# Self-Evolving Skill - OpenClaw集成 ## 项目结构 ``` self-evolving-skill/ ├── core/ # Python核心模块 │ ├── residual_pyramid.py # 残差金字塔分解 │ ├── reflection_trigger.py # 自适应触发器 │ ├── experience_replay.py # 经验回放 │ ├── skill_engine.py # 核心引擎 │ ├── storage.py # 持久化 │ └── mcp_server.py # MCP服务器 ├── src/ # TypeScript封装 │ ├── index.ts # 主入口 │ ├── cli.ts # CLI │ └── mcp-tools.ts # MCP工具定义 ├── skills/ # 供OpenClaw调用 │ └── self-evolving-skill/ # OpenClaw Skill ├── SKILL.md # 技能文档 ├── package.json └── README.md ``` ## 安装到OpenClaw ```bash # 方式1: 链接到OpenClaw skills目录 cd skills/self-evolving-skill npm install npm run build # 链接 ln -s $(pwd)/skills/self-evolving-skill ~/.openclaw/skills/self-evolving-skill # 方式2: 通过ClawHub clawhub install self-evolving-skill ``` ## OpenClaw中调用 ```typescript // 直接调用MCP工具 const result = await useTool('skill_create', { name: 'ProblemSolver' }); const analysis = await useTool('skill_analyze', { embedding: [0.1, 0.2, 0.3, ...] }); ``` ## MCP工具列表 | 工具 | 描述 | 参数 | |------|------|------| | `skill_create` | 创建Skill | `name`, `description` | | `skill_execute` | 执行并学习 | `skill_id`, `context`, `success` | | `skill_analyze` | 分析嵌入 | `embedding` | | `skill_list` | 列出Skills | - | | `skill_stats` | 系统统计 | - | | `skill_save` | 持久化保存 | `skill_id` | | `skill_load` | 加载 | `skill_id` | ## 示例 ```typescript // 1. 创建Skill const skill = await useTool('skill_create', { name: 'TextAnalyzer', description: '文本分析自学习Skill' }); // 2. 执行并观察学习 const result = await useTool('skill_execute', { skill_id: skill.skill_id, context: { task: 'sentiment' }, success: true, value: 1.0 }); console.log('反思触发:', result.reflection_triggered); // 3. 分析新输入 const analysis = await useTool('skill_analyze', { embedding: generateEmbedding(text) }); ``` ## 配置 在OpenClaw配置文件中: ```yaml skills: self-evolving-skill: max_layers: 5 energy_threshold: 0.1 similarity_threshold: 0.85 target_trigger_rate: 0.15 storage_dir: ~/.openclaw/self-evolving ```