continuous-learning

📁 affaan-m/everything-claude-code 📅 Jan 22, 2026
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430
周安装量
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全站排名
安装命令
npx skills add https://github.com/affaan-m/everything-claude-code --skill continuous-learning

Agent 安装分布

claude-code 342
opencode 301
gemini-cli 273
antigravity 217
cursor 208

Skill 文档

Continuous Learning Skill

Automatically evaluates Claude Code sessions on end to extract reusable patterns that can be saved as learned skills.

How It Works

This skill runs as a Stop hook at the end of each session:

  1. Session Evaluation: Checks if session has enough messages (default: 10+)
  2. Pattern Detection: Identifies extractable patterns from the session
  3. Skill Extraction: Saves useful patterns to ~/.claude/skills/learned/

Configuration

Edit config.json to customize:

{
  "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"
  ]
}

Pattern Types

Pattern Description
error_resolution How specific errors were resolved
user_corrections Patterns from user corrections
workarounds Solutions to framework/library quirks
debugging_techniques Effective debugging approaches
project_specific Project-specific conventions

Hook Setup

Add to your ~/.claude/settings.json:

{
  "hooks": {
    "Stop": [{
      "matcher": "*",
      "hooks": [{
        "type": "command",
        "command": "~/.claude/skills/continuous-learning/evaluate-session.sh"
      }]
    }]
  }
}

Why Stop Hook?

  • Lightweight: Runs once at session end
  • Non-blocking: Doesn’t add latency to every message
  • Complete context: Has access to full session transcript

Related

  • The Longform Guide – Section on continuous learning
  • /learn command – Manual pattern extraction mid-session

Comparison Notes (Research: Jan 2025)

vs Homunculus (github.com/humanplane/homunculus)

Homunculus v2 takes a more sophisticated approach:

Feature Our Approach Homunculus v2
Observation Stop hook (end of session) PreToolUse/PostToolUse hooks (100% reliable)
Analysis Main context Background agent (Haiku)
Granularity Full skills Atomic “instincts”
Confidence None 0.3-0.9 weighted
Evolution Direct to skill Instincts → cluster → skill/command/agent
Sharing None Export/import instincts

Key insight from homunculus:

“v1 relied on skills to observe. Skills are probabilistic—they fire ~50-80% of the time. v2 uses hooks for observation (100% reliable) and instincts as the atomic unit of learned behavior.”

Potential v2 Enhancements

  1. Instinct-based learning – Smaller, atomic behaviors with confidence scoring
  2. Background observer – Haiku agent analyzing in parallel
  3. Confidence decay – Instincts lose confidence if contradicted
  4. Domain tagging – code-style, testing, git, debugging, etc.
  5. Evolution path – Cluster related instincts into skills/commands

See: /Users/affoon/Documents/tasks/12-continuous-learning-v2.md for full spec.