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continuous-learning

Documents a legacy workflow for extracting reusable solutions from Claude Code sessions and saving them as learned skills. Useful for reviewing existing setups and extraction settings; this version is deprecated in favor of continuous-learning-v2.

OriginalShow the original description

[DEPRECATED - use continuous-learning-v2] Legacy v1 stop-hook skill extractor. v2 is a strict superset with instinct-based, project-scoped, hook-reliable learning. Do not invoke v1: when continuous learning, session learning, or pattern extraction is requested, route to continuous-learning-v2 instead.

AI rating70/ 100See how to install

Good for

  • Reviewing legacy session-end extraction setups
  • Adjusting extraction thresholds and pattern categories
  • Reviewing saved learned skills
  • Comparing the v1 and v2 learning approaches

Overview

What it does

continuous-learning documents a legacy workflow that evaluates Claude Code conversations when a session ends and saves reusable solutions as skills. It covers error resolution, user corrections, workarounds for library quirks, debugging techniques, and project conventions. A Stop hook starts the process at session end. By default, sessions need at least 10 messages to qualify for evaluation.

When it helps

Use it as a reference when checking settings in an existing installation or reviewing previously saved learning. In config.json, you can adjust extraction criteria, pattern categories, and the save location. The documented settings also exclude patterns such as simple typos and one-time fixes. It helps compare how the legacy version and its successor observe activity and divide what they save into reusable units.

Example requests

  • “Check the extraction settings used by my existing Stop hook.”
  • “Suggest settings for revising which patterns to extract and exclude.”
  • “Review my saved learned skills and explain the differences from v2.”

Notes

This version was deprecated on April 28, 2026, in favor of continuous-learning-v2. The material remains for archival reference and compatibility with existing installations. Its configuration examples assume Claude Code and save learned skills to ~/.claude/skills/learned/.

This overview was written by AI (OpenAI Codex) from the public SKILL.md. Check the original below for the exact behavior.

Objective evaluation

AI rated this skill on four points using only the contents of its SKILL.md. Stars and popularity are not taken into account.

Clear instructionsIs it clear when to use it and what it does?
BPartly lackingSession-end extraction is clear, but deprecation instructions coexist with guidance to continue using v1.
Concrete stepsAre steps, inputs, outputs, and examples spelled out?
AGoodEvaluation, detection, and saving steps are provided, with config.json and Stop hook examples.
SafetyAre sending data, deleting files, and running commands disclosed?
BPartly lackingThe script processes full transcripts and names a save location, but the review procedure before saving is unspecified.
Easy setupDoes it need few tools or API keys, and are they documented?
BPartly lackingClaude Code hook configuration is shown, but installation steps for evaluate-session.sh are absent.

The total counts each point as A = 25, B = 15, C = 5. Rated by AI (OpenAI Codex) as of Oct 19, 2026. Treat it as a guide and read the original before installing.

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Files included (3)

  • SKILL.md4.4 KB
  • config.json391 B
  • evaluate-session.sh2.3 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Continuous Learning Skill - DEPRECATED

DEPRECATED 2026-04-28. Use continuous-learning-v2 instead. v2 is a strict superset: stop-hook observation becomes PreToolUse/PostToolUse observation, full skills become atomic instincts with confidence scoring, and global-only storage becomes project-scoped plus global promotion.

This file is kept for archival reference and backward compatibility with existing installs.


Original v1 Documentation (archival)

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

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 ~/.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

PatternDescription
error_resolutionHow specific errors were resolved
user_correctionsPatterns from user corrections
workaroundsSolutions to framework/library quirks
debugging_techniquesEffective debugging approaches
project_specificProject-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:

FeatureOur ApproachHomunculus v2
ObservationStop hook (end of session)PreToolUse/PostToolUse hooks (100% reliable)
AnalysisMain contextBackground agent (Haiku)
GranularityFull skillsAtomic "instincts"
ConfidenceNone0.3-0.9 weighted
EvolutionDirect to skillInstincts → cluster → skill/command/agent
SharingNoneExport/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: docs/continuous-learning-v2-spec.md for full spec.

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