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data-quality-frameworks

Validate pipeline data with Great Expectations, dbt tests, and data contracts.

Updated Feb 3, 2026
Search Tags:#monitoring#validation#dbt#data-pipelines#data-quality#great-expectations#data-contracts
One-click install
npx skills add https://github.com/leonardoteodoroo/amino-advanced --skill data-quality-frameworks-leonardoteodoroo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-quality-frameworks
Source: https://github.com/leonardoteodoroo/amino-advanced/tree/main/.agent/skills/data-quality-frameworks
Command: npx skills add https://github.com/leonardoteodoroo/amino-advanced --skill data-quality-frameworks-leonardoteodoroo

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Data quality is often overlooked in data pipelines, leading to undetected anomalies, inaccuracies, and missed SLAs. This Skill provides a structured approach to implementing validation with Great Expectations, dbt tests, and data contracts to catch defects early and enforce data contracts across teams.

Core Features & Use Cases

  • Data quality validation pipelines that integrate Great Expectations, dbt tests, and contract checks.
  • Automated monitoring of data freshness, completeness, and consistency.
  • Use Case: Build a validation suite for an orders dataset to ensure non-null order_id, valid status, and accurate amounts.

Quick Start

Configure and run a data quality workflow that validates pipelines using Great Expectations, dbt tests, and data contracts.

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 checks in ETL pipelines?▼

Automated data quality monitoring tracks data freshness, completeness, and consistency across ETL and ELT pipelines by running scheduled validation suites using Great Expectations and dbt tests to catch undetected anomalies.

What is the best way to validate data contracts across analytics teams?▼

Data contracts are validated by applying a structured framework that enforces schema and quality rules using dbt tests and Great Expectations, ensuring reliable data products and preventing missed SLAs across engineering teams.

Can I use dbt tests with Great Expectations for pipeline validation?▼

Yes, dbt tests integrate with Great Expectations within a unified data quality workflow to validate pipeline data, combining dbt's SQL-based testing with Great Expectations' validation suites for comprehensive defect detection.

Why does data quality validation fail to catch anomalies in my data pipelines?▼

Data quality validation fails to catch anomalies when pipelines lack structured validation layers; implementing Great Expectations, dbt tests, and data contracts provides automated monitoring to detect inaccuracies early.

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