About data-quality-frameworks
A skill for teams implementing data quality practices in pipelines. It provides guidance on Great Expectations, dbt tests, data contracts, validation automation, and monitoring.
Key features
- 01Great Expectations validation
- 02dbt test suite design
- 03Data contract rules
- 04CI/CD validation automation
- 05Quality monitoring and remediation
Use cases
- 01Add quality checks to pipelines
- 02Build dbt test suites
- 03Define contracts between teams
- 04Automate validation in CI/CD
Install
Copy the command and run it in a terminal.
$
npx skills add https:/ / github.com/ sickn33/ agentic-awesome-skills --skill data-quality-frameworksAfter installing, type/data-quality-frameworksin Claude Code to confirm it loaded.
Bundled filesresources/implementation-playbook.md
Details
From the project READMEData 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.mdfor detailed frameworks, templates, and examples.
Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
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