Cursor 版自进化技能：把会话中的有效经验沉淀为可复用知识。支持记忆体检、规则晋升、技能提取与显式记忆写入。适用场景：复盘近期改动、把高频模式升级为规则、把排障方案抽成技能、治理记忆冗余与过期内容。
Cursor 版自进化技能：把会话中的有效经验沉淀为可复用知识。支持记忆体检、规则晋升、技能提取与显式记忆写入。适用场景：复盘近期改动、把高频模式升级为规则、把排障方案抽成技能、治理记忆冗余与过期内容。
npx skills add https://github.com/wuhonglei/chat-agent --skill self-improving-agentこのコマンドをClaude Codeにコピー＆ペーストしてスキルをインストール
Tools for building modern React webapps with TypeScript, Tailwind CSS and shadcn/ui. Best suited for applications with complex UI components and state management.
Use this skill when the user requests to generate, create, imagine, or visualize images including characters, scenes, products, or any visual content. Supports structured prompts and reference images for guided generation.
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
Deploy applications and websites to Vercel. Use this skill when the user requests deployment actions such as "Deploy my app", "Deploy this to production", "Create a preview deployment", "Deploy and give me the link", or "Push this live". No authentication required - returns preview URL and claimable deployment link.
Creates a GitHub pull request with base branch main using the GitHub CLI (gh), and builds the PR title and body from commit messages between main and the current branch HEAD. Use when opening a PR to main, using gh pr create, or when the user wants a PR description derived from commits since main.
| name | self-improving-agent |
| description | Cursor 版自进化技能：把会话中的有效经验沉淀为可复用知识。支持记忆体检、规则晋升、技能提取与显式记忆写入。适用场景：复盘近期改动、把高频模式升级为规则、把排障方案抽成技能、治理记忆冗余与过期内容。 |
Capture -> Curate -> Enforce -> Reuse
本技能用于 Cursor 编辑器中的 Agent 工作流：将临时经验持续沉淀为长期有效的工程知识，并把高价值方案转化为规则或独立技能。
满足任一条件时应主动触发本技能：
在 Cursor 项目中按短期到长期管理知识：
.cursorrules 或团队约定文件）SKILL.md + 示例 + 参考文档 形式沉淀输入：
检查维度：
输出：
准入标准（全部满足）：
落地要求：
当某方案具备可复用步骤、明确输入输出、常见陷阱时，提取为独立技能：
SKILL.md：用途、触发条件、步骤、验收标准references/：关键背景资料或链接examples/：至少 1 个正例和 1 个边界例仅在以下情况写入：
同一条经验应放在哪里：
每次执行本技能后，至少确认：
/si:* 指令体系name: "self-improving-agent" description: "Curate Claude Code's auto-memory into durable project knowledge. Analyze MEMORY.md for patterns, promote proven learnings to CLAUDE.md and .claude/rules/, extract recurring solutions into reusable skills. Use when: (1) reviewing what Claude has learned about your project, (2) graduating a pattern from notes to enforced rules, (3) turning a debugging solution into a skill, (4) checking memory health and capacity."
Auto-memory captures. This plugin curates.
Claude Code's auto-memory (v2.1.32+) automatically records project patterns, debugging insights, and your preferences in MEMORY.md. This plugin adds the intelligence layer: it analyzes what Claude has learned, promotes proven patterns into project rules, and extracts recurring solutions into reusable skills.
| Command | What it does |
|---|---|
/si:review | Analyze MEMORY.md — find promotion candidates, stale entries, consolidation opportunities |
/si:promote | Graduate a pattern from MEMORY.md → CLAUDE.md or .claude/rules/ |
/si:extract | Turn a proven pattern into a standalone skill |
/si:status | Memory health dashboard — line counts, topic files, recommendations |
/si:remember | Explicitly save important knowledge to auto-memory |
┌─────────────────────────────────────────────────────────┐
│ Claude Code Memory Stack │
├─────────────┬──────────────────┬────────────────────────┤
│ CLAUDE.md │ Auto Memory │ Session Memory │
│ (you write)│ (Claude writes)│ (Claude writes) │
│ Rules & │ MEMORY.md │ Conversation logs │
│ standards │ + topic files │ + continuity │
│ Full load │ First 200 lines│ Contextual load │
├─────────────┴──────────────────┴────────────────────────┤
│ ↑ /si:promote ↑ /si:review │
│ Self-Improving Agent (this plugin) │
│ ↓ /si:extract ↓ /si:remember │
├─────────────────────────────────────────────────────────┤
│ .claude/rules/ │ New Skills │ Error Logs │
│ (scoped rules) │ (extracted) │ (auto-captured)│
└─────────────────────────────────────────────────────────┘
/plugin marketplace add alirezarezvani/claude-skills
/plugin install self-improving-agent@claude-code-skills
clawhub install self-improving-agent
./scripts/codex-install.sh --skill self-improving-agent
| File | Who writes | Scope | Loaded |
|---|---|---|---|
./.cursorrules | You (+ promote step) | Project rules | Full file, every session |
./.cursor/rules/*.md | You (+ promote step) | Scoped rules | When matching files are in context |
./.agents/skills/**/SKILL.md | You/Agent (extract step) | Reusable workflows | On demand when skill is invoked |
| Session context (Cursor Agent) | Agent runtime | Temporary learnings | Current conversation only |
./docs/knowledge/*.md (optional) | You/Agent | Team-shared durable notes | On demand |
1. Claude discovers pattern → auto-memory (MEMORY.md)
2. Pattern recurs 2-3x → /si:review flags it as promotion candidate
3. You approve → /si:promote graduates it to CLAUDE.md or rules/
4. Pattern becomes an enforced rule, not just a note
5. MEMORY.md entry removed → frees space for new learnings
Auto-memory is excellent at recording what Claude learns. But it has no judgment about:
That's what this plugin does.
When you promote a learning, it moves from Claude's scratchpad (MEMORY.md) to your project's rule system (CLAUDE.md or .claude/rules/). The difference matters:
Promoted rules have higher priority and load in full (not truncated at 200 lines).
Not everything belongs in CLAUDE.md. Use .claude/rules/ for patterns that only apply to specific file types:
# .claude/rules/api-testing.md
---
paths:
- "src/api/**/*.test.ts"
- "tests/api/**/*"
---
- Use supertest for API endpoint testing
- Mock external services with msw
- Always test error responses, not just happy paths
This loads only when Claude works with API test files — zero overhead otherwise.
Analyzes MEMORY.md and topic files to identify:
Takes a proven pattern and generates a complete skill:
/plugin install or clawhub publishMonitors command output for errors. When detected, appends a structured entry to auto-memory with:
Token overhead: Zero on success. ~30 tokens only when an error is detected.
| Platform | Memory System | Plugin Works? |
|---|---|---|
| Claude Code | Auto-memory (MEMORY.md) | ✅ Full support |
| OpenClaw | workspace/MEMORY.md | ✅ Adapted (reads workspace memory) |
| Codex CLI | AGENTS.md | ✅ Adapted (reads AGENTS.md patterns) |
| GitHub Copilot | .github/copilot-instructions.md | ⚠️ Manual promotion only |