# Data Quality Agent

> Detect duplicates, missing values, schema drift, and inconsistencies across datasets. Use before migrations, reporting, or model training.

- Skill: `navinspire-ia/data-quality-agent` (Agent Skill)
- Install (CLI): `npx skillmds@latest add navinspire-ia/data-quality-agent`
- Raw SKILL.md: https://api.skillmd.com/api/skills/navinspire-ia/data-quality-agent/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: Navinspire-ia (https://skillmd.com/u/navinspire-ia)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/navinspire-ia/data-quality-agent

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# Data Quality Agent

## 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-only` then full score

## Workflow

1. Identify datasets and grain (what is one row).
2. Profile columns; compute issue counts.
3. Prioritize by blast radius (joins, finance, PII, outbound lists).
4. Propose remediations; apply only with approval on prod data.
5. Leave a short DQ report with metrics.