Individual skill
data-quality-checks
Design the data quality checks for a table or pipeline across the standard dimensions. Use when asked to add data quality tests, define DQ checks, catch bad data before it hits dashboards, or set up monitoring for a dataset. Produces a checks plan across completeness, validity, uniqueness, freshness, consistency, and accuracy — each with the rule, severity, and where it runs (dbt test / Great Expectations / SQL assertion).
plugins/pm-dataeng/skills/data-quality-checks/SKILL.md
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- Last checked
- Nov 9, 2026
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- Nov 10, 2026
- License
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# 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.