---
name: data-quality-frameworks
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."
risk: unknown
source: community
date_added: "2026-02-27"
---

# Data Quality Frameworks

Production patterns for implementing data quality with Great Expectations, dbt tests, and data contracts to ensure reliable data pipelines.

## Use this skill when

- 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

## Do not use this skill when

- The data sources are undefined or unavailable
- You cannot modify validation rules or schemas
- The task is unrelated to data quality or contracts

## Instructions

- Identify critical datasets and quality dimensions.
- Define expectations/tests and contract rules.
- Automate validation in CI/CD and schedule checks.
- Set alerting, ownership, and remediation steps.
- If detailed patterns are required, open `resources/implementation-playbook.md`.

## Safety

- Avoid blocking critical pipelines without a fallback plan.
- Handle sensitive data securely in validation outputs.

## Resources

- `resources/implementation-playbook.md` for detailed frameworks, templates, and examples.

---

<!-- AGI-INTEGRATION-START -->

## AGI Framework Integration

> **Adapted for [@techwavedev/agi-agent-kit](https://www.npmjs.com/package/@techwavedev/agi-agent-kit)**
> Original source: [antigravity-awesome-skills](https://github.com/sickn33/antigravity-awesome-skills)

### Memory-First Protocol

Retrieve prior test strategies, known flaky tests, and coverage gaps. Cache test infrastructure setup to avoid re-configuring test environments.

```bash
# Check for prior testing/QA context before starting
python3 execution/memory_manager.py auto --query "test patterns and coverage strategies for Data Quality Frameworks"
```

### Storing Results

After completing work, store testing/QA decisions for future sessions:

```bash
python3 execution/memory_manager.py store \
  --content "Testing strategy: integration tests hit real DB (no mocks), 85% line coverage, mutation testing on critical paths" \
  --type technical --project <project> \
  --tags data-quality-frameworks testing
```

### Multi-Agent Collaboration

Share test results and coverage reports with code review agents so they can verify adequate coverage on changed code.

```bash
python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "QA complete — test suite expanded with 12 new integration tests, all passing" \
  --project <project>
```

### TDD Enforcement

This skill integrates with the framework's iron-law RED-GREEN-REFACTOR cycle. No production code without a failing test first.

### Agent Team: QA

Dispatch `qa_team` to generate tests and verify they pass before marking implementation complete.

<!-- AGI-INTEGRATION-END -->
