data-quality

Installation
SKILL.md

Data Quality

Use this skill when the user needs trustworthy datasets, not just successful pipeline runs.

Default stance

  • Prevent bad data early when possible
  • Validate transformed outputs before publishing them broadly
  • Make metric definitions reviewable and owned
  • Combine warehouse constraints with pipeline-level tests

Working approach

  1. Identify what failure would break user trust: missing rows, wrong metric values, schema drift, stale data, or invalid business logic.
  2. Decide whether the rule belongs in:
    • source/ingestion validation
    • storage constraints
    • transformation tests
    • governance and approval workflow
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4
GitHub Stars
4
First Seen
Apr 23, 2026