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/continuous-learning

@90606c8
by Affaan Mustafaaffaan-m/ecc287k stars
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[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.

Use this Skill: https://skilld.dev/gh/affaan-m/ecc/continuous-learning

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SKILL.md

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Example settings and source

The example uses a 200k-token context and default Claude Code settings. The count includes the name, description, separators, and when_to_use when present. Codex also counts local file paths.

Skit's source and limits: Codex 0.160.1, Claude Code 2.1.292.

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

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

  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: SKILL.md on GitHub

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    The skill is a session evaluation utility that uses a bash script hook to identify extractable patterns from conversation transcripts.

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Signed by skilld at 90606c8. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 3 days ago.

Activeupdated 2 months ago
metadata
{
  "origin": "ECC"
}
  • continuous-learning
  • deprecated
  • session-analysis
  • pattern-extraction
  • skills
  • stop-hook
  • learning

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This skill is deprecated as of April 2026. Use continuous-learning-v2 instead, which replaces stop-hook observation with PreToolUse/PostToolUse hooks and adds instinct-based learning with confidence scoring and project scoping.

Generated from the current SKILL.md.

Is this skill still maintained?
No. This skill was deprecated on 2026-04-28. You should install continuous-learning-v2 instead, which uses more reliable hooks and adds confidence scoring and project-scoped learning.
What is the difference between v1 and v2?
v1 extracts patterns using a Stop hook at session end. v2 uses PreToolUse/PostToolUse hooks for 100% reliable observation, stores learned patterns as atomic instincts with confidence scores, and supports project-scoped storage in addition to global storage.
Should I migrate from v1 to v2?
Yes. v2 is a strict superset of v1 with better reliability and more granular learning. The skill documentation explicitly recommends routing all continuous learning requests to v2.

Generated from the current SKILL.md. These answers refresh after source changes.