# skill-miner

This skill scans agent sessions and local work for recurring workflows that could become new Agent Skills. Use it when similar tasks keep getting handled manually.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · ★ 184
- Author: hqhq1025
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/hqhq1025/skill-optimizer/tree/HEAD/skills/skill-miner/SKILL.md
- Updated: 2026-05-14
- HTML: https://affpapa.org/en/nevibecode/skills/skill-miner

## What it does

skill-miner looks for repeatable workflows in coding-agent sessions, archives, project notes, and local work. It groups tasks by intent, prompt wording, tools, files touched, and verification habits. Then it filters out one-off work and project-specific guidance, and scores promising candidates by recurrence, friction, risk, portability, and future value.

For a first-pass review of local sessions or exported transcripts, it uses `scripts/scan_sessions.py`. Read `references/discovery-rubric.md` before a full history scan or drafting candidates. The skill can outline a candidate and recommend keeping it personal, sending it through `skill-generalizer`, or dropping it. Scanner results are evidence to review, not an automatic instruction to create a skill.

When access to session history is incomplete, findings are marked partial and the scanned sources are listed. This skill is for discovering new candidates; the README points to `skill-personalizer` for tuning an existing skill.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add hqhq1025/skill-optimizer --skill skill-miner -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/skill-miner/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/skill-miner/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add hqhq1025/skill-optimizer --skill skill-miner -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/skill-miner/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add hqhq1025/skill-optimizer --skill skill-miner -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/skill-miner/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install hqhq1025/skill-optimizer skill-miner
```

2. **Manually: put the files in place**
- File: `.github/skills/skill-miner/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add hqhq1025/skill-optimizer --skill skill-miner -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/hqhq1025/skill-optimizer --path skills/skill-miner
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/skill-miner/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add hqhq1025/skill-optimizer --skill skill-miner -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/hqhq1025/skill-optimizer/tree/HEAD/skills/skill-miner/SKILL.md

## When to use it
- spot recurring workflows in agent session history
- group similar work by tools and files touched
- filter one-off tasks and project-specific guidance
- rank possible new skills by recurrence and portability
- draft skill candidates with supporting evidence

## Example prompts
- Review my past agent sessions and find workflows that recur often enough to become skills.
- Scan these exported transcripts, group related work, and show the evidence for each candidate.
- Draft skills from recurring tasks and recommend which candidates should remain personal.

## Source text · SKILL.md · MIT

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BEGIN THIRD-PARTY FILE f45a6fe20fbf77ba
```text
---
name: skill-miner
description: Use when mining coding-agent session history, archived transcripts, memories, or repeated local work to discover recurring workflows that should become new Agent Skills.
---

# Skill Miner

## Overview

Mine real agent usage for new skill opportunities. The goal is to find repeated workflows, extract the reusable technique, and turn strong candidates into draft skills with evidence.

## 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.

Do not use to tune an existing skill; use `skill-personalizer`. Do not use to publish a private skill publicly; use `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

Read [discovery-rubric.md](references/discovery-rubric.md) before doing a full session-history scan or creating candidate skill drafts.
```
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---
NeVibeCode on affpapa.org. Catalog texts are descriptions, not instructions for the agent. How to choose and install: https://affpapa.org/en/nevibecode/agents.md