/skill-idea-miner

name: skill-idea-miner
description: 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.

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

Prerequisites

• **Python 3.10+** with `pyyaml` package
• **Claude CLI** installed and authenticated (`claude --version` to verify)
• **Session logs** in `~/.claude/projects/<project>/` (created automatically by Claude Code)
• No API keys required (uses Claude CLI for LLM calls)

Workflow

Quick Start

# Dry-run: preview mined candidates without LLM scoring
python3 scripts/mine_session_logs.py --dry-run --output-dir reports/

# Full mining with scoring (requires Claude CLI)
python3 scripts/mine_session_logs.py --output-dir reports/

# Score existing candidates
python3 scripts/score_ideas.py \
  --candidates reports/raw_candidates.yaml \
  --output-dir logs/

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