skill-discovery

skill-discovery v2.0.0

OpenClaw skill lifecycle toolkit — discover new skills from session patterns, then monitor them for degradation.

The Problem

Two things go wrong with agent skills:

1. You don't create them. You do the same thing 5 times before realizing "I should automate this."
2. They silently break. A skill that worked last month fails when models change, APIs shift, or codebases evolve.

This repo solves both.

What's New in v2

🧠 LCM as a Data Source

Before reading raw JSONL, the skill now queries LCM summaries first. LCM captures compacted history that raw session files lose after compaction — patterns from weeks ago are often only visible there. Uses lcm_grep and lcm_expand_query.

🔴 Operational Anomaly Detection

Three new checks run alongside pattern discovery:

• Camofox tab leaks — sessions that opened browser tabs without closing them
• Workspace file bloat — context files >15KB eating your token budget (MEMORY.md, AGENTS.md, etc.)
• Cron failure patterns — consecutive failures on the same job = broken automation that needs immediate attention

🤖 Subagent Delegation Gap Detection

Identifies sessions where the main agent did too much work inline: >15 tool calls + >3 different tool types + zero sessions_spawn = delegation miss. Flags them as refactor candidates to keep main session context lean.

🔗 Tool Sequence Pattern Matching

Looks for repeated tool-call sequences across sessions (same 3+ tool types in same order, appearing 3+ times). Example: web_fetch → exec → message send repeated 4x = a research-and-report workflow worth scripting into a dedicated skill.

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What's Included

🔍 Skill Discovery (SKILL.md)

Analyzes your OpenClaw session history to find:

• Repeated task patterns → New skill candidates
• Re-learned lessons → Rules for AGENTS.md
• Multi-step workflows → Automation opportunities
• Cross-agent struggles → Infrastructure improvements
• Operational anomalies → Leaks, bloat, broken crons
• Delegation gaps → Tasks that should have used subagents

Works as a behavioral skill — no scripts, just session analysis patterns your agent follows.

📊 Skill Health Tracker (skill-health.py)

Lightweight invocation logger that tracks which skills are failing and whether they're getting worse:

# Log results
./skill-health.py log github success
./skill-health.py log github failure --error "gh auth expired"

# Full report
./skill-health.py report

# Degrading skills only (for cron use)
./skill-health.py check

Example output:

Skill                Invocations  Fail Rate        Trend  Last Error
--------------------------------------------------------------------------------
github                        12       25%  ! degrading  gh auth token expired
weather                        8        0%       stable
todoist                        5       20%  * improving  API timeout

Install

cd ~/.openclaw/skills
git clone https://github.com/tuncer-deniz/skill-discovery
chmod +x skill-discovery/skill-health.py

Custom workspace path: If your OpenClaw data lives somewhere other than ~/.openclaw, set CLAWD_WORKSPACE:

export CLAWD_WORKSPACE=/path/to/your/workspace

The skill's session-find commands respect this variable.

Skill Discovery Usage

Manual

Analyze my sessions from the last 7 days and identify skill candidates.

Weekly Cron (Recommended)

{
  "name": "weekly-skill-discovery",
  "schedule": { "kind": "cron", "expr": "0 10 * * 0", "tz": "America/Edmonton" },
  "payload": {
    "kind": "agentTurn",
    "message": "Run skill discovery analysis on sessions from the last 7 days. Report findings with recommendations."
  },
  "sessionTarget": "isolated"
}

Skill Health Usage

Log invocations from your scripts

if some_command; then
    skill-health.py log my-skill success
else
    skill-health.py log my-skill failure --error "$(cat /tmp/last-error.txt)"
fi

Weekly degradation check

{
  "name": "weekly-skill-health-check",
  "schedule": { "kind": "cron", "expr": "0 9 * * 1" },
  "payload": {
    "kind": "agentTurn",
    "message": "Run skill-health.py check. Report any degrading skills."
  },
  "sessionTarget": "isolated"
}

Configuration

The Lifecycle

Discovery → Creation → Monitoring → Improvement
    ↑                                     |
    └─────────────────────────────────────┘

1. Discovery finds patterns worth automating
2. skill-creator scaffolds the new skill
3. Health tracker monitors it in production
4. When health degrades → discovery flags it for review

Scoring Guide (Discovery)

Real Discoveries

Patterns that became skills from actual usage:

Requirements

• OpenClaw
• Python 3.8+ (skill-health.py, no external deps)

Inspired By

The observe → amend → evaluate loop from cognee-skills — stripped down to what most agent setups actually need.

License

MIT

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A skill that helps you build more skills, then makes sure they keep working.