Agent skill

skill-idea-miner

Mine Claude Code session logs for skill idea candidates. Use when running the weekly skill generation pipeline to extract, score, and backlog new skill ideas from recent coding sessions.

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Forks 117

Install this agent skill to your Project

npx add-skill https://github.com/tradermonty/claude-trading-skills/tree/main/skills/skill-idea-miner

SKILL.md

Skill Idea Miner

Automatically extract skill idea candidates from Claude Code session logs, score them for novelty, feasibility, and trading value, and maintain a prioritized backlog for downstream skill generation.

When to Use

  • Weekly automated pipeline run (Saturday 06:00 via launchd)
  • Manual backlog refresh: python3 scripts/run_skill_generation_pipeline.py --mode weekly
  • Dry-run to preview candidates without LLM scoring

Workflow

Stage 1: Session Log Mining

  1. Enumerate session logs from allowlist projects in ~/.claude/projects/
  2. Filter to past 7 days by file mtime, confirm with timestamp field
  3. Extract user messages (type: "user", userType: "external")
  4. Extract tool usage patterns from assistant messages
  5. Run deterministic signal detection:
    • Skill usage frequency (skills/*/ path references)
    • Error patterns (non-zero exit codes, is_error flags, exception keywords)
    • Repetitive tool sequences (3+ tools repeated 3+ times)
    • Automation request keywords (English and Japanese)
    • Unresolved requests (5+ minute gap after user message)
  6. Invoke Claude CLI headless for idea abstraction
  7. Output raw_candidates.yaml

Stage 2: Scoring and Deduplication

  1. Load existing skills from skills/*/SKILL.md frontmatter
  2. Deduplicate via Jaccard similarity (threshold > 0.5) against:
    • Existing skill names and descriptions
    • Existing backlog ideas
  3. Score non-duplicate candidates with Claude CLI:
    • Novelty (0-100): differentiation from existing skills
    • Feasibility (0-100): technical implementability
    • Trading Value (0-100): practical value for investors/traders
    • Composite = 0.3 * Novelty + 0.3 * Feasibility + 0.4 * Trading Value
  4. Merge scored candidates into logs/.skill_generation_backlog.yaml

Output Format

raw_candidates.yaml

yaml
generated_at_utc: "2026-03-08T06:00:00Z"
period: {from: "2026-03-01", to: "2026-03-07"}
projects_scanned: ["claude-trading-skills"]
sessions_scanned: 12
candidates:
  - id: "raw_2026w10_001"
    title: "Earnings Whispers Image Parser"
    source_project: "claude-trading-skills"
    evidence:
      user_requests: ["Extract earnings dates from screenshot"]
      pain_points: ["Manual image reading"]
      frequency: 3
    raw_description: "Parse Earnings Whispers screenshots to extract dates."
    category: "data-extraction"

Backlog (logs/.skill_generation_backlog.yaml)

yaml
updated_at_utc: "2026-03-08T06:15:00Z"
ideas:
  - id: "idea_2026w10_001"
    title: "Earnings Whispers Image Parser"
    description: "Skill that parses Earnings Whispers screenshots..."
    category: "data-extraction"
    scores: {novelty: 75, feasibility: 60, trading_value: 80, composite: 73}
    status: "pending"

Resources

  • references/idea_extraction_rubric.md — Signal detection criteria and scoring rubric
  • scripts/mine_session_logs.py — Session log parser
  • scripts/score_ideas.py — Scorer and deduplicator

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