Data Quality Agent
SkillDatabases & dataDetect duplicates, missing values, schema drift, and inconsistencies across datasets. Use before migrations, reporting, or model training.
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 Data Quality Agent skill
What this skill tells your AI
The instructions your AI receives, as published by navinspire-ia/navin in navin/skills/data-quality-agent/SKILL.md and read by ahel’s review.
Overview
Profile first, fix second. Quantify issues.
Checks
- Null rates / required fields
- Duplicate business keys
- Type / format violations
- Referential integrity orphans
- Distribution spikes / drift vs baseline
Leads studio CSV (when applicable)
For sales/prospects-*.csv also verify:
- Required columns: company, website, source, confidence
- Valid website URLs; prefer source URLs over free-text when claiming public evidence
- No duplicate domains; confidence in {high, medium, low, unverified}
- No email marked verified without enrichment proof
- Prefer running
lead-qualification/scripts/score_leads.py --validate-onlythen full score
Workflow
- Identify datasets and grain (what is one row).
- Profile columns; compute issue counts.
- Prioritize by blast radius (joins, finance, PII, outbound lists).
- Propose remediations; apply only with approval on prod data.
- Leave a short DQ report with metrics.
Signals
- GitHub stars
- 22
- Forks
- 4
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
data-quality-agent- Source
- github.com/navinspire-ia/navin