Individual skill

analyze-data-quality

Investigate whether structured datasets and query results are trustworthy enough to use. Use for underlying data-quality risks such as freshness, grain, missingness, duplicates, broken joins, schema drift, and conflicting source results.

OpenAI/Codex/plugins/data-analytics/skills/analyze-data-quality/SKILL.md

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SKILL.mdcore · 13,188 bytes
SKILL.mdcore · 13,188 bytes
SKILL.mdcore · 13,188 bytes

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# Analyze Data Quality

Assess whether a dataset is trustworthy enough for analysis, modeling,
dashboards, experiments, or downstream pipelines. Start with the intended use and grain, run the highest-value checks for the data shape, and report concrete evidence, analytical risk, likely causes, and the smallest useful remediation or automated test.

## Overall Instructions

- Follow the [shared Data instructions](../../shared/shared-skill-instructions.md) throughout this workflow.

## Dependencies

Apply the shared [dependency resolution policy](../../shared/shared-skill-instructions.md#dependency-resolution) to the categories below.

- Data Warehouse: Raw records, schemas, and reference tables for grain, completeness, freshness, and reconciliation checks.