npx skills add https://github.com/openclaw/skills
jackjin1997/clawforge
Self-Evolving Skill
Meta-cognitive self-learning system - Automated skill evolution based on predictive coding and value-driven mechanisms.
Installation
$
npx skills add jackjin1997/clawforge --skill self-evolving-skill
Similar popular skills
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Also in this package
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npx skills add jackjin1997/clawforge
More details
Agent compatibility
Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.
Claude Code
Not declared
Cursor
Not declared
Codex
Not declared
GitHub Copilot
Not declared
Windsurf
Not declared
Gemini CLI
Not declared
Cline
Not declared
OpenCode
Not declared
Also listed on
Alternate registries and mirrors of this skill.
Repository health
Stars
12
Default branch
main
Open issues
1
Status
Active
Package contents
Files included with this skill beyond the listing page.
-
skill md
SKILL.md4,024 B -
docs
SUMMARY.md143 B
History
- First recorded snapshot · 313 installs
SKILL.md
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
-: 残差 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
# 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
# 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
# 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
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
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
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
- [MCPCONFIG.md](./MCPCONFIG.md) - MCP配置说明
- [MEMORY.md](../MEMORY.md) - 研究笔记