Obsidian/claws_en/How to Make Your OpenClaw Agent 10x More Powerful.md

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title	source	author	published	created	description	tags
How to Make Your OpenClaw Agent 10x More Powerful	https://x.com/PrajwalTomar_/status/2031709787805905128	Unknown	2026-03-11	2026-03-13	Be quiet	clippings

Most people treat OpenClaw like ChatGPT.

Ask questions. Get answers. Close it.

They're leaving 90% of the power on the table.

I've been running a 6-agent autonomous system inside OpenClaw for weeks. Wally, my manager agent, built my entire team while I slept. Every morning I wake up to content ideas, research, and drafts, without asking for a single one.

That's what this tool actually is when you set it up right.

Here's the system ↓

The Real Problem

People open OpenClaw and start asking questions.

They get answers. They close the tab.

They think they've used it.

They've used maybe 10% of what it can do.

The other 90%? It's sitting there. Waiting. Most people never find it because they never change how they think about the tool.

They treat it like a search engine with a personality. That's not what this is.

OpenClaw is an operating system for getting work done. The moment you start treating it that way, everything changes.

I was one of those people. Until I wasn't.

Stop Using One Agent for Everything

This is the first unlock. And it changes everything.

Your main agent should be a manager, not a worker.

When you use one agent for everything, research, strategy, writing, optimising, you get average results across the board. The context gets muddled. The outputs get generic.

The fix is specialisation.

I built Wally, my manager agent. He doesn't do the work. He delegates it.

Here's my team:

→ Radar (trend scout) → Iris (strategist) → Echo (content creator) → Soul (humanizer) → Volt (optimizer)

Each agent has its own memory. Its own model. Its own job.

Wally knows the big picture. The others go deep in their lane. The quality of output when you run it this way versus a single agent is not even close.

This is the system. This is what makes OpenClaw DIFFERENT.

Building Sub-Agents Is Stupid Easy

This is where most people assume it gets complicated. It doesn't.

One prompt does it:

"Create a new persistent agent named [Name] for [specific task]. Set [Model] as primary. Use [Name] for all [task type]."

Your main agent handles the rest. It sets up the agent, assigns the model, and stores the context. You don't touch any settings manually.

I built my entire 6-agent team in under an hour.

You don't need to be technical. You don't need to know how it works under the hood. You just need to know what you want each agent to do and ask clearly.

The Memory Problem Everyone Hits

OpenClaw does "compactions" to save tokens.

The problem: it forgets things after each one.

You re-explain your goals. It forgets again after the next compaction. You re-explain again. It's painful and it kills momentum.

Most people hit this wall and assume it's just a limitation they have to live with.

It's not.

The fix is simple: Memory flush + session memory search.

One prompt enables both. Right before compaction hits, it saves the critical context automatically, your goals, your preferences, your active projects, your workflows. Everything that matters gets preserved.

This saved me hours. Genuinely. If you do nothing else from this article, do this.

Use the Right Model for Each Task

Opus 4.6 is INSANE. But it's not the right tool for everything.

Using Opus for every single task is like hiring a senior consultant to answer your emails. Expensive. Overkill. And it slows you down.

Here's how I think about it:

→ Opus/ Sonnet 4.6 = brain (Wally uses this for high-level strategy and decisions) → GPT 5.4 = coding muscle (anything technical goes here) → Perplexity Sonar Pro via openrouter = web search (Radar uses this to find what's happening right now) → Grok = social search (trends, X conversations, real-time sentiment)

Match the model to the task. You save money. You get better results. Both at the same time.

This is one of those things that sounds obvious once you hear it but almost nobody actually does it.

Skills Make OpenClaw Actually Useful

ClawHub is the app store for your agent.

Think of it like plugins, except your agent can install them itself.

The must-install: Last 30 Days skill.

→ Searches Reddit, X, and YouTube from the past 30 days → Radar uses this to find what's actually trending right now, not six months ago

The difference between an agent with this skill and one without it is massive. Without it, your trend research is based on training data. With it, it's based on what people are actually talking about today.

Your agent can install skills itself. Just paste the link and tell it to install.

One warning: ClawHub is community-driven. Not everything on there is safe. Watch for malicious code. Don't install blindly. Vet what you're adding.

Treat It Like an Employee, Not a Search Engine

This is the mindset shift that changes everything.

Most people prompt reactively. They have a task, they ask, they get an answer. Repeat.

That's not how you get the most out of a great employee. And it's not how you get the most out of OpenClaw.

Brain dump first. Tell it everything about you. Your goals, your projects, your workflows, your voice, your audience, your revenue model. All of it. The more context it has, the better every single output becomes.

Then set expectations:

"I want you to work while I sleep and surprise me daily, but everything that you do should help me advance toward my goals."

That's it. That's the prompt.

Now every morning I wake up to new content ideas, research, and drafts. Ready to go. Without asking.

The quality of what you get out is directly proportional to the quality of context you put in. Treat the onboarding seriously.

Stop Telling It What to Do

This one sounds counterintuitive. It works.

Instead of: "Build this feature"

Try: "Based on what you know about me, what should we build next?"

OpenClaw is smarter than you think. Let it lead.

When you reverse the prompt, you stop being the bottleneck. You stop limiting the output to what you already know. You open the door to ideas you wouldn't have thought of yourself.

I use this with Wally every day. He consistently comes up with better tasks than I would have assigned him.

Reverse prompting. Remember that phrase.

Make It Proactive (This Is Where It Gets INSANE)

Set up morning briefs:

"Every morning at 8am, send me AI news, content ideas, and tasks."

Then go further:

"Surprise me daily at 9am with something you built overnight."

Wally built my entire agent team, the one I described at the start of this article, while I slept. I woke up to a fully structured multi-agent system with named agents, assigned models, and defined roles.

I didn't ask for it. He just did it.

This is not a gimmick. This is not a flex. This is what happens when you set up the system properly and let it run.

This is the future.

TLDR

→ Sub-agents for specialized tasks (manager + workers) → Memory flush before compaction → Right models for right tasks (brain vs muscles) → Skills from ClawHub (Last 30 Days is a must) → Brain dump + set expectations → Reverse prompting (let it lead) → Autonomous morning briefs

Most people will read this and do nothing.

The ones who move on it this week will have a fully autonomous system running while everyone else is still asking ChatGPT questions one prompt at a time.

2026 is going to be UNFAIR for builders who move early.

Bookmark this.