# Conversation To Skill Miner

> Mine conversation archives, topic memory, idea inboxes, and project notes for reusable agent-skill candidates, then classify which should be packaged, reviewed, or kept private. Use when Codex is asked to search chats/conversations for possible skills, turn repeated workflows into skills, run a weekly skill-mining review, or prepare a safe public skill backlog from private conversation history.

- Skill: `walterkken/conversation-to-skill-miner` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds@latest add walterkken/conversation-to-skill-miner`
- Raw SKILL.md: https://api.skillmd.com/api/skills/walterkken/conversation-to-skill-miner/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Product & Planning
- Author: walterkken (https://skillmd.com/u/walterkken)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/walterkken/conversation-to-skill-miner

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# Conversation To Skill Miner

## Overview

Convert messy conversation history into a safe skill backlog. The goal is not to
publish every idea; it is to identify repeatable workflows, remove private or
high-risk material, and package only the candidates that can be useful without
leaking personal context.

Read `references/publishability-rubric.md` when deciding whether a candidate can
be published.

## Workflow

1. Define the scan boundary.
   - Prefer durable archives: `conversation-memory/`, `conversation-archive/`,
     `daily-ideas/`, project READMEs, and existing local skill folders.
   - Do not scan private messaging exports, browser profiles, secrets, or raw
     logs unless the user explicitly asks and the output will remain private.

2. Find repeated workflows.
   - Look for phrases such as `skill`, `workflow`, `pipeline`, `SOP`, `agent`,
     `继续`, `整理`, `归档`, `自动化`, `weekly`, `GitHub`, `PPT`, `paper`,
     `Simulink`, and `screen`.
   - Prefer workflows that have been requested more than once or already have
     artifacts, scripts, reports, or topic-memory entries.

3. Classify each candidate.
   - `publish_now`: generic, safe, repeatable, and already proven by at least
     one real task.
   - `review_first`: promising but may contain personal data, domain risk,
     weak examples, missing validation, or unclear audience.
   - `keep_private`: depends on private contacts, credentials, sensitive
     profiling, personal health/finance decisions, or private chat content.

4. Package only safe candidates.
   - Strip raw conversation text and personal examples.
   - Keep general workflows, safety boundaries, trigger phrases, and reusable
     scripts.
   - Add references only when they are public, lawful, and one level deep from
     `SKILL.md`.

5. Validate and report.
   - Run `quick_validate.py` for each skill.
   - Produce a concise report with candidate name, source pointer, decision,
     risk, next packaging action, and recommended repository.
   - Keep generated reports local unless they are manually sanitized.

## Script

Use the bundled scanner for a first pass:

```bash
python3 scripts/scan_conversations_for_skills.py \
  --workspace /path/to/Codex \
  --output /path/to/weekly-skill-candidates.md
```

The script outputs a markdown report and a sibling JSON file. Treat its result
as a triage aid; the agent still makes final publishability decisions.

## Weekly Review Rule

For weekly operation, schedule only the scan/report step automatically. Do not
auto-publish directly from private conversations. Public upload should happen
after an explicit review pass that confirms no secrets, raw chats, private
profiles, or copyrighted material are included.