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Data Quality Frameworks

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.

techwavedev/agi-agent-kit0installs4stars

SKILL.md

Full skill instructions

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.
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AGI Framework Integration

Adapted for @techwavedev/​agi-agent-kit Original source: 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.

# 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:

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.

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.

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