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About data-quality-frameworks
data-quality-frameworks
Implement data quality validation
data-quality-frameworks is an agent skill from the wshobson/agents collection for building data quality validation into data pipelines. It covers three tools and approaches: Great Expectations for validating datasets against expectations, dbt tests for checking transformed models, and data contracts that set agreed rules between the teams that produce and consume data. The skill begins with the core dimensions of data quality, which give the checks a shared vocabulary, and then gives patterns for building the checks into a pipeline so bad data is caught before it reaches reports or models. Its uses include implementing data quality checks in pipelines, setting up Great Expectations validation, building comprehensive dbt test suites, establishing data contracts between teams, monitoring quality metrics over time, and running data validation automatically in CI/CD. It is written for data engineers and analytics engineers who want quality rules to live in code and run on every change rather than be checked by hand. The skill is installed with the skills CLI from the wshobson/agents repository. Its skills.sh page showed about 11.0K installs and passing results from three security audits at the time of the scan.
- Price
- Free
- Made by
- wshobson
- Category
- AI Skills
- Website
- github.com/wshobson/agents/data-quality-frameworks