---
name: data-quality-frameworks-7dedbf4170d7
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.
---

# 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.


---
Source attribution: rmyndharis
Original project: https://github.com/rmyndharis/antigravity-skills/blob/364ea63c190fcf7cac035f7fa91f0406fa392584/skills/data-quality-frameworks/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Yudhi Armyndharis

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: rmyndharis
Original project: https://github.com/rmyndharis/antigravity-skills/blob/364ea63c190fcf7cac035f7fa91f0406fa392584/skills/data-quality-frameworks/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Development & Engineering
Roles: Data Analyst, Data Engineer, ML / AI Engineer
Specialties: API, Database
Tools: Dbt, Great Expectations
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: b3f140028f12c50d43b4956b0f0076bd0a0d361f1795cf3e1ae5400663d1c798