Continuous Learning

Continuous Learning Skill

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

When to Activate

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

Status

This v1 skill is still supported, but continuous-learning-v2 is the preferred path for new installs. Keep v1 when you explicitly want the simpler Stop-hook extraction flow or need compatibility with older learned-skill workflows.

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 ~/.gemini/skills/learned/

Configuration

Edit config.json to customize:

{
  "min_session_length": 10,
  "extraction_threshold": "medium",
  "auto_approve": false,
  "learned_skills_path": "~/.gemini/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

Hook Setup

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

{
  "hooks": {
    "Stop": [{
      "matcher": "*",
      "hooks": [{
        "type": "command",
        "command": "~/.gemini/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

• Felipe Marzochi - 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:

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

Source: Fmarzochi/EGC — distributed by TomeVault.