Continuous Learning Skill

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

When to Activate

Setting up automatic pattern extraction from Claude Code sessions
Configuring the Stop hook for session evaluation
Reviewing or curating learned skills in ~/.claude/skills/learned/
Adjusting extraction thresholds or pattern categories
Comparing v1 (this) vs v2 (instinct-based) approaches

How It Works

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

Session Evaluation: Checks if session has enough messages (default: 10+)
Pattern Detection: Identifies extractable patterns from the session
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

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

Comparison Notes (Research: Jan 2025)

vs 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

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

See: docs/continuous-learning-v2-spec.md for full spec.

continuous-learning