data-quality-frameworks

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

Implement data quality validation across tables, columns, and pipelines.

Updated Apr 5, 2026

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

Ready to run

npx skills add https://github.com/Jhabbig/Habbig --skill data-quality-frameworks-jhabbig

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-quality-frameworks
Source: https://github.com/Jhabbig/Habbig/tree/main/.claude/plugins/wshobson/data-engineering/skills/data-quality-frameworks
Command: npx skills add https://github.com/Jhabbig/Habbig --skill data-quality-frameworks-jhabbig

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It helps teams catch bad data before it breaks analytics, dashboards, and downstream models by standardizing validation rules and quality gates.

Core Features & Use Cases

Validation Suites: Define clear checks for completeness, uniqueness, validity, consistency, and freshness.
Pipeline Safety: Run Great Expectations checkpoints or dbt tests to fail fast when critical data quality rules are violated.
Data Contracts: Document schema, ownership, and service-level expectations so producers and consumers stay aligned.
Use Case: A warehouse team can validate an orders table every day, alert on missing keys or stale records, and block releases when contract rules are broken.

Quick Start

Ask the skill to design a data quality framework for your dataset using Great Expectations, dbt tests, and a data contract.

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 quality checks with Great Expectations and dbt tests?▼

You create data quality validation suites by defining schema checks, uniqueness, nullability, and freshness rules for your tables and columns. The framework applies these assertions to Great Expectations checkpoints and dbt tests to fail pipelines fast when critical data quality rules are broken.

What is a data contract and how does it improve pipeline monitoring?▼

A data contract documents schema, ownership, and service-level expectations to keep producers and consumers aligned. It improves pipeline monitoring by standardizing validation rules and quality gates, ensuring bad data is caught before it breaks downstream analytics and dashboards.

Can I use this data quality framework to validate warehouse tables for stale records?▼

Yes, you can validate warehouse tables for stale records by applying freshness rules and threshold validation. The framework standardizes daily checks for completeness and staleness, alerting teams when records are missing or outdated to maintain production reliability.

When do I need data contracts and quality gates in analytics workflows?▼

You need data contracts and quality gates when analytics workflows require guaranteed completeness, uniqueness, and freshness. They prevent bad data from breaking dashboards by standardizing validation rules, blocking releases when contract expectations are broken, and aligning producers with consumers.

What is the best way to block bad data from breaking downstream models?▼

The best way to block bad data is implementing validation suites with Great Expectations checkpoints and dbt tests. You define strict schema checks, nullability assertions, and threshold validations that fail fast, blocking releases and alerting teams when critical data quality rules are violated.

Does this data quality validation approach support both column and table-level assertions?▼

Yes, this data quality validation approach supports both column and table-level assertions. It applies schema checks, uniqueness, nullability, and threshold validation across tables, columns, and pipelines, providing comprehensive coverage for analytics and warehouse workflows.