Prompt
Create an operating checklist
Turn a recurring process into a clear checklist with ownership and exception handling.
ClaudeChatGPTGeminiOperations
Dave AI
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.
instructions.md
--- name: data-quality-frameworks-09e25c780c09 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 -->--- name: data-quality-frameworks-09e25c780c09 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 -->
Platform guidance
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.