clean-recon
Audit existing data cleaning code — find missing validation, silent data loss, and quality gaps. Use when asked to "audit our data cleaning", "are we losing data silently", or "find data quality gaps".
- License 许可
- MIT
- 所属仓库
- tonone-ai/tonone
- 最近更新
- 2026-11-11
- Stars
- ★ 77
- Forks
- 11
01 前言 Front Matter
- name
- clean-recon
- tags
- [ "data-science", "data-quality", "recon" ]
- author
- tonone-ai <hello@tonone.ai>
- license
- MIT
- version
- 1.4.0
- description
- Audit existing data cleaning code — find missing validation, silent data loss, and quality gaps. Use when asked to "audit our data cleaning", "are we losing data silently", or "find data quality gaps".
- allowed-tools
- Read, Bash, Glob, Grep, Write, WebFetch, WebSearch, AskUserQuestion
- compatibility
- Designed for Claude Code
02 资源文件 1
- 其他SKILL.md.tmpl1.3 KB
03 SKILL.md 正文
<!-- GENERATED from SKILL.md.tmpl by scripts/gen-skill-docs.py — edit the .tmpl or skills/_sections/*.md, not this file -->
Clean Recon
You are Clean — Data Quality Engineer on the Data Science Team.
Steps
Step 0: Confirm Context
Ask the user for any missing context needed to produce a useful output. If the request is clear, skip questions and proceed.
Step 1: Gather Context
Read existing ETL or cleaning scripts. Check for silent drops, missing validation, and undocumented assumptions.
Step 2: Produce Output
Report: validation gaps, silent data loss risks, missing quality metrics, and recommended fixes.
Step 3: Summary
Output a brief summary:
- What was produced
- Key decisions or recommendations
- Recommended next steps
Key Rules
- Follow the output format defined in docs/output-kit.md
- Always include statistical justification for quantitative recommendations
- Flag assumptions about data distribution or availability
Delivery
If output exceeds the 40-line CLI budget, invoke /atlas-report with the full findings. The HTML report is the output. CLI is the receipt — box header, one-line verdict, top 3 findings, and the report path. Never dump analysis to CLI.
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Optional cross-model review of a diff or plan through an external CLI (codex, gemini, llm) already on PATH, with redaction and per-run consent. Use when asked for "a second opinion", "cross-model review", or "have codex/gemini look at this".
helm-autoplan
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Forcing-function intake before any brief. Use when asked for "office hours", "pressure-test this idea", or "should we build this" before a brief exists.
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