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持續學習技能
continuous-learning
Automatically extract reusable patterns from Claude Code sessions and save them as learned skills for future use.
SKILL.md
Full skill instructions
持續學習技能
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。
運作方式
此技能作為 Stop hook 在每個工作階段結束時執行：
- 工作階段評估：檢查工作階段是否有足夠訊息（預設：10+ 則）
- 模式偵測：從工作階段識別可提取的模式
- 技能提取：將有用模式儲存到
~/.claude/skills/learned/
設定
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"
]
}
模式類型
| 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 |
Hook 設定
新增到你的 ~/.claude/settings.json：
{
"hooks": {
"Stop": [{
"matcher": "*",
"hooks": [{
"type": "command",
"command": "~/.claude/skills/continuous-learning/evaluate-session.sh"
}]
}]
}
}
為什麼用 Stop Hook？
- 輕量：工作階段結束時只執行一次
- 非阻塞：不會為每則訊息增加延遲
- 完整上下文：可存取完整工作階段記錄
相關
- Longform Guide - 持續學習章節
/learn指令 - 工作階段中手動提取模式
比較筆記（研究：2025 年 1 月）
vs Homunculus (github.com/humanplane/homunculus)
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。"
潛在 v2 增強
- 基於本能的學習 - 較小的原子行為，帶信心評分
- 背景觀察者 - Haiku agent 並行分析
- 信心衰減 - 如果被矛盾則本能失去信心
- 領域標記 - code-style、testing、git、debugging 等
- 演化路徑 - 將相關本能聚類為技能/指令
參見：/Users/affoon/Documents/tasks/12-continuous-learning-v2.md 完整規格。
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