data-quality-audit

name	data-quality-audit
description	Audits database data quality (nulls, duplicates, orphans, invalid ranges) and produces a short findings report with remediation queries. Use when debugging data issues, validating migrations, or verifying analytics correctness.

Data Quality Audit

Quick Start

Goal: find correctness issues quickly and return a small, actionable report.

When to use this skill

• bug reports that smell like data drift (missing rows, double counts, weird nulls)
• after migrations/backfills to prove invariants still hold
• when analytics numbers disagree between systems

Before you run checks

• database engine
• target tables + expected primary keys
• expected invariants (unique, not null, fk relationships, allowed ranges)
• performance constraints (large tables, peak hours)

Workflow (default)

1. pick 1–3 core tables for the issue
2. run cheap checks first (nulls, duplicates on keys)
3. run relationship checks (orphans)
4. run domain checks (ranges, enums)
5. write a short findings report + safe remediation plan

Core checks (portable sql)

null checks

SELECT COUNT(*) AS null_count
FROM your_table
WHERE important_col IS NULL;

duplicates on a candidate key

SELECT key_col, COUNT(*) AS c
FROM your_table
GROUP BY key_col
HAVING COUNT(*) > 1
ORDER BY c DESC
LIMIT 50;

orphan rows (broken references)

SELECT COUNT(*) AS orphan_count
FROM child c
LEFT JOIN parent p ON p.id = c.parent_id
WHERE c.parent_id IS NOT NULL
  AND p.id IS NULL;

invalid ranges

SELECT COUNT(*) AS bad_count
FROM your_table
WHERE amount < 0;

time sanity (example)

SELECT COUNT(*) AS bad_count
FROM your_table
WHERE created_at > NOW();

Performance-safe tips

• always start with count(*) + targeted where clauses
• add limit when inspecting example rows
• scope by time window if tables are huge (last 7/30 days)
• prefer indexed predicates (id ranges, created_at) for sampling

Remediation patterns

fix duplicates

• decide on a canonical row rule (latest by updated_at, highest priority status, etc.)
• write a deterministic dedupe query
• add a unique constraint or unique index after cleanup

fix orphans

• pick policy: delete orphans, reattach to parent, or set fk to null
• add fk constraint after data is corrected

fix nulls

• backfill from source columns or defaults
• add not null only after verification

Output format (copy/paste)

## data quality audit

### scope

- tables:
- time window:

### findings

- [severity] issue: evidence

### likely impact

- user impact:
- analytics impact:

### remediation

- step 1:
- step 2:

### verification

- query checks: