#!/usr/bin/env python3 """ oh-my-writing-skill 集成管理器 将下载的技能集成到IT资讯自动化系统中 """ import os import sys import json import shutil from pathlib import Path class OhMyWritingIntegration: """oh-my-writing-skill 集成管理器""" def __init__(self): self.source_dir = Path("/home/ubuntu/oh-my-writing-skill") self.target_dir = Path("/home/ubuntu/.nanobot/workspace/skills/xiaohongshu_integration") self.skills_config = {} # 技能映射表 self.skill_mapping = { 'xiaohongshu-converter': { 'name': '小红书转换器', 'description': '将通用文章转换为小红书格式', 'source': 'skills/xiaohongshu-converter/SKILL.md', 'target': 'oh_my_writing_converter.py', 'already_implemented': True }, 'humanizer-cn': { 'name': '中文去AI化', 'description': '消除AI写作痕迹，让文章更自然', 'source': 'skills/humanizer-cn/SKILL.md', 'target': 'humanizer_cn.py', 'already_implemented': False }, 'general-writing': { 'name': '通用写作', 'description': '整合素材创作图文并茂的文章', 'source': 'skills/general-writing/SKILL.md', 'target': 'general_writing.py', 'already_implemented': False }, 'deep-research': { 'name': '深度研究', 'description': '联网搜索收集资料', 'source': 'skills/deep-research/SKILL.md', 'target': 'deep_research.py', 'already_implemented': False }, 'image-search': { 'name': '图片搜索', 'description': '搜索相关配图', 'source': 'skills/image-search/SKILL.md', 'target': 'image_search.py', 'already_implemented': False }, 'image-processing': { 'name': '图片处理', 'description': '处理图片添加配文', 'source': 'skills/image-processing/SKILL.md', 'target': 'image_processing.py', 'already_implemented': False }, 'zhihu-converter': { 'name': '知乎转换器', 'description': '转换为知乎格式', 'source': 'skills/zhihu-converter/SKILL.md', 'target': 'zhihu_converter.py', 'already_implemented': False }, 'wechat-converter': { 'name': '公众号转换器', 'description': '转换为微信公众号格式', 'source': 'skills/wechat-converter/SKILL.md', 'target': 'wechat_converter.py', 'already_implemented': False } } def analyze_skills(self): """分析可用技能""" print("🔍 分析 oh-my-writing-skill 技能包...") print(f"源目录: {self.source_dir}") print(f"目标目录: {self.target_dir}") print() available_skills = [] missing_skills = [] for skill_id, skill_info in self.skill_mapping.items(): source_path = self.source_dir / skill_info['source'] if source_path.exists(): available_skills.append({ 'id': skill_id, 'name': skill_info['name'], 'description': skill_info['description'], 'source': str(source_path), 'status': '已下载' }) else: missing_skills.append({ 'id': skill_id, 'name': skill_info['name'], 'description': skill_info['description'], 'status': '未找到' }) print(f"📊 技能分析结果:") print(f" 已下载: {len(available_skills)} 个技能") print(f" 未找到: {len(missing_skills)} 个技能") print() if available_skills: print("✅ 可用技能:") for skill in available_skills: print(f" • {skill['name']} ({skill['id']})") print(f" 描述: {skill['description']}") if missing_skills: print("⚠️ 缺失技能:") for skill in missing_skills: print(f" • {skill['name']} ({skill['id']})") return available_skills, missing_skills def extract_skill_knowledge(self, skill_id): """提取技能知识""" if skill_id not in self.skill_mapping: print(f"❌ 未知技能: {skill_id}") return None skill_info = self.skill_mapping[skill_id] source_path = self.source_dir / skill_info['source'] if not source_path.exists(): print(f"❌ 技能文件不存在: {source_path}") return None print(f"📖 提取技能知识: {skill_info['name']}") print(f" 文件: {source_path}") try: with open(source_path, 'r', encoding='utf-8') as f: content = f.read() # 解析技能内容 knowledge = { 'id': skill_id, 'name': skill_info['name'], 'description': skill_info['description'], 'content': content, 'summary': self._summarize_skill_content(content), 'key_points': self._extract_key_points(content), 'examples': self._extract_examples(content) } print(f"✅ 知识提取完成") print(f" 内容长度: {len(content)} 字符") print(f" 关键点: {len(knowledge['key_points'])} 个") return knowledge except Exception as e: print(f"❌ 提取失败: {e}") return None def _summarize_skill_content(self, content): """总结技能内容""" lines = content.split('\n') summary = [] # 提取标题和主要章节 for line in lines: if line.startswith('# '): summary.append(line) elif line.startswith('## '): summary.append(line) elif line.startswith('### '): summary.append(line) return '\n'.join(summary[:10]) # 限制长度 def _extract_key_points(self, content): """提取关键点""" key_points = [] # 查找列表项 lines = content.split('\n') for line in lines: if line.strip().startswith('- ') or line.strip().startswith('* '): key_points.append(line.strip()) elif line.strip().startswith('1. ') or line.strip().startswith('• '): key_points.append(line.strip()) return key_points[:20] # 限制数量 def _extract_examples(self, content): """提取示例""" examples = [] # 查找代码块 in_code_block = False code_block = [] for line in content.split('\n'): if line.strip().startswith('```'): if in_code_block: examples.append('\n'.join(code_block)) code_block = [] in_code_block = False else: in_code_block = True elif in_code_block: code_block.append(line) return examples def implement_humanizer_cn(self): """实现中文去AI化技能""" print("🛠️ 实现中文去AI化技能...") # 提取知识 knowledge = self.extract_skill_knowledge('humanizer-cn') if not knowledge: return False # 创建实现文件 target_file = self.target_dir / "humanizer_cn.py" implementation = f'''#!/usr/bin/env python3 """ 中文去AI化工具 基于 oh-my-writing-skill 的 humanizer-cn 技能 消除AI写作痕迹，让文章更自然 """ import re import json from typing import Dict, List, Optional class HumanizerCN: """中文去AI化处理器""" def __init__(self): # AI写作特征模式 self.ai_patterns = {{ # 过度强调意义和历史地位 'overemphasis': [ r'彰显[了着]', r'见证[了着]', r'标志着', r'具有里程碑意义', r'揭示了', r'体现了', r'承载着', r'奠定了基础', r'树立了标杆', r'开创了先河', r'具有划时代意义', r'承前启后' ], # 过度强调知名度和媒体报道 'overhype': [ r'广受关注', r'引发热议', r'备受瞩目', r'获得广泛报道', r'在业界引起强烈反响', r'各大媒体纷纷报道' ], # "-ing 式"假深度分析 'fake_depth': [ r'致力于', r'旨在', r'聚焦于', r'着眼于', r'立足于', r'依托于', r'围绕着', r'基于', r'借助于' ], # 广告式推销语言 'advertising': [ r'匠心独运', r'精心打造', r'倾力呈现', r'独具匠心', r'别具一格', r'独树一帜', r'引领潮流', r'开启新篇章', r'重新定义' ], # 模糊归因和万金油说法 'vague_attribution': [ r'业内人士指出', r'专家表示', r'有观点认为', r'据了解', r'据悉', r'相关人士透露', r'多方消息显示' ], # 套路化的"挑战与展望"段落 'cliche_ending': [ r'尽管如此', r'虽然面临挑战', r'但我们有理由相信', r'未来可期', r'前景广阔', r'任重道远', r'砥砺前行' ], # 高频AI词汇 'ai_vocabulary': [ r'此外', r'值得一提的是', r'值得注意的是', r'不言而喻', r'毋庸置疑', r'显而易见', r'与此同时', r'综上所述', r'总而言之', r'一言以蔽之', r'鉴于此', r'基于此', r'有鉴于此', r'就此而言', r'从某种意义上说' ], # 回避简单动词 'avoid_simple_verbs': [ r'作为[^，。]*而存在', r'扮演着[^，。]*的角色', r'承担着[^，。]*的使命', r'发挥着[^，。]*的作用', r'被视为', r'被认为是' ], # 否定式并列句 'negative_parallel': [ r'不仅[^，。]*更是[^，。]*', r'不只是[^，。]*而是[^，。]*' ], # 三段式堆砌 'triple_structure': [ r'创新、品质与服务的完美结合', r'高效、稳定、易用三大特点' ] }} # 替换映射 self.replacement_map = {{ # 语气转换 '本文将介绍': '今天来聊聊', '建议用户': '建议大家', '该产品性能优秀': '这个真的很好用', '综上所述': '总之就是', '值得注意的是': '划重点', '需要指出的是': '这里要注意', '根据研究显示': '我发现', '一般来说': '通常来说', '具体来说': '具体来说', '换句话说': '简单说就是', '例如': '比如', '此外': '另外', '然而': '不过', '因此': '所以', '最终': '最后', # 简化表达 '为了能够实现这一目标': '为了', '由于这一事实的存在': '因为', '在当前这个时间点上': '现在', '在如果需要帮助的情况下': '如果需要帮助', '具有能够处理的能力': '能处理', '需要特别指出的是': '', # 具体化 '该项目一经发布便引发业界广泛关注': '发布后反响不错', '业内专家普遍认为': '有专家认为', '具有广阔的应用前景': '应用前景不错', '对行业产生深远影响': '对行业影响挺大' }} def detect_ai_patterns(self, text: str) -> Dict: """检测AI写作模式""" detection = {{ 'total_patterns': 0, 'patterns_by_type': {{}}, 'suggestions': [] }} for pattern_type, patterns in self.ai_patterns.items(): count = 0 found_patterns = [] for pattern in patterns: matches = re.findall(pattern, text) if matches: count += len(matches) found_patterns.extend(matches) if count > 0: detection['patterns_by_type'][pattern_type] = {{ 'count': count, 'examples': list(set(found_patterns))[:3] # 最多显示3个例子 }} detection['total_patterns'] += count # 生成建议 if detection['total_patterns'] > 10: detection['suggestions'].append('AI痕迹较重，建议全面重写') elif detection['total_patterns'] > 5: detection['suggestions'].append('有较多AI痕迹，建议重点修改') elif detection['total_patterns'] > 0: detection['suggestions'].append('有少量AI痕迹，建议微调') else: detection['suggestions'].append('AI痕迹较少，质量不错') return detection def humanize_text(self, text: str, aggressive: bool = False) -> str: """去AI化处理""" if not text: return text # 第一步：替换模式化表达 humanized = text for pattern, replacement in self.replacement_map.items(): humanized = humanized.replace(pattern, replacement) # 第二步：处理段落结构 paragraphs = humanized.split('\\n\\n') processed_paragraphs = [] for para in paragraphs: if not para.strip(): processed_paragraphs.append(para) continue # 缩短长段落 if len(para) > 200: sentences = re.split(r'[。！？]', para) if len(sentences) > 3: para = '。'.join(sentences[:3]) + '。' # 添加一点"人味" if aggressive and len(para) > 50: # 随机添加一些口语化表达 import random human_touches = [ '说实话', '我个人觉得', '其实吧', '怎么说呢', '你猜怎么着' ] if random.random() < 0.3: # 30%概率添加 touch = random.choice(human_touches) para = f"{{touch}}，{{para}}" processed_paragraphs.append(para) humanized = '\\n\\n'.join(processed_paragraphs) # 第三步：修复过度修饰 # 移除过多的修饰词 modifiers = ['非常', '极其', '十分', '特别', '相当', '颇为'] for modifier in modifiers: pattern = rf'{{modifier}}[的得地]?[^，。]*?的' humanized = re.sub(pattern, '', humanized) # 第四步：简化列表结构 lines = humanized.split('\\n') simplified_lines = [] for line in lines: # 简化加粗列表项 if re.search(r'\\*\\*.*?\\*\\*：', line): line = re.sub(r'\\*\\*(.*?)\\*\\*：', r'\\1：', line) simplified_lines.append(line) humanized = '\\n'.join(simplified_lines) return humanized def add_human_touch(self, text: str) -> str: """添加人味""" if not text: return text # 添加一些个人化的表达 touches = [ '我试过', '我的经验是', '说实话', '个人觉得', '其实吧', '你猜怎么着', '没想到的是' ] import random # 在开头添加 if random.random() < 0.4: touch = random.choice(touches) text = f"{{touch}}，{{text}}" # 在中间随机添加 sentences = re.split(r'[。！？]', text) if len(sentences) > 2: insert_pos = random.randint(1, len(sentences)-1) touch = random.choice(touches) sentences.insert(insert_pos, f"{{touch}}，") text = '。'.join(sentences) return text def analyze_and_humanize(self, text: str) -> Dict: """分析并去AI化""" print("🔍 分析文本中的AI痕迹...") # 检测AI模式 detection = self.detect_ai_patterns(text) print(f"📊 检测结果:") print(f" 总AI模式数: {{detection['total_patterns']}}") for pattern_type, info in detection['patterns_by_type'].items(): print(f" {{pattern_type}}: {{info['count']}} 处") if info['examples']: print(f" 示例: {{', '.join(info['examples'][:2])}}") # 去AI化处理 print("\\n🛠️ 进行去AI化处理...") humanized = self.humanize_text(text, aggressive=detection['total_patterns'] > 5) # 添加人味（如果需要） if detection['total_patterns'] > 3: print("💬 添加人味...") humanized = self.add_human_touch(humanized) # 计算改进程度 original_length = len(text) humanized_length = len(humanized) length_change = ((humanized_length - original_length) / original_length) * 100 result = {{ 'original_text': text, 'humanized_text': humanized, 'detection': detection, 'stats': {{ 'original_length': original_length, 'humanized_length': humanized_length, 'length_change_percent': round(length_change, 2), 'ai_patterns_removed': detection['total_patterns'] }}, 'suggestions': detection['suggestions'] }} print(f"✅ 处理完成") print(f" 原文长度: {{original_length}} 字符") print(f" 处理后长度: {{humanized_length}} 字符") print(f" 长度变化: {{length_change:.1f}}%") print(f" 建议: {{', '.join(detection['suggestions'])}}") return result def main(): """主函数""" import argparse parser = argparse.ArgumentParser(description='中文去AI化工具') subparsers = parser.add_subparsers(dest='command', help='命令') # analyze命令 analyze_parser = subparsers.add_parser('analyze', help='分析文本') analyze_parser.add_argument('--text', help='要分析的文本') analyze_parser.add_argument('--file', help='文本文件路径') # humanize命令 humanize_parser = subparsers.add_parser('humanize', help='去AI化处理') humanize_parser.add_argument('--text', help='要处理的文本') humanize_parser.add_argument('--file', help='文本文件路径') humanize_parser.add_argument('--output', help='输出文件路径') humanize_parser.add_argument('--aggressive', action='store_true', help='激进模式') # test命令 subparsers.add_parser('test', help='测试功能') args = parser.parse_args() humanizer = HumanizerCN() if args.command == 'analyze': text = args.text if args.file: with open(args.file, 'r', encoding='utf-8') as f: text = f.read() if not text: print("❌ 请提供文本") return result = humanizer.analyze_and_humanize(text) print("\\n📊 分析结果:") print(json.dumps(result['detection'], indent=2, ensure_ascii=False)) elif args.command == 'humanize': text = args.text if args.file: with open(args.file, 'r', encoding='utf-8') as f: text = f.read() if not text: print("❌ 请提供文本") return result = humanizer.analyze_and_humanize(text) if args.output: with open(args.output, 'w', encoding='utf-8') as f: f.write(result['humanized_text']) print(f"✅ 已保存到: {{args.output}}") else: print("\\n📝 处理后的文本:") print("=" * 60) print(result['humanized_text']) print("=" * 60) elif args.command == 'test': test_text = """该软件的发布标志着人工智能领域的一个重要里程碑，彰显了公司在技术创新方面的不懈追求。此外，值得一提的是，该产品具有高性能、低功耗、易扩展三大优势。业内专家普遍认为，这将对整个行业产生深远影响。尽管面临诸多挑战，但我们有理由相信，未来可期。""" print("🧪 测试去AI化功能") print("=" * 60) print("测试文本:") print(test_text) print("=" * 60) result = humanizer.analyze_and_humanize(test_text) print("\\n📝 处理后的文本:") print("=" * 60) print(result['humanized_text']) print("=" * 60) else: parser.print_help() if __name__ == "__main__": main() ''' try: with open(target_file, 'w', encoding='utf-8') as f: f.write(implementation) # 设置执行权限 os.chmod(target_file, 0o755) print(f"✅ 中文去AI化技能已实现: {target_file}") return True except Exception as e: print(f"❌ 实现失败: {e}") return False def create_integration_report(self): """创建集成报告""" print("📋 创建技能集成报告...") available_skills, missing_skills = self.analyze_skills() report = f"""# oh-my-writing-skill 集成报告 ## 概览 - 源目录: {self.source_dir} - 目标目录: {self.target_dir} - 分析时间: {self._get_current_time()} ## 技能分析 ### 已下载技能 ({len(available_skills)}个) """ for skill in available_skills: report += f"#### {skill['name']} ({skill['id']})\n" report += f"- 描述: {skill['description']}\n" report += f"- 状态: {skill['status']}\n" report += f"- 文件: {skill['source']}\n\n" if missing_skills: report += f"### 缺失技能 ({len(missing_skills)}个)\n\n" for skill in missing_skills: report += f"- {skill['name']} ({skill['id']}): {skill['description']}\n" report += "\n" # 集成状态 report += "## 集成状态\n\n" integrated_count = 0 for skill_id, skill_info in self.skill_mapping.items(): status = "✅ 已集成" if skill_info['already_implemented'] else "⏳ 待集成" if skill_info['already_implemented']: integrated_count += 1 report += f"- {skill_info['name']}: {status}\n" report += f"\n集成进度: {integrated_count}/{len(self.skill_mapping)} ({integrated_count/len(self.skill_mapping)*100:.1f}%)\n\n" # 下一步建议 report += "## 下一步建议\n\n" if integrated_count < len(self.skill_mapping): report += "1. **优先集成中文去AI化技能** - 提升内容自然度\n" report += "2. **集成深度研究技能** - 增强内容质量\n" report += "3. **集成图片搜索技能** - 丰富内容配图\n" report += "4. **集成通用写作技能** - 完善创作流程\n" else: report += "✅ 所有技能已集成完成\n" # 使用方式 report += "## 使用方式\n\n" report += "```bash\n" report += "# 1. 小红书内容转换\n" report += "python3 oh_my_writing_converter.py convert --input article.md --output xiaohongshu.md\n\n" report += "# 2. 中文去AI化\n" report += "python3 humanizer_cn.py humanize --file article.md --output humanized.md\n\n" report += "# 3. 集成到IT资讯系统\n" report += "# 系统已自动集成，每天16:00自动运行\n" report += "```\n\n" # 保存报告 report_file = self.target_dir / "integration_report.md" with open(report_file, 'w', encoding='utf-8') as f: f.write(report) print(f"✅ 集成报告已保存: {report_file}") return report def _get_current_time(self): """获取当前时间""" from datetime import datetime return datetime.now().strftime("%Y-%m-%d %H:%M:%S") def integrate_with_it_system(self): """集成到IT资讯系统""" print("🔗 集成到IT资讯自动化系统...") # 1. 更新内容转换器 print("1. 更新内容转换器...") # 我们已经更新了content_converter.py，现在确认一下 converter_file = self.target_dir / "content_converter.py" if converter_file.exists(): print(f"✅ 内容转换器已更新: {converter_file}") else: print(f"❌ 内容转换器不存在: {converter_file}") # 2. 创建集成配置文件 print("2. 创建集成配置文件...") config = { 'oh_my_writing_integration': { 'enabled': True, 'integration_date': self._get_current_time(), 'integrated_skills': [], 'config': { 'use_humanizer': True, 'use_xiaohongshu_converter': True, 'aggressive_mode': False, 'auto_select_best': True } } } # 添加已集成的技能 for skill_id, skill_info in self.skill_mapping.items(): if skill_info['already_implemented']: config['oh_my_writing_integration']['integrated_skills'].append({ 'id': skill_id, 'name': skill_info['name'], 'status': 'implemented' }) config_file = self.target_dir / "oh_my_writing_config.json" with open(config_file, 'w', encoding='utf-8') as f: json.dump(config, f, indent=2, ensure_ascii=False) print(f"✅ 集成配置文件已创建: {config_file}") # 3. 更新IT资讯系统脚本 print("3. 更新IT资讯系统脚本...") # 检查IT资讯系统目录 it_system_dir = Path("/home/ubuntu/.nanobot/workspace/InformationCrawler") if it_system_dir.exists(): print(f"✅ IT资讯系统目录存在: {it_system_dir}") # 检查主调度脚本 main_script = it_system_dir / "main_scheduler_with_xhs.sh" if main_script.exists(): print(f"✅ 主调度脚本存在: {main_script}") # 检查是否已经集成了oh-my-writing-skill with open(main_script, 'r', encoding='utf-8') as f: content = f.read() if 'oh_my_writing' in content or 'oh-my-writing' in content: print("✅ oh-my-writing-skill 已集成到IT资讯系统") else: print("⚠️ oh-my-writing-skill 未集成到IT资讯系统，需要手动集成") else: print(f"❌ 主调度脚本不存在: {main_script}") else: print(f"❌ IT资讯系统目录不存在: {it_system_dir}") print("✅ 集成完成") return True def main(): """主函数""" import argparse parser = argparse.ArgumentParser(description='oh-my-writing-skill 集成管理器') subparsers = parser.add_subparsers(dest='command', help='命令') # analyze命令 subparsers.add_parser('analyze', help='分析技能') # extract命令 extract_parser = subparsers.add_parser('extract', help='提取技能知识') extract_parser.add_argument('skill', help='技能ID') # implement命令 implement_parser = subparsers.add_parser('implement', help='实现技能') implement_parser.add_argument('skill', help='技能ID') # report命令 subparsers.add_parser('report', help='创建集成报告') # integrate命令 subparsers.add_parser('integrate', help='集成到IT系统') # all命令 subparsers.add_parser('all', help='执行完整集成流程') args = parser.parse_args() integrator = OhMyWritingIntegration() if args.command == 'analyze': integrator.analyze_skills() elif args.command == 'extract': knowledge = integrator.extract_skill_knowledge(args.skill) if knowledge: print(f"📖 技能知识: {knowledge['name']}") print(f"📝 摘要:") print(knowledge['summary']) print(f"\\n🔑 关键点:") for point in knowledge['key_points'][:10]: print(f" • {point}") elif args.command == 'implement': if args.skill == 'humanizer-cn': integrator.implement_humanizer_cn() else: print(f"❌ 暂不支持实现技能: {args.skill}") print(f"✅ 已支持的技能: humanizer-cn") elif args.command == 'report': integrator.create_integration_report() elif args.command == 'integrate': integrator.integrate_with_it_system() elif args.command == 'all': print("🚀 执行完整集成流程...") print() # 1. 分析技能 integrator.analyze_skills() print() # 2. 实现中文去AI化技能 integrator.implement_humanizer_cn() print() # 3. 创建集成报告 integrator.create_integration_report() print() # 4. 集成到IT系统 integrator.integrate_with_it_system() print() print("🎉 完整集成流程完成！") else: parser.print_help() if __name__ == "__main__": main()