Which data validation tools does this skill support?

This skill provides production patterns for Great Expectations for deep Python-based validation, dbt for SQL-based model testing, and SodaCL or YAML-based standards for data contracts.

Can I use this for real-time data monitoring?

Yes, it includes patterns for Great Expectations checkpoints and Slack notification actions that can be triggered within orchestration tools to provide real-time quality alerts.

How do Data Contracts work within this skill?

The skill provides a standardized YAML-based contract format that defines schemas, quality requirements, and SLAs, serving as a 'source of truth' between data providers and consumers.

Does this support custom dbt tests?

Absolutely. It includes templates for both generic dbt tests for reusable logic and singular tests for specific, complex business rule validation.

Data Quality Frameworks

Name: Data Quality Frameworks
Author: EngineerWithAI

byEngineerWithAI

0•

データベース管理

Implements production-grade data validation and observability using Great Expectations, dbt tests, and formal data contracts.

This skill empowers developers to build reliable data pipelines by integrating industry-standard validation frameworks into their workflow. It provides structured implementation patterns for Great Expectations suites, dbt test configurations, and the establishment of formal data contracts to manage inter-team dependencies. By automating validation across the data testing pyramid—covering schema, unit, and integration tests—this skill ensures data integrity, freshness, and consistency while streamlining CI/CD workflows and pipeline observability.

主な機能

01Formal data contract definitions for schema and SLA enforcement

02Comprehensive coverage of data quality dimensions like completeness and validity

03Advanced dbt test patterns including generic, singular, and custom SQL tests

04Automated Great Expectations suite and checkpoint configuration

05Integration for automated quality notifications and documentation updates

060 GitHub stars

ユースケース

01Establishing formal Data Contracts between engineering and analytics teams to ensure schema stability.

02Automating data validation in CI/CD pipelines to prevent upstream changes from breaking production dashboards.

03Building comprehensive data observability using Great Expectations checkpoints and Slack alerts.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add engineerwithai/engineerwith-agents data-quality-frameworks

For use in Claude.ai and ChatGPT

Download Skill