data-quality-frameworks
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.
Best use case
data-quality-frameworks is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using data-quality-frameworks should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/data-quality-frameworks/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How data-quality-frameworks Compares
| Feature / Agent | data-quality-frameworks | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# 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. ## 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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