# Data Quality Frameworks

> <!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT -->

- Skill: `frank-luongt/data-quality-frameworks` (Agent Skill)
- Install (CLI): `npx skillmds@latest add frank-luongt/data-quality-frameworks`
- Raw SKILL.md: https://api.skillmd.com/api/skills/frank-luongt/data-quality-frameworks/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: frank-luongt (https://skillmd.com/u/frank-luongt)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/frank-luongt/data-quality-frameworks

---

<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT -->
---
name: data-quality-frameworks
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.
tags: [data, data-engineering]
---

# 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.

## Safety

- Avoid blocking critical pipelines without a fallback plan.
- Handle sensitive data securely in validation outputs.

## Resources


<!-- Source: .faos/custom/skills/data/data-quality-frameworks/SKILL.md -->