data-quality-frameworks

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

Implement data quality validation frameworks using Great Expectations, dbt tests, and data contracts.

4|Updated Jan 10, 2026

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

Ready to run

npx skills add https://github.com/EngineerWithAI/engineerwith-agents --skill data-quality-frameworks-engineerwithai

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-quality-frameworks
Source: https://github.com/EngineerWithAI/engineerwith-agents/tree/main/plugins/data-engineering/skills/data-quality-frameworks
Command: npx skills add https://github.com/EngineerWithAI/engineerwith-agents --skill data-quality-frameworks-engineerwithai

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the critical need for reliable data by implementing comprehensive validation strategies, preventing data corruption and ensuring trustworthy insights.

Core Features & Use Cases

Data Validation: Implement checks using Great Expectations, dbt tests, and data contracts.
Pipeline Integration: Seamlessly integrate data quality checks into your data pipelines.
Use Case: When building a new data pipeline for customer orders, use this Skill to automatically validate that order IDs are unique, customer IDs are not null, and order statuses are within the expected set of values before the data is used for reporting.

Quick Start

Use the data-quality-frameworks skill to implement Great Expectations validation for the 'orders' table using the 'orders_suite' expectation suite.

Frequently Asked Questions about data-quality-frameworks

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

FAQPage Schema

How do I implement data quality validation in a data pipeline?▼

Data quality validation is implemented in data pipelines by integrating Great Expectations, dbt tests, and data contracts to automate integrity checks, enforce schemas, and detect anomalies before data reaches reporting.

What are data contracts and when do I need them for pipeline validation?▼

Data contracts establish strict validation agreements between upstream and downstream systems. You need them when defining schema enforcement rules to prevent data corruption and ensure trustworthy insights across inter-system pipelines.

How do I validate unique IDs and non-null constraints using dbt tests?▼

Validating unique IDs and non-null constraints is achieved by applying dbt tests to pipeline tables. This automatically verifies that critical fields like order IDs remain unique and customer IDs are not null during processing.

Can I use Great Expectations for anomaly detection and schema enforcement?▼

Great Expectations supports automated anomaly detection and schema enforcement by defining expectation suites. These suites validate that incoming data statuses match expected value sets before pipeline execution continues.

What is the best way to integrate automated data integrity checks into existing data pipelines?▼

The best way to integrate automated data integrity checks is by embedding validation frameworks directly into pipeline workflows, ensuring rules execute automatically against data loads to prevent corrupted records from advancing.

Do I need predefined validation rules to enforce data contracts between systems?▼

Yes, enforcing data contracts requires predefined validation rules. You must establish specific checks for schema enforcement and expected value sets to successfully validate data integrity between interacting systems.