49 lines
1.7 KiB
Markdown
49 lines
1.7 KiB
Markdown
---
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name: data-quality-frameworks
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description: "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."
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risk: unknown
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source: community
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date_added: "2026-02-27"
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---
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# Data Quality Frameworks
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Production patterns for implementing data quality with Great Expectations, dbt tests, and data contracts to ensure reliable data pipelines.
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## Use this skill when
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- Implementing data quality checks in pipelines
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- Setting up Great Expectations validation
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- Building comprehensive dbt test suites
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- Establishing data contracts between teams
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- Monitoring data quality metrics
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- Automating data validation in CI/CD
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## Do not use this skill when
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- The data sources are undefined or unavailable
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- You cannot modify validation rules or schemas
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- The task is unrelated to data quality or contracts
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## Instructions
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- Identify critical datasets and quality dimensions.
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- Define expectations/tests and contract rules.
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- Automate validation in CI/CD and schedule checks.
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- Set alerting, ownership, and remediation steps.
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- If detailed patterns are required, open `resources/implementation-playbook.md`.
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## Safety
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- Avoid blocking critical pipelines without a fallback plan.
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- Handle sensitive data securely in validation outputs.
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## Resources
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- `resources/implementation-playbook.md` for detailed frameworks, templates, and examples.
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## Limitations
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- Use this skill only when the task clearly matches the scope described above.
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- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
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- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
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