continuous-learning

$2a

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:

• 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

Related

• 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.