Files
ECC/skills/continuous-learning/SKILL.md
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d29cf651c7 fix(skills): declare activation triggers in descriptions and normalize version metadata (#2618)
* fix(skills): move version into metadata and normalize to semver

29 skills declared `version` at the top level of their frontmatter. The
schema reads it from `metadata`, so tooling that follows the schema either
misses it or has to special-case the top level.

Three motion skills also declared `version: 1.0`, which is not a valid
semantic version; normalized to `1.0.0`.

No behavioral change — frontmatter metadata only.

* fix(skills): state activation triggers in skill descriptions

148 skills described what they cover but never named the situation that
should trigger them. Since the description is what Claude matches against
to decide whether to load a skill, a description without a trigger makes
activation guesswork — the skill is either missed or loaded at the wrong
time.

Added a "Use when ..." clause to each, derived from the skill's own body
(most already stated the trigger under "## When to Use" or in the opening
line; that intent is now reflected in the frontmatter where it is actually
read from).

Descriptions were only appended to; no existing wording was removed.

* fix(skills): sync activation triggers into the Codex skill mirror

10 of the skills whose descriptions changed are also mirrored under
`.agents/skills/`, where the description was previously a verbatim copy.
Left alone, the two surfaces would disagree about when the skill applies.

Only the description line is synced; the Codex copies keep their reduced
frontmatter, since that validator accepts only name, description,
metadata, license, and allowed-tools.

* fix(skills): correct three activation clauses from review

- autonomous-loops: the clause pulled new loop work into a skill that its
  own body marks as a compatibility shim retained for one release. It now
  points at the canonical continuous-agent-loop instead.
- continuous-learning: the description carried the v1 routing directive
  twice; collapsed to one.
- homelab-pihole-dns: the clause fired on any broken home DNS. Narrowed to
  tasks that actually involve Pi-hole.

* chore: retain current main lockfile

---------

Co-authored-by: Çağrı Solakoğlu <cagri.solakoglu@vtcenerji.com>
Co-authored-by: haelyra <49814733+haelyra@users.noreply.github.com>
2026-08-11 23:58:14 -04:00

4.4 KiB

name, description, metadata
name description metadata
continuous-learning [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.
origin
ECC

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
  • 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.