Claude-trading-skills 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.
install
source · Clone the upstream repo
git clone https://github.com/tradermonty/claude-trading-skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/tradermonty/claude-trading-skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/skill-idea-miner" ~/.claude/skills/tradermonty-claude-trading-skills-skill-idea-miner && rm -rf "$T"
manifest:
skills/skill-idea-miner/SKILL.mdsource content
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
packagepyyaml - Claude CLI installed and authenticated (
to verify)claude --version - Session logs in
(created automatically by Claude Code)~/.claude/projects/<project>/ - 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
- Enumerate session logs from allowlist projects in
~/.claude/projects/ - Filter to past 7 days by file mtime, confirm with
fieldtimestamp - Extract user messages (
,type: "user"
)userType: "external" - Extract tool usage patterns from assistant messages
- Run deterministic signal detection:
- Skill usage frequency (
path references)skills/*/ - Error patterns (non-zero exit codes,
flags, exception keywords)is_error - Repetitive tool sequences (3+ tools repeated 3+ times)
- Automation request keywords (English and Japanese)
- Unresolved requests (5+ minute gap after user message)
- Skill usage frequency (
- Invoke Claude CLI headless for idea abstraction
- Output
raw_candidates.yaml
Stage 2: Scoring and Deduplication
- Load existing skills from
frontmatterskills/*/SKILL.md - Deduplicate via Jaccard similarity (threshold > 0.5) against:
- Existing skill names and descriptions
- Existing backlog ideas
- 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
- 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
— Signal detection criteria and scoring rubricreferences/idea_extraction_rubric.md
— Session log parserscripts/mine_session_logs.py
— Scorer and deduplicatorscripts/score_ideas.py