **内容结构与可读性（AI-Friendly Structure）**
+ - **清晰的层级结构**：使用 H2/H3 作为问题式标题（匹配用户自然提问），添加摘要/Quick Answer（顶部 40-80 字直接回答核心问题）。
+ - **列表、表格、步骤**：便于 AI 提取和总结。
+ - **对话式写作**：采用自然、直接、问答式的语言，像在回答真实用户问题。
+ - **实体明确**：明确指出人名、品牌、概念，避免模糊表达，并通过内部/外部链接关联相关实体。
+
+### 3. **技术与结构化数据优化**
+ - **Schema Markup**：添加 LocalBusiness、FAQ、Article 等结构化数据，帮助 AI 更好解析实体和关系。
+ - **页面元数据**：优化 title 和 description，让 AI 快速理解页面主题。
+ - **移动友好 + 加载速度**：AI 和用户都偏好体验好的页面。
+ - **llms.txt**（如果适用）：为 AI 提供文档结构索引。
+ - 确保内容易抓取，避免重复或低质量页面。
+
+### 4. **用户意图与查询匹配**
+ - 从 **Prompts（提示词）** 而非单纯关键词出发，研究 AI 常见提问方式（How to、What is、Comparison 等）。
+ - 创建 FAQ、对比表、清单、案例研究等高意图内容。
+ - 覆盖长尾、具体问题，增加信息密度（提供全面上下文和例子）。
+
+### 5. **权威信号与外部强化**
+ - **PR 与外部提及**：增加品牌在权威媒体、Wikipedia 等地方的曝光，提升整体可信度。
+ - **一致性**：NAP（名称、地址、电话）等实体信息在全网保持一致（尤其本地业务）。
+ - **社交与多渠道**：通过社交证明权威性，制造热点让 AI 更容易捕捉。
+
+### 6. **监测、迭代与程序化策略**
+ - 跟踪 AI 引用情况（手动测试 ChatGPT/Perplexity 等，或用专用工具）。
+ - 定期更新高价值页面，保持内容时效性。
+ - 考虑程序化 GEO：批量生成针对性强的优化页面（但需确保质量，避免垃圾内容）。
+ - 结合传统 SEO：GEO 是 SEO 的延伸，两者协同效果更好。
+
+### 实施优先级建议
+1. 先从核心页面入手（首页、产品/服务页、博客高流量文章）。
+2. 优化现有内容 → 创建新针对性内容。
+3. 测试：在不同 AI 工具中搜索相关查询，观察自己的内容是否被引用。
+4. 持续性：AI 模型会不断更新，优化是长期过程。
+
+**注意**：GEO 还在快速发展中，核心仍是“写给人类 + 方便 AI 提取”的高质量内容。过度优化或生成低质内容可能适得其反。
+
+如果你的“GEO”指的是 **地理位置优化（Local SEO / Geotargeting）**，请补充说明，我可以提供另一套针对本地搜索、Google 我的商家、NAP 一致性等的优化策略。需要具体案例或某个方面的深入细节吗？
+
diff --git a/MyWorks/体系材料编写.md b/MyWorks/体系材料编写.md
new file mode 100644
index 0000000..311f35e
--- /dev/null
+++ b/MyWorks/体系材料编写.md
@@ -0,0 +1,37 @@
+
+# 背景描述
+
+深圳市飞点健康管理有限公司的尿常规14项的尿检App产品名称是**尿大夫**，这个产品涵盖android 版本和 ios版本。
+这个app属于二类医疗器械独立软件。
+
+# 你的工作目标
+帮我编写《工作环境控制程序》，涵盖的内容包括：研发/测试/生产设备的软件和硬件环境控制程序。
+
+**请严格依照我国国家标准，行业标准对二类医疗器械独立软件的研发/测试/生产过程的要求，帮我编写软件和硬件环境控制程序。**
+## app开发环境
+android版本的app的开发环境：android studio开发工具，基于windows操作系统开发，使用PC机。
+ios版本的app开发环境是：XCode开发工具，基于MAC OS操作系统。
+
+
+## 测试环境
+
+尿大夫android app 基于 国产android手机测试；
+尿大夫ios app基于 iphone手机测试；
+测试会使用专用的android和iphone手机。
+
+## 生产环境
+尿大夫app属于二类医疗器械，是独立软件。其生产环境不牵涉到任何物料的生产。软件开发和测试完成后（验证和确认），通过专用的一台生产电脑，上传到各个app的应用市场。
+ios版本的尿大夫app会上传并发布到apple store。
+android版本的尿大夫app会上传并发布到应用宝，小米，vivo，oppo等应用市场。
+
+## 云服务器
+基于腾讯云的ubuntu云服务器。尿检用户信息会存储在云服务器上。
+app用户登录使用手机号码和短信验证码或者密码登录云服务器。
+所有的网络传输采用https方式。
+云服务器及运行于云服务器上的程序，不属于我公司医疗器械软件的组成部分。
+
+
+# 其他
+
+你仔细分析我的需求，如果有问题，请问我。你的文档，要求真实符合实际法规要求，不能瞎编。
+
diff --git a/MyWorks/公司官网.md b/MyWorks/公司官网.md
new file mode 100644
index 0000000..7e77ee8
--- /dev/null
+++ b/MyWorks/公司官网.md
@@ -0,0 +1,2080 @@
+运行的本地官网项目。使用类似fumamxCRM 项目的技术栈，重写官网。
+
+https://niaodaifu.cn/
+
+你再阅读一下目录下的文件： 企业官网 CMS 系统需求规格说明书.md 这个文件是别的AI写的一个官网需求，肯定有不合理的地方，你也了解一下，作为我们的技术文档的参考。你了解了现有的代码，及这个基本的需求说明书之后，可以在项目目录下创建一个docs目录，编写你的设计文档，实现步骤和计划。
+
+**我们先把前后端的架构先建立起来，一步步实施每一个模块。做完一个模块，测试一个模块，保证每一步的实现都能使用。**
+
+**我们的第一步是实现首页，及对应的首页内容的后台管理功能**
+
+**我让你开始写代码，你才开始。**
+
+### 以下是针对你的问题的答复：
+
+1. **后端数据**：是否需要将外部 API 数据迁移到本地 SQLite 数据库？还是继续调用外部 API？
+Answer： 这个最后再做。我们一步步来。
+
+2. **页面范围**：是否需要重写全部 22 个页面？还是优先核心页面（首页 + 产品介绍）？
+Answer： 我们一步步实现。第一步，做首页。
+
+3. **设计**：是否需要重新设计 UI，还是保持现有视觉风格？
+Answer：可以使用新粗野风格。但是会使用现在的文章内容，和图片。
+
+4. **静态资源**：图片资源（约 400+ 张）是继续使用现有路径，还是迁移到新项目？
+Answer：现有的图片，需要用到的才导入。
+
+---
+
+设计文档已全部创建完成。让我给你总结一下：
+
+---
+
+## 📁 创建的设计文档
+
+| 文档 | 说明 |
+|------|------|
+| `docs/ARCHITECTURE.md` | 系统架构设计、技术栈、目录结构、部署方案 |
+| `docs/DATABASE.md` | 数据库表设计（admins/home_modules/nav_menus/settings） |
+| `docs/API.md` | RESTful API 接口规范（认证/首页模块/导航/设置） |
+| `docs/FRONTEND.md` | 前端组件设计、新粗野主义样式规范 |
+| `docs/ROADMAP.md` | 分阶段实现路线图 |
+
+---
+
+## 🗺️ 实现路线图概览
+
+```
+第一阶段：基础架构搭建
+├── 后端：FastAPI + SQLite 基础配置
+└── 前端：React + Vite + TailwindCSS 项目初始化
+
+第二阶段：数据模型与认证
+├── 数据库模型创建
+├── JWT 认证接口
+└── 后台登录页面
+
+第三阶段：首页模块管理 ⭐ 核心
+├── 首页模块 CRUD API
+├── 后台模块管理（列表/排序/编辑）
+├── 前台模块组件（轮播图/优势/新闻等）
+└── 首页页面组装
+
+第四阶段：导航与布局
+├── 导航菜单管理
+├── Navbar 组件
+└── Footer 组件
+
+第五阶段：系统设置
+├── 设置管理
+└── 文件上传
+```
+
+---
+
+## 🎨 关键设计决策
+
+1. **模块化首页**：每个区块是一个独立的 `home_module`，支持拖拽排序、启用/禁用、自定义样式
+
+2. **新粗野主义风格**：
+ - 背景：`#FEF9E7` 奶油色
+ - 边框：2px 纯黑硬边框
+ - 阴影：4px 偏移硬阴影
+ - 交互：点击位移 + 阴影消失
+
+3. **数据复用**：保留原有网站的文字内容和图片，按需导入
+
+---
+
+**设计文档已准备就绪。请确认后，我将开始第一阶段：基础架构搭建。**
+
+---
+
+## 老项目分析总结
+
+### 当前技术栈
+
+| 层级 | 技术 |
+|------|------|
+| **前端** | Vue 3 + Vite 5 + Element Plus + Vant + Swiper |
+| **后端** | 外部 API (`https://niao.niaodaifu.cn`) |
+| **部署** | 纯静态前端 |
+
+### 网站内容结构
+
+**品牌**：飞点健康（尿大夫、吾大夫、吴试纸）- 手机尿检健康服务平台
+
+**主要页面**（22个路由）：
+| 页面 | 说明 |
+|------|------|
+| `/` | 首页 - Banner轮播 + 新闻动态 |
+| `/product/wudaifu` | 吾大夫产品介绍 |
+| `/product/wushizhi` | 吴试纸产品介绍 |
+| `/product/niaojianyun` | 尿检云产品介绍 |
+| `/product/project-fourteen` | 尿14试纸 |
+| `/health-information` | 健康资讯列表 |
+| `/news-information` | 新闻动态列表 |
+| `/about-us` | 关于我们 |
+| `/join-us` | 加入我们 |
+| `/business-cooperation` | 商务合作 |
+| `/mobile` | 手机官网 |
+| 其他 | 隐私政策、用户协议、下载页面等 |
+
+**API 接口**（调用外部服务）：
+- `GET /yaoqing/site/news/menu` - 栏目列表
+- `GET /yaoqing/site/news/article-list` - 资讯列表
+- `GET /yaoqing/site/news/article-detail` - 资讯详情
+
+---
+
+## 重写方案
+
+使用 **fumamxCRM 技术栈**：
+
+| 层级 | 新方案 | 端口 |
+|------|--------|------|
+| **前端** | React 19 + Vite 8 + TailwindCSS 4 | 10002 |
+| **后端** | FastAPI + Python 3.12 + SQLite | 10003 |
+
+---
+
+### 确认几个问题：
+
+1. **后端数据**：是否需要将外部 API 数据迁移到本地 SQLite 数据库？还是继续调用外部 API？
+
+2. **页面范围**：是否需要重写全部 22 个页面？还是优先核心页面（首页 + 产品介绍）？
+
+3. **设计**：是否需要重新设计 UI，还是保持现有视觉风格？
+
+4. **静态资源**：图片资源（约 400+ 张）是继续使用现有路径，还是迁移到新项目？
+
+---
+
+
+
+
+
+## 概述
+
+
+实现一个有前端呈现的公司官网，后端有管理后台的内容管理CMS系统。
+总体上，要求所有的页面都能动态修改。
+
+
+## 信息架构
+无论是呈现给外部用户的前台的界面和页面，还是后端管理界面，呈现和修改添加的内容，都是基于这样的信息架构，及模块来展开。
+
+**后台管理界面，需要对每一个模块，有对应的管理界面，对其内容进行增删改查。**
+
+### 首页
+Index页面由一系列可重用的子页面/子模块（栏目）拼装组成：
+> 每一个子页面，都要求单独编辑管理其内容
+> Index页面怎么组织这些子页面？
+> 子页面在Index页面出现的先后顺序？
+> 子页面的样式？
+#### 顶部导航
+公司Logo：这个写死代码
+导航菜单：菜单可以动态调整，即增加，删除，修改和隐藏菜单。只支持一级菜单即可。每个菜单链接到某个页面或者模块链接。
+#### 轮播图
+
+#### 产品优势
+#### 新闻动态
+#### 合作伙伴
+#### 友情链接
+#### 下载链接
+#### 联系方式
+#### 常见问题
+#### 产品介绍
+#### Footer
+
+
+### 产品介绍
+
+### 新闻资讯
+
+### 行业博客
+
+### 关于我们
+
+### 联系我们
+
+### 加入我们
+
+### 商务合作
+
+
+# 企业官网 CMS 系统需求规格说明书
+
+# 企业官网CMS系统需求规格说明书
+
+## 一、项目概述
+
+本项目为**企业官网内容管理系统（CMS）**，分为**前台用户端**（面向访客浏览官网）、**后台管理端**（面向管理员维护内容）、**后端API服务**（数据交互核心）三部分。
+
+系统核心能力：**可视化拼装首页、动态配置导航、全内容可后台编辑、无需代码修改即可更新官网**，满足企业日常官网内容维护、产品展示、新闻发布、客户沟通等核心需求。
+
+技术栈严格遵循要求：
+
+- 前端：React 19.2.4 + Vite 8.0.4 + TailwindCSS 4.2.2 + React Router 7.14.1
+
+- 后端：Python 3.12 + FastAPI + SQLite + SQLAlchemy + JWT + Pydantic
+
+- 部署：前端 serve 静态部署、后端 Uvicorn 运行
+
+
+---
+
+## 二、总体功能架构
+
+```Plain
+企业官网CMS
+├─ 前台用户端（访客访问）
+│ ├─ 首页（模块化拼装）
+│ ├─ 产品介绍
+│ ├─ 新闻资讯
+│ ├─ 行业博客
+│ ├─ 关于我们
+│ ├─ 联系我们
+│ ├─ 加入我们
+│ └─ 商务合作
+├─ 后台管理端（管理员操作）
+│ ├─ 管理员认证
+│ ├─ 首页模块管理（排序/样式/内容）
+│ ├─ 导航菜单管理
+│ ├─ 内容管理（产品/新闻/博客/单页）
+│ ├─ 基础信息管理（轮播/合作伙伴/链接等）
+│ └─ 系统设置
+└─ 后端API服务
+ ├─ 认证API
+ ├─ 首页模块API
+ ├─ 导航菜单API
+ ├─ 内容数据API
+ └─ 公共资源API
+```
+
+---
+
+## 三、前台用户端详细需求
+
+### 3.1 通用要求
+
+1. **响应式设计**：适配PC、平板、手机端，基于TailwindCSS实现
+
+2. **统一布局**：所有页面包含**顶部导航栏**、**底部Footer**
+
+3. **页面加载**：路由跳转无刷新，静态资源优化加载
+
+4. **SEO友好**：页面标题、描述可后台配置
+
+
+---
+
+### 3.2 首页（核心模块化页面）
+
+首页由**可独立编辑、可排序、可配置样式**的子模块拼装而成，管理员可在后台自由调整模块顺序、显示/隐藏、样式、内容。
+
+#### 3.2.1 固定模块（不可删除，可编辑/隐藏）
+
+1. **顶部导航**
+
+ 1. 公司Logo：代码固定，后台可上传图片替换
+
+ 2. 导航菜单：**一级动态菜单**，支持新增/编辑/删除/隐藏，点击跳转对应页面/模块
+
+ 3. 移动端：导航自动折叠为汉堡菜单
+
+2. **Footer（页脚）**
+
+ 1. 包含版权信息、公司地址、电话、邮箱、备案号
+
+ 2. 所有文字内容可后台编辑
+
+
+#### 3.2.2 可拼装子模块（全部支持独立编辑、排序、样式配置）
+
+|模块名称|功能要求|
+|---|---|
+|轮播图|支持多张图片轮播，后台可上传图片、配置跳转链接、轮播速度、是否显示|
+|产品优势|标题+图标+描述，支持多条数据编辑，可配置展示样式（网格/横向）|
+|新闻动态|展示最新N条新闻，带缩略图、标题、发布时间、查看详情按钮|
+|合作伙伴|企业logo墙，后台可上传logo、配置名称、链接|
+|友情链接|文字链接列表，可编辑名称、URL、排序|
+|下载链接|文件下载列表，支持上传PDF/Word等文件，显示文件名、大小|
+|联系方式|展示电话、邮箱、地址、地图定位，全部可后台编辑|
+|常见问题（FAQ）|问答列表，支持展开/收起，后台可增删改查|
+|产品介绍|展示精选产品，带图片、名称、简介、跳转详情按钮|
+
+#### 3.2.3 首页核心规则
+
+1. 子模块**顺序可自由拖拽调整**
+
+2. 子模块可**单独设置显示/隐藏**
+
+3. 子模块可**单独配置样式**：背景色、边距、文字颜色、布局方式
+
+4. 所有子模块内容**无需修改代码，纯后台配置生效**
+
+
+---
+
+### 3.3 独立页面需求
+
+1. **产品介绍页**
+
+ 1. 列表页：展示所有产品（图片+名称+简介），支持分页
+
+ 2. 详情页：产品大图、详细参数、详情描述，后台富文本编辑
+
+2. **新闻资讯页**
+
+ 1. 列表页：按时间倒序展示新闻，支持分页
+
+ 2. 详情页：标题、发布时间、作者、富文本内容、封面图
+
+3. **行业博客页**
+
+ 1. 同新闻结构，区分新闻与博客分类，独立管理
+
+4. **关于我们 / 联系我们 / 加入我们 / 商务合作**
+
+ 1. 均为**单页富文本页面**，后台可直接编辑全部内容
+
+ 2. 联系我们额外支持表单提交（姓名、电话、留言，提交后后台可查看）
+
+
+---
+
+## 四、后台管理端详细需求
+
+### 4.1 管理员认证
+
+1. **登录功能**：账号+密码登录，JWT令牌认证
+
+2. **权限控制**：仅管理员可访问后台，token过期自动跳转登录
+
+3. **密码修改**：支持管理员修改登录密码
+
+
+---
+
+### 4.2 核心功能模块
+
+#### 4.2.1 首页模块管理（核心）
+
+1. 展示所有首页子模块列表
+
+2. 支持**拖拽排序**修改模块在首页的展示顺序
+
+3. 支持模块**启用/禁用**（隐藏不显示）
+
+4. 支持**编辑模块内容**：文字、图片、列表数据
+
+5. 支持**配置模块样式**：背景色、内边距、布局、显示数量
+
+6. 实时预览修改效果
+
+
+#### 4.2.2 导航菜单管理
+
+1. 展示一级菜单列表
+
+2. 支持：新增、编辑、删除、隐藏菜单
+
+3. 菜单配置项：菜单名称、跳转目标（页面/外部链接）、排序
+
+4. 修改后前台导航实时生效
+
+
+#### 4.2.3 内容管理
+
+1. **产品管理**
+
+ 1. 增删改查产品
+
+ 2. 字段：产品名称、封面图、详情图、简介、详情富文本、排序、是否显示
+
+2. **新闻管理**
+
+ 1. 增删改查新闻
+
+ 2. 字段：标题、封面图、作者、发布时间、富文本内容、是否置顶
+
+3. **博客管理**
+
+ 1. 同新闻管理，独立数据分类
+
+4. **单页管理**
+
+ 1. 编辑关于我们、联系我们、加入我们、商务合作
+
+ 2. 富文本编辑器，支持图片上传、文字排版
+
+
+#### 4.2.4 公共资源管理
+
+1. 轮播图管理：增删改查、排序、上传图片、配置链接
+
+2. 合作伙伴管理：上传logo、填写名称、链接
+
+3. 友情链接管理：增删改查
+
+4. 下载链接管理：上传文件、填写名称、排序
+
+5. FAQ管理：增删改查问题与答案
+
+
+#### 4.2.5 留言管理（联系我们表单）
+
+1. 查看用户提交的留言列表
+
+2. 展示：姓名、电话、留言内容、提交时间
+
+3. 支持标记已读/删除
+
+
+#### 4.2.6 系统设置
+
+1. 公司基础信息：名称、logo、地址、电话、邮箱、版权信息
+
+2. 备案号、SEO标题、首页描述配置
+
+
+---
+
+## 五、后端API服务需求
+
+### 5.1 通用规范
+
+1. 接口风格：RESTful API
+
+2. 数据格式：JSON
+
+3. 认证方式：请求头携带 `Authorization: Bearer {token}`
+
+4. 异常处理：统一错误码、错误信息返回
+
+5. 文档：自动生成 Swagger UI 接口文档
+
+
+### 5.2 核心API分类
+
+#### 5.2.1 认证接口
+
+- `POST /api/login` 管理员登录
+
+- `POST /api/change-password` 修改密码
+
+- `GET /api/admin/info` 获取管理员信息
+
+
+#### 5.2.2 首页模块接口
+
+- `GET /api/home/modules` 获取前台首页所有启用模块（排序后）
+
+- `GET /api/admin/home/modules` 获取后台所有模块列表
+
+- `PUT /api/admin/home/modules/sort` 修改模块排序
+
+- `PUT /api/admin/home/modules/:id` 编辑模块内容/样式/状态
+
+- `GET /api/admin/home/modules/:id` 获取单模块详情
+
+
+#### 5.2.3 导航菜单接口
+
+- `GET /api/nav` 获取前台启用菜单
+
+- `GET /api/admin/nav` 后台菜单列表
+
+- `POST /api/admin/nav` 新增菜单
+
+- `PUT /api/admin/nav/:id` 编辑菜单
+
+- `DELETE /api/admin/nav/:id` 删除菜单
+
+
+#### 5.2.4 内容管理接口
+
+统一标准CRUD接口：
+
+- 产品：`/api/products` `/api/admin/products`
+
+- 新闻：`/api/news` `/api/admin/news`
+
+- 博客：`/api/blogs` `/api/admin/blogs`
+
+- 单页：`/api/pages` `/api/admin/pages`
+
+
+#### 5.2.5 公共资源接口
+
+- 轮播图 `/api/banners`
+
+- 合作伙伴 `/api/partners`
+
+- 友情链接 `/api/links`
+
+- 下载资源 `/api/downloads`
+
+- FAQ `/api/faq`
+
+
+#### 5.2.6 留言与上传接口
+
+- `POST /api/message` 提交留言
+
+- `GET /api/admin/messages` 查看留言
+
+- `POST /api/upload` 图片/文件上传（本地存储）
+
+
+### 5.3 数据库设计（核心表）
+
+1. `admins` 管理员表（id、用户名、密码哈希）
+
+2. `home_modules` 首页模块表（名称、标识、排序、启用状态、样式配置、内容数据）
+
+3. `nav_menus` 导航菜单表（名称、链接、排序、启用状态）
+
+4. `products` 产品表
+
+5. `news` 新闻表
+
+6. `blogs` 博客表
+
+7. `pages` 单页表（关于我们等）
+
+8. `banners/partners/links/downloads/faq` 公共表
+
+9. `messages` 留言表
+
+10. `settings` 系统设置表
+
+
+---
+
+## 六、非功能需求
+
+### 6.1 性能
+
+- 接口响应时间 < 300ms
+
+- 首页加载时间 < 2s
+
+- 支持图片压缩、懒加载
+
+
+### 6.2 安全性
+
+- 密码加密存储（bcrypt）
+
+- JWT令牌时效控制
+
+- 接口权限校验
+
+- 文件上传类型/大小限制
+
+
+### 6.3 可维护性
+
+- 代码结构清晰，注释完整
+
+- 后端模块化拆分（路由、模型、服务、工具）
+
+- 前端组件化复用
+
+
+### 6.4 兼容性
+
+- 前端兼容主流浏览器（Chrome、Edge、Firefox、Safari）
+
+- 支持移动端全尺寸适配
+
+
+---
+
+## 七、交付物清单
+
+1. 前端前台代码（React）
+
+2. 前端后台管理代码（React）
+
+3. 后端API服务代码（FastAPI）
+
+4. SQLite数据库初始化脚本
+
+5. 项目启动说明文档
+
+6. 接口说明文档（Swagger）
+
+7. 管理员使用手册
+
+
+---
+
+## 八、开发与运行要求
+
+### 8.1 前端运行
+
+```Bash
+# 安装依赖
+npm install
+# 开发模式
+npm run dev
+# 生产构建
+npm run build
+# 生产部署
+serve -s dist
+```
+
+### 8.2 后端运行
+
+```Bash
+# 安装依赖
+pip install -r requirements.txt
+# 开发模式
+uvicorn main:app --reload
+# 生产运行
+uvicorn main:app --host 0.0.0.0 --port 8000
+```
+
+---
+
+### 总结
+
+这份需求说明书完整覆盖**企业官网CMS**的全场景需求：
+
+1. **前台**：模块化拼装首页 + 7个标准企业页面，响应式适配全设备
+
+2. **后台**：管理员认证 + 首页模块可视化配置 + 全内容编辑 + 资源管理
+
+3. **后端**：标准化RESTful API + 完整数据库设计 + 安全认证 + 文件上传
+
+4. 严格匹配你指定的技术栈，开箱可用，无需代码修改即可完成官网日常维护
+
+---
+
+
+
+
+https://niaodaifu.cn/是正式环境部署的网站，对应的代码就是 /home/ubuntu/nanocode/website
+可以看到首页的大致内容，从上到下：
+- header导航区（logo + 菜单）
+- 轮播图
+- 产品优势
+-
+- 新闻动态
+- 合作伙伴
+- 友情链接
+- footer
+
+我们先从后台轮播图子模块开始设计，你可以看看现有官网轮播图的效果，我们可以实现类似效果。轮播图需要关联链接，点击可以跳转。
+你实现的时候，可以从老代码里到导入轮播图的图片，插入到你后台的轮播图子模块设计板块。
+当然，我后续可以在这个轮播图子模块设计板块，替换图片，添加图片等。
+
+你如果设计好了，我们就可以通过前端看效果。
+
+你可以看到你之前的设计思想，我们逐步验证合理性！（ 首页每个子模块排序，启用状态，样式配置，内容数据等怎么实现，你需要一点点实现和修改！！！！以下是你的设计，之前你跟我说的）
+2. `home_modules` 首页模块表（名称、标识、排序、启用状态、样式配置、内容数据）
+
+3. `nav_menus` 导航菜单表（名称、链接、排序、启用状态）
+
+
+我们先做轮播图
+
+---
+
+
+前端首页采用 **配置驱动渲染** 模式拼装模块，核心机制如下：
+
+## 1. 模块注册表（第275-281行）
+
+```javascript
+const moduleComponents = {
+ banner: BannerModule, // 轮播图
+ features: FeaturesModule, // 产品优势
+ news: NewsModule, // 新闻动态
+ partners: PartnersModule, // 合作伙伴
+ contact: ContactModule, // 联系我们
+};
+```
+
+每个 `type` 对应一个组件，类似路由表。
+
+## 2. 数据获取（第292-305行）
+
+```javascript
+const [modules, settings] = await Promise.all([
+ modulesApi.getPublic(), // GET /api/home/modules
+ settingsApi.getPublic(), // GET /api/settings
+]);
+```
+
+一次性获取所有启用的模块，按 `sort_order` 排序。
+
+## 3. 动态渲染（第317-329行）
+
+```javascript
+{modules.map((module) => {
+ const Component = moduleComponents[module.type];
+ if (!Component) return null;
+
+ return (
+
+ );
+})}
+```
+
+**关键逻辑**：
+- 遍历后端返回的模块数组
+- 根据 `type` 查找对应组件
+- 统一传递 `title/subtitle/content` 三个属性
+
+## 4. 各模块自治
+
+每个模块组件独立解析自己的 `content` 结构：
+
+| 模块 | content 结构 | 数据来源 |
+|-----|-------------|---------|
+| Banner | `slides: [{image, title, subtitle, link}]` | 后台配置 |
+| Features | `items: [{icon, title, description}]` | 后台配置 |
+| News | `count: number` | 静态占位 |
+| Partners | `logos: [{url, name}]` | 后台配置 |
+| Contact | `phone, email, address` | 后台配置 |
+
+## 优势
+
+1. **可配置** - 后台启用/禁用模块，前台自动显示/隐藏
+2. **可排序** - 调整 `sort_order` 改变模块顺序
+3. **可扩展** - 新增模块只需：加组件 + 注册到 `moduleComponents`
+4. **解耦** - 各模块内部逻辑独立，互不影响
+
+目前只有 **Banner 模块** 接入了真实数据（4张轮播图），其他模块显示的是静态占位数据。需要我继续对接其他模块的后台数据吗？
+
+---
+
+
+看到了，现在的 features 模块很简单，只有 icon + 标题 + 描述。我来设计一个完整的**特性展示卡片系统**方案：
+
+---
+
+## 方案建议
+
+### 1. 卡片数据结构
+
+```json
+{
+ "items": [
+ {
+ "id": 1,
+ "title": "智能检测",
+ "subtitle": "AI 驱动的健康分析",
+ "description": "通过手机拍照即可检测尿常规指标...",
+ "media_type": "image", // image | video | icon | none
+ "media_url": "/assets/feature-1.jpg",
+ "icon": "🔬", // media_type=icon 时显示
+ "link": "/product",
+ "accent_color": "#4ADE80" // 卡片主题色
+ }
+ ],
+ "layout": "grid", // grid | horizontal | vertical
+ "columns": 3, // 2-4列
+ "hover_effect": "lift" // lift | scale | glow | border
+}
+```
+
+### 2. 视觉风格（新粗野主义）
+
+| 元素 | 设计 |
+|:---|:---|
+| **卡片** | 白底 + 2px黑边框 + 4px硬阴影 |
+| **悬停** | 阴影消失 + 卡片位移(2px,2px) + 边框加粗 |
+| **媒体区** | 顶部图片/视频，圆角0px，保持直角风格 |
+| **图标** | 大图标(48px) + 彩色背景圆形/方形 |
+
+### 3. 支持的媒体类型
+
+| 类型 | 说明 |
+|:---|:---|
+| **Icon** | emoji 或 SVG 图标，简单轻量 |
+| **Image** | 产品截图、示意图，建议 400x300 |
+| **Video** | 短视频/GIF，自动静音播放，悬停时播放 |
+| **None** | 纯文字卡片，突出标题和描述 |
+
+### 4. 悬停动效选项
+
+| 效果 | 描述 |
+|:---|:---|
+| **Lift** | 卡片上移4px + 阴影加深，经典感 |
+| **Scale** | 微微放大1.02倍 + 边框变色 |
+| **Glow** | 边框发光（适合深色卡片）|
+| **Border** | 边框从2px→4px，强调轮廓 |
+
+---
+
+## 我的建议
+
+**先简单一点**（你的风格）：
+
+1. **卡片结构**：图片(上) + 标题 + 描述 + 链接按钮
+2. **数量**：默认3个，可添加至5个
+3. **动效**：Lift（上移+阴影变化），最符合新粗野主义
+4. **后台管理**：类似 BannerManage，可拖拽排序卡片
+
+**后台界面示意：**
+```
+┌─────────────────────────────┐
+│ 特性展示管理 [+添加卡片] │
+├─────────────────────────────┤
+│ 布局: [3列 ▼] 悬停效果: [Lift ▼] │
+├─────────────────────────────┤
+│ ┌─────┐ ┌─────┐ ┌─────┐ │
+│ │ 🖼️ │ │ 🖼️ │ │ 🖼️ │ ← 卡片预览 │
+│ │标题1│ │标题2│ │标题3│ │
+│ │ ↑↓ │ │ ↑↓ │ │ ↑↓ │ ← 排序按钮 │
+│ │编辑 │ │编辑 │ │编辑 │ │
+│ └─────┘ └─────┘ └─────┘ │
+└─────────────────────────────┘
+```
+
+---
+
+**确认后我开始写代码**：
+
+1. 改 PublicHome.jsx 的 FeaturesModule（支持图片/视频 + 悬停动效）
+2. 创建 FeaturesManage.jsx 后台管理页面（卡片CRUD + 排序）
+3. 确认数据结构和字段
+
+**你的选择是？**（可以逐项确认）
+- A. 卡片结构（图片+标题+描述+链接）
+- B. 数量限制（3-5个，默认3）
+- C. 悬停效果（Lift/Scale/Glow/Border）
+- D. 是否支持视频（先只做图片，还是图片+视频）
+
+---
+
+**你的选择是？**（可以逐项确认）
+- A. 卡片结构（图片+标题+描述+链接）
+Answer： 图片+标题+描述+链接 可以都有，也可以缺少某１，２项。
+
+- B. 数量限制（3-5个，默认3）
+Answer: 默认３项，但是可以删除或者添加１个？也就是动态的，不超过个５个不少于２个。
+卡片越少，卡片越大，两个最大，５个最小。
+
+- C. 悬停效果（Lift/Scale/Glow/Border）
+Answer：悬停效果，你先做一个，我看效果。
+- D. 是否支持视频（先只做图片，还是图片+视频）
+Answer：我觉得支持视频和图片，视频和图片的效果应该是不一样的？
+鼠标移到上去，视频播放？鼠标移开，视频停止？这个你觉得可否？你的建议？
+支持视频是想测试一下视频效果，如果效果好，其他的模块也可以考虑视频。
+我在website-new目录下，放了720p.mp4视频文件，你可以复制或移动到合适的地方，做测试
+
+---
+
+对，**目前只能修改代码**。
+
+当前架构下，样式和动效是硬编码在 React 组件里的（`FeatureCard` 组件）。用户通过 CMS 只能改内容（标题、描述、图片），不能改：
+
+- 悬停效果（现在是 Lift 上移+阴影）
+- 卡片圆角、边框样式
+- 动画类型和速度
+- 文字位置、遮罩样式
+
+---
+
+## 如果要实现样式定制，有几个方案：
+
+### 方案1：Content 里加 style 配置（推荐）
+
+在 `content` 里增加 `style` 字段，存储样式参数：
+
+```json
+{
+ "items": [...],
+ "style": {
+ "cardEffect": "lift", // lift | scale | glow | none
+ "borderRadius": 0, // 0-20px
+ "hoverShadow": true,
+ "titlePosition": "bottom", // bottom | overlay | top
+ "overlayGradient": "dark" // dark | light | none
+ }
+}
+```
+
+**优点**：用户可配，不需要改代码
+**缺点**：需要写很多条件渲染代码，容易出 bug
+
+---
+
+### 方案2：预定义主题模板
+
+提供 3-5 套预设样式（卡片 A/B/C 版），用户选一套：
+
+```json
+{
+ "items": [...],
+ "theme": "minimal" // minimal | elevated | glass | neon
+}
+```
+
+每套主题对应写好的 CSS class，切换即可。
+
+---
+
+### 方案3：CSS 变量覆盖（最简单）
+
+在 CMS 里加一个"自定义 CSS"文本框，用户写：
+
+```css
+.feature-card:hover {
+ transform: scale(1.02);
+ box-shadow: 0 0 20px rgba(102, 126, 234, 0.3);
+}
+```
+
+保存后注入到页面 `