Data Quality Checks Skill

Bad data quietly poisons dashboards and models until someone notices the number is wrong. The fix is checks that fail loudly before that — across the standard DQ dimensions. This skill designs them for a specific table/pipeline: the exact rule per dimension, its severity (block vs. warn), and where it runs (dbt test, Great Expectations, or a SQL assertion), so quality is enforced, not hoped for.

Required Inputs

Ask for these only if they aren't already provided:

• The table/pipeline and what it represents (grain, key columns).
• The columns that matter — keys, required fields, enums, ranges, dates.
• Freshness expectation — how current the data must be.
• Tooling — dbt tests, Great Expectations, Soda, or raw SQL assertions.

Output Format

Data Quality Checks: [table]