Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.
Start a fresh conversation. Replace the highlighted placeholders.
Use data-quality-frameworks to define validation tests, data contracts, ownership, alerting, and remediation for [defined data sources], with a fallback plan before any critical pipeline is blocked.
Required inputs
[defined data sources]
User-approved starter prompt based on a pinned source version; check current skill requirements before running. Task evidence ↗
SKILL.md
name: data-quality-frameworksdescription: Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.
Data Quality Frameworks
Production patterns for implementing data quality with Great Expectations, dbt tests, and data contracts to ensure reliable data pipelines.
Use this skill when
Implementing data quality checks in pipelines
Setting up Great Expectations validation
Building comprehensive dbt test suites
Establishing data contracts between teams
Monitoring data quality metrics
Automating data validation in CI/CD
Do not use this skill when
The data sources are undefined or unavailable
You cannot modify validation rules or schemas
The task is unrelated to data quality or contracts
Instructions
Identify critical datasets and quality dimensions.
Define expectations/tests and contract rules.
Automate validation in CI/CD and schedule checks.
Set alerting, ownership, and remediation steps.
If detailed patterns are required, open resources/implementation-playbook.md.
Safety
Avoid blocking critical pipelines without a fallback plan.
Handle sensitive data securely in validation outputs.
Resources
resources/implementation-playbook.md for detailed frameworks, templates, and examples.
data-quality-frameworks by rmyndharis · The Skill Leaderboard