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.

Safety Notice

This listing is imported from skills.sh public index metadata. Review upstream SKILL.md and repository scripts before running.

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Install skill "skill-idea-miner" with this command: npx skills add tradermonty/claude-trading-skills/tradermonty-claude-trading-skills-skill-idea-miner

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

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)

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

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

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