Automatically extract reusable patterns from Claude Code sessions and save them as learned skills for future use.
Automatically extract reusable patterns from Claude Code sessions and save them as learned skills for future use.
npx skills add https://github.com/affaan-m/ECC --skill continuous-learning复制此命令并粘贴到 Claude Code 中以安装该技能
React 18/19 patterns including hooks discipline, server/client component boundaries, Suspense + error boundaries, form actions, data fetching, state management decision trees, and accessibility-first composition. Use when writing or reviewing React components.
React and Next.js performance optimization patterns adapted from Vercel Engineering's React Best Practices (https://github.com/vercel-labs/agent-skills). Organizes 70+ rules across 8 priority categories — waterfalls, bundle size, server-side, client fetching, re-render, rendering, JS micro-perf, advanced. Use when writing, reviewing, or refactoring React/Next.js code for performance.
React component testing with React Testing Library, Vitest/Jest, MSW for network mocking, accessibility assertions with axe, and the decision boundary between component tests and Playwright/Cypress end-to-end runs. Use when writing or fixing tests for React components, hooks, or pages.
Agent-driven scheduling and publishing of social media posts across 13 platforms via SocialClaw. Use when the user wants to publish to X, LinkedIn, Instagram, Facebook Pages, TikTok, Discord, Telegram, YouTube, Reddit, WordPress, or Pinterest — or when managing campaigns, uploading media, or monitoring post delivery status.
End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches.
Accessibility patterns for React and Next.js — semantic HTML, ARIA attributes, form labeling, keyboard navigation, focus management, and screen reader support. Use when building any interactive UI component or form.
| name | continuous-learning |
| description | Automatically extract reusable patterns from Claude Code sessions and save them as learned skills for future use. |
自動評估 Claude Code 工作階段結束時的內容，提取可重用模式並儲存為學習技能。
此技能作為 Stop hook 在每個工作階段結束時執行：
~/.claude/skills/learned/編輯 config.json 以自訂：
{
"min_session_length": 10,
"extraction_threshold": "medium",
"auto_approve": false,
"learned_skills_path": "~/.claude/skills/learned/",
"patterns_to_detect": [
"error_resolution",
"user_corrections",
"workarounds",
"debugging_techniques",
"project_specific"
],
"ignore_patterns": [
"simple_typos",
"one_time_fixes",
"external_api_issues"
]
}
| 模式 | 描述 |
|---|---|
error_resolution | 特定錯誤如何被解決 |
user_corrections | 來自使用者修正的模式 |
workarounds | 框架/函式庫怪異問題的解決方案 |
debugging_techniques | 有效的除錯方法 |
project_specific | 專案特定慣例 |
新增到你的 ~/.claude/settings.json：
{
"hooks": {
"Stop": [{
"matcher": "*",
"hooks": [{
"type": "command",
"command": "~/.claude/skills/continuous-learning/evaluate-session.sh"
}]
}]
}
}
/learn 指令 - 工作階段中手動提取模式Homunculus v2 採用更複雜的方法：
| 功能 | 我們的方法 | Homunculus v2 |
|---|---|---|
| 觀察 | Stop hook（工作階段結束） | PreToolUse/PostToolUse hooks（100% 可靠） |
| 分析 | 主要上下文 | 背景 agent（Haiku） |
| 粒度 | 完整技能 | 原子「本能」 |
| 信心 | 無 | 0.3-0.9 加權 |
| 演化 | 直接到技能 | 本能 → 聚類 → 技能/指令/agent |
| 分享 | 無 | 匯出/匯入本能 |
來自 homunculus 的關鍵見解：
"v1 依賴技能進行觀察。技能是機率性的——它們觸發約 50-80% 的時間。v2 使用 hooks 進行觀察（100% 可靠），並以本能作為學習行為的原子單位。"
參見：docs/continuous-learning-v2-spec.md 完整規格。