frank-luongt/plugins/faos-data-ai-analyst/skills/data-quality-frameworks
plugins/faos-data-ai-analyst/skills/data-quality-frameworks/SKILL.md
<!-- AUTO-GENERATED by export-plugins.py — DO NOT EDIT --> --- 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. tags: [data, data-engineering] --- # Data Quality Frameworks Production patterns for implementing data quality with Great Expectations, dbt tests, and data contracts to ensure reliable data pipelin
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skillsauthnpx skillsauth add frank-luongt/faos-skills-marketplace plugins/faos-data-ai-analyst/skills/data-quality-frameworksInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
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TrivyContainer and dependency vulnerability scanner
95%
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SemgrepStatic code analysis for vulnerabilities
95%
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mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
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Socket.devSupply chain security analysis
50%
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VirusTotalMulti-engine malware detection
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CrowdStrikeAdvanced threat intelligence
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OSV-ScannerOpen Source Vulnerability database check
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OWASP Dep-Check
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Last scanned: Apr 20, 2026, 2:57 PM4.6s1 file scanned
SKILL.md
<!-- AUTO-GENERATED by export-plugins.py — DO NOT EDIT -->
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. tags: [data, data-engineering]
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.
Safety
- Avoid blocking critical pipelines without a fallback plan.
- Handle sensitive data securely in validation outputs.
Resources
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