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+# Self-Evolving Skill - OpenClaw集成指南
+
+## 安装完成 ✅
+
+### 文件位置
+
+| 位置 | 说明 |
+|------|------|
+| `~/.openclaw/skills/self-evolving-skill/` | 技能根目录 |
+| `~/.openclaw/agents/main/agent/mcp_servers.json` | MCP服务器配置 |
+| `~/.openclaw/skills/self-evolving-skill/storage/` | 数据存储 |
+
+### 项目结构
+
+```
+~/.openclaw/skills/self-evolving-skill/
+├── core/                      # Python核心
+│   ├── residual_pyramid.py     # SVD分解
+│   ├── reflection_trigger.py   # 自适应触发
+│   ├── 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 SDK
+├── SKILL.md                  # 技能文档
+├── package.json              # npm配置
+├── mcporter_adapter.py       # mcporter适配器
+└── venv/                     # Python虚拟环境
+```
+
+## 使用方式
+
+### 1. 直接调用（推荐）
+
+```bash
+# 激活虚拟环境
+source ~/.openclaw/skills/self-evolving-skill/venv/bin/activate
+
+# 列出所有Skill
+python3 ~/.openclaw/skills/self-evolving-skill/mcporter_adapter.py skill_list '{}'
+
+# 创建新Skill
+python3 ~/.openclaw/skills/self-evolving-skill/mcporter_adapter.py skill_create '{"name":"MySkill"}'
+
+# 分析嵌入
+python3 ~/.openclaw/skills/self-evolving-skill/mcporter_adapter.py skill_analyze '{"embedding":[0.1,0.2,0.3]}'
+
+# 系统统计
+python3 ~/.openclaw/skills/self-evolving-skill/mcporter_adapter.py skill_stats '{}'
+```
+
+### 2. OpenClaw MCP调用
+
+在OpenClaw中可直接调用MCP工具：
+
+```json
+{
+  "tool": "skill_create",
+  "arguments": {
+    "name": "ProblemSolver",
+    "description": "问题解决技能"
+  }
+}
+```
+
+### 3. 启动MCP服务器
+
+```bash
+# 前台运行
+source ~/.openclaw/skills/self-evolving-skill/venv/bin/activate
+python3 ~/.openclaw/skills/self-evolving-skill/mcp_server.py --storage ~/.openclaw/skills/self-evolving-skill/storage
+
+# 或通过配置自动启动（已在mcp_servers.json中配置）
+```
+
+## MCP工具
+
+| 工具 | 描述 | 参数 |
+|------|------|------|
+| `skill_create` | 创建新的自演化Skill | `name`, `description` |
+| `skill_execute` | 执行Skill并触发学习 | `skill_id`, `context`, `success`, `value` |
+| `skill_analyze` | 分析嵌入向量（不触发学习） | `embedding` |
+| `skill_list` | 列出所有已保存的Skill | - |
+| `skill_stats` | 获取系统统计信息 | - |
+| `skill_save` | 持久化保存Skill | `skill_id` |
+| `skill_load` | 加载已保存的Skill | `skill_id` |
+| `skill_clear` | 清空所有数据和缓存 | - |
+
+## 测试结果
+
+```
+=== skill_list ===
+Skills: 20
+
+=== skill_create ===
+{"skill_id":"1ac4a2cb3f79347f","name":"TestOpenClaw"}
+
+=== skill_analyze ===
+{
+  "total_energy": 0.55,
+  "residual_ratio": 0.086,
+  "suggested_abstraction": "POLICY",
+  "novelty_score": 0.657
+}
+```
+
+## 核心算法
+
+### 1. 残差金字塔分解
+
+```python
+pyramid = ResidualPyramid(max_layers=5, use_pca=True)
+decomposition = pyramid.decompose(embedding)
+
+# 输出:
+# - residual_ratio: 残差能量比率
+# - suggested_abstraction: POLICY / SUB_SKILL / PREDICATE
+# - novelty_score: 综合新颖性
+```
+
+### 2. 三层跃迁规则
+
+| 覆盖率 | 抽象层级 | 操作 |
+|--------|---------|------|
+| >80% | POLICY | 调整策略权重 |
+| 40-80% | SUB_SKILL | 生成子Skill |
+| <40% | PREDICATE | 归纳新谓词 |
+
+### 3. 自适应阈值
+
+```python
+trigger = ReflectionTrigger(
+  min_energy_ratio=0.10,     # 初始阈值
+  value_gain_threshold=0.20, # 触发阈值
+  target_trigger_rate=0.15   # 目标15%触发率
+)
+```
+
+## OpenClaw配置
+
+MCP服务器已配置在：
+
+```json
+// ~/.openclaw/agents/main/agent/mcp_servers.json
+{
+  "servers": {
+    "self-evolving-skill": {
+      "name": "self-evolving-skill",
+      "type": "stdio",
+      "command": "/bin/bash",
+      "args": [
+        "-c",
+        "source ~/.openclaw/skills/self-evolving-skill/venv/bin/activate && python3 ~/.openclaw/skills/self-evolving-skill/mcp_server.py --storage ~/.openclaw/skills/self-evolving-skill/storage"
+      ]
+    }
+  }
+}
+```
+
+## 下一步
+
+- [ ] 在OpenClaw中测试MCP工具调用
+- [ ] 集成到Agent执行流程
+- [ ] 添加强化学习策略优化
+
+## 相关文档
+
+- [SKILL.md](SKILL.md) - 完整技能文档
+- [MEMORY.md](../../workspace/MEMORY.md) - 研究笔记