continuous-learning
Automatically extract reusable patterns from Claude Code sessions and save them as learned skills for future use.
Skill by Luohaothu
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
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What's inside
- Skill name
continuous-learning- Description
- Automatically extract reusable patterns from Claude Code sessions and save them as learned skills for future use.
- License
- MIT
- Instructions
- 107 lines in SKILL.md · view on GitHub
Agent Skills spec issues
- body: contains an absolute path from the author's machine
Files (1)
- SKILL.md
SKILL.md (first lines)
# 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:
1. **工作階段評估**:檢查工作階段是否有足夠訊息(預設:10+ 則)
2. **模式偵測**:從工作階段識別可提取的模式
3. **技能提取**:將有用模式儲存到 `~/.claude/skills/learned/`
## 設定
編輯 `config.json` 以自訂:
```json
{
"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`:
```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](https://x.com/affaanmustafa/status/2014040193557471352) - 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
1. **基於本能的學習** - 較小的原子行為,帶信心評分
2. **背景觀察者** - Haiku agent 並行分析
3. **信心衰減** - 如果被矛盾則本能失去信心
4. **領域標記** - code-style、testing、git、debugging 等
5. **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。From Luohaothu/everything-codex, MIT.
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Signals
- Stars
- 26
- Forks
- 5
- Open issues
- 0
- Last commit (this folder)
- Jan 29, 2026 (9 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/Luohaothu/everything-codex
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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continuous-learning skill. Agent Mart. https://agentmart.dev/skills/luohaothu-continuous-learning-daca2f (last updated 2026-10-24)