Skill idea miner

Skill BaggaT236/AI-Trading-Skills/skills/skill-idea-miner

Ai agent claude trading skills for disiplined, repeatable trading workflows with a modern typescript platform

Install

npx -y skills add BaggaT236/AI-Trading-Skills --skill skill-idea-miner

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What its author says it does

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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.md

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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

Enumerate session logs from allowlist projects in ~/.claude/projects/
Filter to past 7 days by file mtime, confirm with timestamp field
Extract user messages (type: "user", userType: "external")
Extract tool usage patterns from assistant messages
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)
Invoke Claude CLI headless for idea abstraction
Output raw_candidates.yaml

Stage 2: Scoring and Deduplication

Load existing skills from skills/*/SKILL.md frontmatter
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

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