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Data Quality Audit Checklist

Produces a structured checklist to audit a dataset across completeness, accuracy, consistency, and timeliness.

VettedUpdated June 2026
The prompt
Build a data quality audit checklist for {{dataset}} with columns {{columns}}. Cover completeness, accuracy, consistency, uniqueness, validity, and timeliness. For each dimension give a concrete check and what a failure looks like. Use context: {{use_context}}.
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Variables

datasetDataset
columnsColumns
use_contextUse context

Example output

Dataset: customer_orders (order_id, customer_id, order_date, amount, status). Completeness: no nulls in order_id or amount — failure: blank amounts. Accuracy: amount matches line-item sum — failure: totals that do not reconcile. Consistency: status uses one fixed vocabulary — failure: Shipped and shipped both present. Uniqueness: order_id has no duplicates — failure: repeated IDs. Validity: order_date is a real date not in the future — failure: 2099 dates. Timeliness: rows load within 24 hours — failure: stale partitions.

Details

Author

AI Khazna

License

—

Security

Vetted

Type

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