data-quality-frameworks-sickn33

name	data-quality-frameworks-sickn33
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.
metadata	{"upstream":{"risk":"unknown","source":"community","date_added":"2026-02-27"},"category":"data","source":{"repository":"https://github.com/sickn33/antigravity-awesome-skills","path":"skills/data-quality-frameworks","license_path":"LICENSE","commit":"bdce4fc3ce8899e7c1133204e5825955a7f940da"}}

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.

Limitations

• 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.