data-quality

Automate data quality validation across pipeline stages with Great Expectations.

3|Updated May 28, 2026

Search Tags:#validation #data #quality #pipeline #anomaly-detection #great-expectations

Ready to run

npx skills add https://github.com/mahg-es/araya --skill data-quality-mahg-es

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-quality
Source: https://github.com/mahg-es/araya/tree/main/skills/data-quality
Command: npx skills add https://github.com/mahg-es/araya --skill data-quality-mahg-es

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Bad data in leads to bad decisions. Implementing automated data quality checks ensures data entering analytics and models is complete, accurate, and timely, preventing downstream issues.

Core Features & Use Cases

Per-table schema validation with a defined set of expectations (existence, non-null, uniqueness)
Anomaly detection and trend monitoring across pipeline stages (Bronze → Silver → Gold)
Configurable quality gates: warn, fail, and alert with actionable reports and dashboards

Quick Start

Run a data quality validation pass on the latest daily batch to generate a quality report.

Frequently Asked Questions about data-quality

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

FAQPage Schema

How do I automate data quality validation across my data pipeline?▼

Per-table column expectations enforce data quality by validating schema existence, non-null constraints, and uniqueness. These validation checks prevent downstream analytics issues by ensuring data completeness, accuracy, and timeliness across pipeline batches.

How does anomaly detection work for pipeline data stages?▼

Quality gates enforce data pipeline validation by triggering configurable warn, fail, or alert actions based on validation results. They use per-table column expectations and drift handling to generate actionable reports and dashboards for anomaly detection.

Can I use Great Expectations to validate data pipeline schemas?▼

Yes, you can implement custom validators alongside Great Expectations to enforce data quality gates. This approach supports per-table schema validation, anomaly detection, and configurable reporting for pipeline data completeness and accuracy.

What is the best way to set up quality gates for a daily data batch?▼

Drift handling in data quality validation identifies and manages unexpected changes in data trends across pipeline stages. It works with anomaly detection and row-count checks to trigger quality gates and generate actionable alert reports.