# Data Quality Frameworks

Implement data quality validation with Great Expectations, dbt tests, and data contracts.

- Repository: https://github.com/foryourhealth111-pixel/Vibe-Skills (3789 stars)
- Category: Testing
- License: Apache-2.0
- Last updated: 2026-10-31
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add foryourhealth111-pixel/Vibe-Skills --skill data-quality-frameworks -g -a claude-code -y`
Codex: `npx skills add foryourhealth111-pixel/Vibe-Skills --skill data-quality-frameworks -g -a codex -y`
Cursor: `npx skills add foryourhealth111-pixel/Vibe-Skills --skill data-quality-frameworks -g -a cursor -y`
Gemini CLI: `npx skills add foryourhealth111-pixel/Vibe-Skills --skill data-quality-frameworks -g -a gemini-cli -y`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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 | `expect_column_values_to_not_be_null` |
| **Uniqueness** | No duplicates | `expect_column_values_to_be_unique` |
| **Validity** | Values in expected range | `expect_column_values_to_be_in_set` |
| **Accuracy** | Data matches reality | Cross-reference validation |
| **Consistency** | No contradictions | `expect_column_pair_values_A_to_be_greater_than_B` |
| **Timeliness** | Data is recent | `expect_column_max_to_be_between` |

### 2. Testing Pyramid for Data

```
          /\
         /  \     Integration Tests (cross-table)
        /────\
       /      \   Unit Tests (single column)
      /────────\
     /          \ Schema Tests (structure)
    /────────────\
```

## Quick Start

### Great Expectations Setup

```bash
# Install
pip install great_expectations

# Initialize project
great_expectations init

# Create datasource
great_expectations datasource new
```

```python
# great_expectations/checkpoints/daily_validation.yml
import great_expectations as gx

# Create context
context = gx.get_context()

# Create expectation suite
suite = context.add_expectation_suite("orders_suite")

# Add expectations
suite.add_expectation(
    gx.expectations.ExpectColumnValuesToNotBeNull(column="order_id")
)
suite.add_expectation(
    gx.expectations.ExpectColumnValuesToBeUnique(column="order_id")
)

The rest: https://github.com/foryourhealth111-pixel/Vibe-Skills/blob/HEAD/bundled/skills/data-quality-frameworks/SKILL.md