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data-quality-frameworks

Automate data quality validation across ETL/ELT pipelines with Great Expectations, dbt tests, and data contracts.

1|Updated Apr 14, 2026
Search Tags:#validation#dbt#governance#pipelines#data-quality#great-expectations#data-contracts
One-click install
npx skills add https://github.com/Sumeet138/qwen-code-agents --skill data-quality-frameworks-sumeet138
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-quality-frameworks
Source: https://github.com/Sumeet138/qwen-code-agents/tree/main/plugins/data-engineering/skills/data-quality-frameworks
Command: npx skills add https://github.com/Sumeet138/qwen-code-agents --skill data-quality-frameworks-sumeet138

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Data engineering teams struggle to ensure data quality across pipelines. This skill provides automated validation via Great Expectations, dbt tests, and data contracts to catch quality issues before they impact analytics.

Core Features & Use Cases

  • Define and run data quality validations with GE expectations across datasets.
  • Build and execute dbt tests to enforce business rules and data consistency.
  • Create and enforce data contracts to govern schema, ownership, and data usage.

Quick Start

Run the data-quality-frameworks skill to initialize and validate your first data quality suite in a project.

Frequently Asked Questions about data-quality-frameworks

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

FAQPage Schema
How do I automate data quality validation across ETL pipelines?▼

Automate data quality validation across ETL pipelines by applying validation suites, dbt tests, and data contracts to enforce completeness, validity, and freshness before analytics are impacted.

What is a data contract and how does it govern schema usage?▼

A data contract governs schema, ownership, and data usage between teams. It enforces rules and expectations formally to prevent downstream analytics pipeline failures.

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

Yes, you can use dbt tests alongside Great Expectations suites to execute data quality checks, catching consistency and validity issues across datasets efficiently.

How do I enforce data freshness and completeness in ELT workflows?▼

Enforce data freshness and completeness in ELT workflows by defining and running automated validation suites that check datasets against expected quality rules and governance contracts.

What is the best way to catch data quality issues before they impact analytics?▼

The best way to catch data quality issues before analytics impact is implementing automated validation via Great Expectations, dbt tests, and formal data contracts across engineering pipelines.

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