Skill Miner

Overview

When To Use

• A user wants to scan past coding-agent sessions for repeated workflows.
• The user suspects they keep asking agents to do similar tasks manually.
• A team wants a backlog of candidate skills based on actual work rather than brainstorming.
• Existing memories, session logs, or project notes contain recurring procedures that have not been packaged.

skill-personalizer

skill-generalizer

Workflow

1. Locate real evidence: session JSONL, memory summaries, repo notes, repeated scripts, and recent project folders.
2. Run

   scripts/scan_sessions.py

   for a first-pass sanitized cluster report when local session files or exported transcripts are available.
3. Cluster repeated work by intent, trigger phrasing, tools used, files touched, and verification pattern.
4. Filter out one-off tasks, ordinary coding knowledge, and project-specific instructions better suited for

   AGENTS.md

   .
5. Score candidates by recurrence, friction, risk, portability, and future value.
6. For each strong candidate, draft a concise skill name, trigger description, workflow outline, bundled-resource needs, and validation prompts.
7. Recommend whether each candidate should stay personal, become a public skill via

   skill-generalizer

   , or be skipped.
8. If the user asks to proceed, create the selected skill folders and verify frontmatter/layout.

Evidence Rules

• Quote or summarize enough source evidence to justify each candidate.
• Do not expose sensitive transcript content unless the user explicitly asks for raw evidence.
• Avoid turning every repeated task into a skill; prefer workflows where guidance changes future behavior.
• Treat broad intent clusters as navigation hints, not skill drafts.
• Check sampled positives and near misses before trusting a regex-based workflow candidate.
• If session access is incomplete, label findings as partial and list what was scanned.

References

scripts/scan_sessions.py --help

--export

--patterns