.claude/skills/continuous-learning/SKILL.md
Automatically extract reusable patterns from Claude Code sessions and save them as learned skills for future use.
npx skillsauth add fox3000wang/my-claude continuous-learningInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Automatically evaluates Claude Code sessions on end to extract reusable patterns that can be saved as learned skills.
~/.claude/skills/learned/This skill runs as a Stop hook at the end of each session:
~/.claude/skills/learned/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 | 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 |
Add to your ~/.claude/settings.json:
{
"hooks": {
"Stop": [{
"matcher": "*",
"hooks": [{
"type": "command",
"command": "~/.claude/skills/continuous-learning/evaluate-session.sh"
}]
}]
}
}
/learn command - Manual pattern extraction mid-sessionHomunculus 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."
See: docs/continuous-learning-v2-spec.md for full spec.
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