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.
By tradermonty · 1,939 installs
npx skills add tradermonty/claude-trading-skills --skill skill-idea-miner
Source repository · Upstream listing
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
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
Backlog (logs/.skill generation backlog.yaml)
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