Agent skill · data analytics · aiskillstore
data-quality-frameworks
Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.
Why this skill is useful
Provides specific implementation patterns and scripts for data quality validation using Great Expectations and dbt that the AI wouldn't reliably generate on its own.
What it needs
Requires great_expectations installed locally. About 6k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
Data Quality Frameworks Production patterns for implementing data quality with Great Expectations, dbt tests, and data contracts to ensure reliable data pipelines. When to Use This Skill Implementing data quality checks in pipelines Setting up Great Expectations validation Building comprehensive dbt test suites Establishing data contracts between teams Monitoring data quality metrics Automating data validation in CI/CD Core Concepts 1. Data Quality Dimensions Dimension Description Example Check ----------- ------------- --------------- Completeness No missing values expectcolumnvaluestonotbenull Uniqueness No duplicates expectcolumnvaluestobeunique Validity Values in expected range expectcolumnvaluestobeinset Accuracy Data matches reality Cross-reference validation Consistency No contradictions expectcolumnpairvaluesAtobegreaterthanB Timeliness Data is recent expectcolumnmaxtobebetween 2. Testing Pyramid for Data Quick Start Great Expectations Setup Patterns Pattern 1: Great Expectations Suite Pattern 2: Great Expectations Checkpoint Pattern 3: dbt Data Tests Pattern 4: Custom dbt Tests Pattern 5: Data Contracts Pattern 6: Automated Quality Pipeline Best Practices Do's Test early - Validate source data before transformations Test incrementally - Add tests as you find issues Document expectations - Clear descriptions for each test Alert on failures - Integrate with monitoring Version contracts - Track schema changes Don'ts Don't test everything - Focus on critical columns Don't ignore warnings - They often precede failures Don't skip freshness - Stale data is bad data Don't hardcode thresholds - Use dynamic baselines Don't test in isolation - Test relationships too Resources Great Expectations Documentation dbt Testing Documentation Data Contract Specification Soda Core
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/data-quality-frameworks--ef9684