domains / families / data / data-quality-frameworks

data-quality-frameworks

combined

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.

When to use

• 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

When not to use

• The data sources are undefined or unavailable
• You cannot modify validation rules or schemas
• The task is unrelated to data quality or contracts
• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Skill page

Conflict

The three copies share the same high-level scope (Great Expectations, dbt tests, data contracts) but diverge in how they instruct the agent. The foryourhealth111-pixel copy is a detailed, self-contained procedure with specific code patterns for Great Expectations suites and checkpoints. The rmyndharis and sickn33 copies are thin wrappers that delegate the actual work to an external file (resources/implementation-playbook.md) and add safety/limitation constraints not present in the first copy. Because the first copy contains the actual implementation steps while the others contain distinct safety and delegation constraints, crowning one would hide either the detailed patterns or the safety/delegation approach.