Clean Recon
SkillDev toolsAudit 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".
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Clean Recon skill
What this skill tells your AI
The instructions your AI receives, as published by tonone-ai/tonone in skills/clean-recon/SKILL.md and read by ahel’s review.
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.
Signals
- GitHub stars
- 71
- Forks
- 9
- Last commit
- Aug 2026
Advanced
- Catalog kind
- skill
- Gateway key
clean-recon- Source
- github.com/tonone-ai/tonone