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Skills
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Continuous Learning

affaan-m/everything-claude-code

The continuous-learning skill automatically evaluates Claude Code sessions at their conclusion to identify and extract reusable patterns—such as error resolutions, debugging techniques, and workarounds—saving them as learned skills for future reference. It's designed for Claude Code users who want to build a personalized knowledge base of patterns from their own work without manual curation. The skill solves the problem of losing valuable problem-solving approaches across sessions by making them discoverable and reusable through a configurable Stop hook that runs at the end of each session.

installs
5.9K
GitHub stars
229.1K
last updated
Jul 14, 2026
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Install

$ npx -y skills add affaan-m/everything-claude-code --skill continuous-learning --agent claude-code

Installs into .claude/skills of the current project.

Files

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.

SKILL.md on GitHub

Sponsors

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Details

Repository
github.com/affaan-m/everything-claude-code

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