Best fit
- Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents.
affaan-m/ECC
Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents.
npx skills add https://github.com/affaan-m/ECC --skill "docs/zh-TW/skills/continuous-learning-v2"Source checked Jul 28, 2026·Refresh due Oct 26, 2026
Reorganized from the pinned upstream SKILL.md
According to the pinned SKILL.md from affaan-m/ECC: 進階學習系統,透過原子「本能」(帶信心評分的小型學習行為)將你的 Claude Code 工作階段轉化為可重用知識。
npx skills add https://github.com/affaan-m/ECC --skill "docs/zh-TW/skills/continuous-learning-v2"Best fit
Bring this context
Expected outputs
Key source sections
Sections are extracted automatically from the pinned SKILL.md and link back to the source.
Review the “v2 的新功能” section in the pinned source before continuing.
yaml --- id: prefer-functional-style trigger: "when writing new functions" confidence: 0.7 domain: "code-style" source: "session-observation" ---
工作階段活動 │ │ Hooks 捕獲提示 + 工具使用(100% 可靠) ▼ ┌─────────────────────────────────────────┐ │ observations.jsonl │ │ (提示、工具呼叫、結果) │ └─────────────────────────────────────────┘ │ │ Observer agent 讀取(背景、Haiku) ▼ ┌─────────────────────────────────────────┐ │ 模式偵測 │ │ • 使用者修正 → 本能 │ │ • 錯誤解…
Review the “動作” section in the pinned source before continuing.
屬性: - 原子性 — 一個觸發器,一個動作 - 信心加權 — 0.3 = 試探性,0.9 = 近乎確定 - 領域標記 — code-style、testing、git、debugging、workflow 等 - 證據支持 — 追蹤建立它的觀察
SkillSignal prompt templates
These prompts were written by SkillSignal from the source structure; they are not upstream text.
Task-start prompt
Confirm source fit, inputs, and outputs before acting.
Use continuous-learning-v2 to help me with: [specific task]. Context: [files, data, or background]. Constraints: [environment, scope, and prohibited actions]. Before acting, check the pinned SKILL.md and explain which sections apply, what inputs are still missing, and what you will deliver.
Source-guided execution
Make the Agent explicitly follow the key extracted sections.
Apply the pinned continuous-learning-v2 source to [task]. Pay particular attention to these source sections: “v2 的新功能”, “本能模型”, “偏好函式風格”, “動作”, “證據”. Preserve the important decision at each step. Mark facts not covered by the source as “needs confirmation” instead of inventing them. Then verify the result against my acceptance criteria: [criteria].
Result-review prompt
Check omissions, permissions, and source drift before delivery.
Review the current continuous-learning-v2 result: (1) does it satisfy the original task; (2) were any applicable steps or limits in the pinned SKILL.md missed; (3) did it perform any unauthorized file, command, network, or data action; and (4) which conclusions remain unverified? List issues first, then fix only what the source or user authorization supports.
Output checklist
The task matches the purpose documented in the SKILL.md.
The source section “v2 的新功能” has been checked.
The source section “本能模型” has been checked.
The source section “偏好函式風格” has been checked.
The source section “動作” has been checked.
Inputs, constraints, and acceptance criteria are explicit.
Unverified facts, compatibility, and outcome claims are clearly marked.
Any file, command, network, or data action has been reviewed.
Choose a different workflow
Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents. v2.1 adds project-scoped instincts to prevent cross-project contamination.
A separate implementation from affaan-m/ECC; compare its source, maintenance signals, and permission requirements.
Open source detailHook'lar aracılığıyla oturumları gözlemleyen, güven skorlaması ile atomik instinct'ler oluşturan ve bunları skill/command/agent'lara evriltiren instinct tabanlı öğrenme sistemi. v2.1 çapraz proje kontaminasyonunu önlemek için proje kapsamlı instinct'ler ekler.
A separate implementation from affaan-m/ECC; compare its source, maintenance signals, and permission requirements.
Open source detail훅을 통해 세션을 관찰하고, 신뢰도 점수가 있는 원자적 본능을 생성하며, 이를 스킬/명령어/에이전트로 진화시키는 본능 기반 학습 시스템. v2.1에서는 프로젝트 간 오염을 방지하기 위한 프로젝트 범위 본능이 추가되었습니다.
A separate implementation from affaan-m/ECC; compare its source, maintenance signals, and permission requirements.
Open source detailFAQ
進階學習系統,透過原子「本能」(帶信心評分的小型學習行為)將你的 Claude Code 工作階段轉化為可重用知識。
The catalog detected this source-specific install command: npx skills add https://github.com/affaan-m/ECC --skill "docs/zh-TW/skills/continuous-learning-v2". Inspect the command and pinned source before running it.
No dedicated Agent platform is declared in the pinned source record.
Quality breakdown
Based on traceable docs and repository signals; stars are not treated as quality.
Compare before choosing
These links are selected from shared tasks, functions, stacks, platforms, and same-name variants. Compare the source owner, documentation, permissions, and maintenance signals.
Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents. v2.1 adds project-scoped instincts to prevent cross-project contamination.
Hook'lar aracılığıyla oturumları gözlemleyen, güven skorlaması ile atomik instinct'ler oluşturan ve bunları skill/command/agent'lara evriltiren instinct tabanlı öğrenme sistemi. v2.1 çapraz proje kontaminasyonunu önlemek için proje kapsamlı instinct'ler ekler.
훅을 통해 세션을 관찰하고, 신뢰도 점수가 있는 원자적 본능을 생성하며, 이를 스킬/명령어/에이전트로 진화시키는 본능 기반 학습 시스템. v2.1에서는 프로젝트 간 오염을 방지하기 위한 프로젝트 범위 본능이 추가되었습니다.
Sistema de aprendizaje basado en instintos que observa sesiones mediante hooks, crea instintos atómicos con puntuación de confianza y los evoluciona en skills/comandos/agentes. v2.1 agrega instintos con alcance de proyecto para prevenir contaminación entre proyectos.
基于本能的学习系统,通过钩子观察会话,创建带置信度评分的原子本能,并将其进化为技能/命令/代理。v2.1版本增加了项目范围的本能,以防止跨项目污染。
進階學習系統,透過原子「本能」(帶信心評分的小型學習行為)將你的 Claude Code 工作階段轉化為可重用知識。
| 功能 | v1 | v2 |
|---|---|---|
| 觀察 | Stop hook(工作階段結束) | PreToolUse/PostToolUse(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 |
本能是一個小型學習行為:
---
id: prefer-functional-style
trigger: "when writing new functions"
confidence: 0.7
domain: "code-style"
source: "session-observation"
---
# 偏好函式風格
## 動作
適當時使用函式模式而非類別。
## 證據
- 觀察到 5 次函式模式偏好
- 使用者在 2025-01-15 將基於類別的方法修正為函式
屬性:
工作階段活動
│
│ Hooks 捕獲提示 + 工具使用(100% 可靠)
▼
┌─────────────────────────────────────────┐
│ observations.jsonl │
│ (提示、工具呼叫、結果) │
└─────────────────────────────────────────┘
│
│ Observer agent 讀取(背景、Haiku)
▼
┌─────────────────────────────────────────┐
│ 模式偵測 │
│ • 使用者修正 → 本能 │
│ • 錯誤解決 → 本能 │
│ • 重複工作流程 → 本能 │
└─────────────────────────────────────────┘
│
│ 建立/更新
▼
┌─────────────────────────────────────────┐
│ instincts/personal/ │
│ • prefer-functional.md (0.7) │
│ • always-test-first.md (0.9) │
│ • use-zod-validation.md (0.6) │
└─────────────────────────────────────────┘
│
│ /evolve 聚類
▼
┌─────────────────────────────────────────┐
│ evolved/ │
│ • commands/new-feature.md │
│ • skills/testing-workflow.md │
│ • agents/refactor-specialist.md │
└─────────────────────────────────────────┘
如果作為外掛安裝(建議):
不需要在 ~/.claude/settings.json 中額外加入 hook。Claude Code v2.1+ 會自動載入外掛的 hooks/hooks.json,其中已經註冊了 observe.sh。
如果你之前把 observe.sh 複製到 ~/.claude/settings.json,請移除重複的 PreToolUse / PostToolUse 區塊。重複註冊會造成重複執行,並觸發 ${CLAUDE_PLUGIN_ROOT} 解析錯誤;這個變數只會在外掛自己的 hooks/hooks.json 中展開。
如果手動安裝到 ~/.claude/skills,新增到你的 ~/.claude/settings.json:
{
"hooks": {
"PreToolUse": [{
"matcher": "*",
"hooks": [{
"type": "command",
"command": "~/.claude/skills/continuous-learning-v2/hooks/observe.sh"
}]
}],
"PostToolUse": [{
"matcher": "*",
"hooks": [{
"type": "command",
"command": "~/.claude/skills/continuous-learning-v2/hooks/observe.sh"
}]
}]
}
}
mkdir -p ~/.claude/homunculus/{instincts/{personal,inherited},evolved/{agents,skills,commands}}
touch ~/.claude/homunculus/observations.jsonl
觀察者可以在背景執行並分析觀察:
# 啟動背景觀察者
~/.claude/skills/continuous-learning-v2/agents/start-observer.sh
| 指令 | 描述 |
|---|---|
/instinct-status | 顯示所有學習本能及其信心 |
/evolve | 將相關本能聚類為技能/指令 |
/instinct-export | 匯出本能以分享 |
/instinct-import <file> | 從他人匯入本能 |
編輯 config.json:
{
"version": "2.0",
"observation": {
"enabled": true,
"store_path": "~/.claude/homunculus/observations.jsonl",
"max_file_size_mb": 10,
"archive_after_days": 7
},
"instincts": {
"personal_path": "~/.claude/homunculus/instincts/personal/",
"inherited_path": "~/.claude/homunculus/instincts/inherited/",
"min_confidence": 0.3,
"auto_approve_threshold": 0.7,
"confidence_decay_rate": 0.05
},
"observer": {
"enabled": true,
"model": "haiku",
"run_interval_minutes": 5,
"patterns_to_detect": [
"user_corrections",
"error_resolutions",
"repeated_workflows",
"tool_preferences"
]
},
"evolution": {
"cluster_threshold": 3,
"evolved_path": "~/.claude/homunculus/evolved/"
}
}
~/.claude/homunculus/
├── identity.json # 你的個人資料、技術水平
├── observations.jsonl # 當前工作階段觀察
├── observations.archive/ # 已處理觀察
├── instincts/
│ ├── personal/ # 自動學習本能
│ └── inherited/ # 從他人匯入
└── evolved/
├── agents/ # 產生的專業 agents
├── skills/ # 產生的技能
└── commands/ # 產生的指令
當你使用 Skill Creator GitHub App 時,它現在產生兩者:
從倉庫分析的本能有 source: "repo-analysis" 並包含來源倉庫 URL。
信心隨時間演化:
| 分數 | 意義 | 行為 |
|---|---|---|
| 0.3 | 試探性 | 建議但不強制 |
| 0.5 | 中等 | 相關時應用 |
| 0.7 | 強烈 | 自動批准應用 |
| 0.9 | 近乎確定 | 核心行為 |
信心增加當:
信心減少當:
"v1 依賴技能進行觀察。技能是機率性的——它們根據 Claude 的判斷觸發約 50-80% 的時間。"
Hooks 100% 的時間確定性地觸發。這意味著:
v2 完全相容 v1:
~/.claude/skills/learned/ 技能仍可運作基於本能的學習:一次一個觀察,教導 Claude 你的模式。