Self Evolving Skill

# Self-Evolving Skill

元认知自学习系统 - 基于预测编码和价值驱动的Skill自动演化。

## 功能

- **ResidualPyramid金字塔分解，量化认知缺口** - **自适应反思触发**: 基于残差能量自动判断何时需要学习 - **经验回放**: 缓存已学模式，降低重复触发 - **价值门控**: 只有提升长期价值才接受变异 - **持久化**: 经验自动保存/加载

## 安装

```bash # 技能已安装到 ~/.openclaw/skills/self-evolving-skill # 或使用ClawHub clawhub install self-evolving-skill ```

## 架构

``` self-evolving-skill/ ├── core/ # Python核心 │ ├── residual_pyramid.py # 残差金字塔（SVD分解） │ ├── reflection_trigger.py # 自适应触发器 │ ├── experience_replay.py # 经验回放缓存 │ ├── skill_engine.py # 核心引擎+ValueGate │ ├── storage.py # 持久化 │ └── mcp_server.py # MCP服务器 ├── src/ # TypeScript SDK │ ├── index.ts # 主入口 │ ├── cli.ts # CLI │ └── mcp-tools.ts # 工具定义 ├── skills/ # OpenClaw Skill │ └── self-evolving-skill/ # 技能封装 ├── MCP_CONFIG.md # MCP配置 └── README.md # 文档 ```

## MCP工具

| 工具 | 描述 | 参数 | |------|------|------| | `skill_create` | 创建Skill | `name`, `description` | | `skill_execute` | 执行并学习 | `skill_id`, `context`, `success`, `value` | | `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` |

## 使用方式

### CLI

```bash # 列出所有Skill openclaw skill self-evolving-skill list

# 创建Skill openclaw skill self-evolving-skill create --name "MySkill"

# 执行 openclaw skill self-evolving-skill execute <id> --success

# 分析 openclaw skill self-evolving-skill analyze --embedding '[0.1,0.2,...]'

# 统计 openclaw skill self-evolving-skill stats ```

### MCP服务器

```bash # 启动MCP服务器 cd ~/.openclaw/skills/self-evolving-skill ./run_mcp.sh

# 或使用适配器 python3 mcporter_adapter.py skill_list '{}' ```

### 编程

```typescript import { SelfEvolvingSkillEngine } from 'self-evolving-skill';

const engine = new SelfEvolvingSkillEngine(); await engine.init();

const { skillId } = await engine.createSkill({ name: 'Analyzer' }); const stats = await engine.stats(); ```

## 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 ) ```

## 文件位置

| 路径 | 说明 | |------|------| | `~/.openclaw/skills/self-evolving-skill` | 技能根目录 | | `~/.openclaw/mcp_servers/self-evolving-skill.json` | MCP服务器配置 | | `~/.openclaw/workspace/self-evolving-skill/storage` | 数据存储 |

## 相关文档

- [README.md](./README.md) - 完整文档 - [MCP_CONFIG.md](./MCP_CONFIG.md) - MCP配置说明 - [MEMORY.md](../MEMORY.md) - 研究笔记