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Data Quality Agent

SkillDatabases & data

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

Use Data Quality Agent in Claude, ChatGPT or Ahel Desktop

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Also: Claude Code · Cursor · Codex

Then ask your AI: use the Data Quality Agent skill

Details

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add Ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Data Quality AgentStart free

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-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.

Signals

GitHub stars
36
Forks
4
Last commit
Oct 2026
Advanced
Item type
skill
Key
data-quality-agent
Source
github.com/navinspire-ia/navin

github.com/navinspire-ia/navin

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