Skill Idea Miner

Mine Claude Code session logs for skill idea candidates. Use when running the skill generation pipeline to extract, score, and backlog new skill ideas from recent coding sessions.

No API Required

Download Skill Package (.skill) View Source on GitHub

Table of Contents

1. Overview

Skill Idea Miner

2. Prerequisites

  • API Key: None required
  • Python 3.9+ recommended

3. Quick Start

Stage 1: Session Log Mining

  1. Enumerate session logs from project directories 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)

4. How It Works

Stage 1: Session Log Mining

  1. Enumerate session logs from project directories 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

See the skill’s SKILL.md for the full end-to-end workflow.

5. Usage Examples

  • Weekly automated pipeline run for skill idea generation
  • Manual backlog refresh: python3 scripts/mine_session_logs.py
  • Dry-run to preview candidates without LLM scoring

6. Understanding the Output

raw_candidates.yaml 

generated_at_utc: "2026-03-08T06:00:00Z"
lookback_days: 7
sessions_analyzed: 12
candidates:
  - id: "raw_20260308_001"
    title: "Project Charter Generator"
    scanned_projects: ["PycharmProjects-claude-skills-library"]
    description: "Generate project charters from requirements."
    category: "project-management"

Backlog (logs/.skill_generation_backlog.yaml)

```yaml updated_at_utc: “2026-03-08T06:15:00Z”

The full output details are documented in SKILL.md.

7. Tips & Best Practices

  • Begin with the smallest realistic sample input so you can validate the workflow before scaling up.
  • Keep skills/skill-idea-miner/SKILL.md open while working; it remains the authoritative source for the full procedure.
  • Review the most relevant reference files first instead of scanning every guide: idea_extraction_rubric.md.
  • Run helper scripts on test data before using them on final assets or production-bound inputs: mine_session_logs.py, score_ideas.py.
  • Preserve intermediate outputs so you can explain assumptions, diffs, and follow-up actions clearly.

8. Combining with Other Skills

  • Combine this skill with adjacent skills in the same category when the work spans planning, implementation, and review.
  • Browse the broader category for neighboring workflows: category index.
  • Use the English skill catalog when you need to chain this workflow into a larger end-to-end process.

9. Troubleshooting

  • Re-check prerequisites first: missing runtime dependencies and unsupported file formats are the most common failures.
  • If a helper script is involved, run it with a minimal sample input before applying it to a full dataset or repository.
  • Compare your input shape against the reference files to confirm expected fields, sections, or metadata are present.
  • Confirm the expected Python version and required packages are installed in the active environment.
  • When output looks incomplete, inspect the script arguments and rerun with explicit input/output paths.

10. Reference

References:

  • skills/skill-idea-miner/references/idea_extraction_rubric.md

Scripts:

  • skills/skill-idea-miner/scripts/mine_session_logs.py
  • skills/skill-idea-miner/scripts/score_ideas.py