Data Quality Frameworks
This gives you production-ready data quality validation using Great Expectations, dbt tests, and data contracts. You get specific expectation suites for completeness, uniqueness, and range validation, plus automated checkpoints that can fail your pipeline when data quality drops. The patterns cover everything from basic null checks to statistical validation and cross-table relationships. Honestly, the Great Expectations setup is a bit verbose, but once configured it catches data issues before they hit production dashboards. Most useful when you need bulletproof data pipelines and can't afford bad data making it to stakeholders.
- skills.sh installs
- 11.7K
- GitHub stars
- 42.4K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add wshobson/agents --skill data-quality-frameworks --agent claude-codeInstalls into .claude/skills of the current project.
Files
Data Quality Frameworks
Production patterns for implementing data quality with Great Expectations, dbt tests, and data contracts to ensure reliable data pipelines.
When to Use This Skill
- 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
Core Concepts
1. Data Quality Dimensions
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- Repository
- github.com/wshobson/agents
- Category
- Code Review & Quality
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