data-quality-frameworks

Name: data-quality-frameworks
Availability: InStock
Author: drgaciw

Implements data quality validation pipelines with Great Expectations, dbt tests, and data contracts.

Updated Dec 23, 2025

Search Tags:#validation #data quality #dbt #data pipelines #data contracts #great expectations

Ready to run

npx skills add https://github.com/drgaciw/academic-compliance-hub-glm --skill data-quality-frameworks-drgaciw

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-quality-frameworks
Source: https://github.com/drgaciw/academic-compliance-hub-glm/tree/main/agents/plugins/data-engineering/skills/data-quality-frameworks
Command: npx skills add https://github.com/drgaciw/academic-compliance-hub-glm --skill data-quality-frameworks-drgaciw

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the critical need for reliable data pipelines by implementing comprehensive data quality validation using industry-standard frameworks. It prevents bad data from propagating through your systems, ensuring trustworthy analytics and operations.

Core Features & Use Cases

Data Validation: Implement checks for completeness, uniqueness, validity, accuracy, consistency, and timeliness.
Framework Integration: Utilize Great Expectations for defining and running expectations, dbt tests for data modeling validation, and data contracts for clear agreements on data structure and quality.
Use Case: Automatically validate that incoming customer data meets defined schema requirements, contains no duplicate email addresses, and that critical fields like created_at are within an acceptable freshness window before loading into the data warehouse.

Quick Start

Use the data-quality-frameworks skill to set up Great Expectations validation for the orders table.

Frequently Asked Questions about data-quality-frameworks

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I set up data validation pipelines to ensure data integrity in my warehouse?▼

Data validation pipelines ensure data integrity by implementing automated checks for completeness, uniqueness, and validity using Great Expectations, dbt tests, and data contracts before loading data into your warehouse.

What is the best way to enforce data contracts and schema requirements for incoming data?▼

The best way to enforce data contracts is by defining clear agreements on data structure and quality, utilizing dbt tests for data modeling validation and schema enforcement to prevent bad data from propagating.

Can I use Great Expectations and dbt tests together for data quality checks?▼

Yes, you can use Great Expectations to define and run expectations alongside dbt tests for data modeling validation, creating a comprehensive framework that ensures data reliability and consistency across your data assets.

How do I automatically validate customer data for duplicate emails and freshness before loading?▼

Automatically validate incoming customer data by configuring validation rules that check for duplicate email addresses, enforce schema requirements, and verify that critical fields like created_at are within an acceptable freshness window.

When do I need automated validation rules in my data pipelines?▼

You need automated validation rules in your data pipelines when you must ensure data accuracy, consistency, and timeliness, preventing bad data from propagating through your systems and ensuring trustworthy analytics and operations.