mcpField
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data-quality-frameworks

data-quality-frameworks

github.com/rmyndharisData & databases1.9k43 installsUpdated 2w ago
Repository

About data-quality-frameworks

A skill for implementing production data quality checks with Great Expectations, dbt tests, and data contracts. It guides teams through validation design, CI/CD automation, monitoring, and remediation.

Key features

  1. 01Great Expectations validation
  2. 02Comprehensive dbt test suites
  3. 03Data contract definition
  4. 04CI/CD validation automation
  5. 05Quality monitoring and alerts
  6. 06Ownership and remediation planning

Use cases

  1. 01Add quality checks to data pipelines
  2. 02Set up Great Expectations validation
  3. 03Define contracts between data teams
  4. 04Automate data validation in CI/CD

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/rmyndharis/antigravity-skills --skill data-quality-frameworks

After installing, type/data-quality-frameworksin Claude Code to confirm it loaded.

Bundled filesresources/implementation-playbook.md

Details

From the project README

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.

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