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。
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionself-evolving-skillExecute the skills CLI command in your project's root directory to begin installation:
Fetches self-evolving-skill from jackjin1997/clawforge and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate self-evolving-skill. Access via /self-evolving-skill in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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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。
# 技能已安装到 ~/.openclaw/skills/self-evolving-skill
# 或使用ClawHub
clawhub install self-evolving-skill
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
| 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 |
# 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
# 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 '{}'
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();
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
| 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 |
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
)
| 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 |
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
self-evolving-skill reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend self-evolving-skill for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in self-evolving-skill — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
self-evolving-skill fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for self-evolving-skill matched our evaluation — installs cleanly and behaves as described in the markdown.
We added self-evolving-skill from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
self-evolving-skill fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added self-evolving-skill from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
self-evolving-skill reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend self-evolving-skill for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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