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.
Best use case
data-quality-frameworks is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using data-quality-frameworks should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/data-quality-frameworks/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How data-quality-frameworks Compares
| Feature / Agent | data-quality-frameworks | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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.
Related Skills
web-quality-audit
Comprehensive web quality audit covering performance, accessibility, SEO, and best practices. Use when asked to "audit my site", "review web quality", "run lighthouse audit", "check page quality", or "optimize my website".
sqlmap-database-pentesting
This skill should be used when the user asks to "automate SQL injection testing," "enumerate database structure," "extract database credentials using sqlmap," "dump tables and columns...
gdpr-data-handling
Implement GDPR-compliant data handling with consent management, data subject rights, and privacy by design. Use when building systems that process EU personal data, implementing privacy controls, o...
data-storytelling
Transform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data reports, or building executive present...
database
Database development and operations workflow covering SQL, NoSQL, database design, migrations, optimization, and data engineering.
database-optimizer
Expert database optimizer specializing in modern performance tuning, query optimization, and scalable architectures.
database-migrations-sql-migrations
SQL database migrations with zero-downtime strategies for PostgreSQL, MySQL, and SQL Server. Focus on data integrity and rollback plans.
database-migrations-migration-observability
Migration monitoring, CDC, and observability infrastructure
database-cloud-optimization-cost-optimize
You are a cloud cost optimization expert specializing in reducing infrastructure expenses while maintaining performance and reliability. Analyze cloud spending, identify savings opportunities, and ...
database-architect
Expert database architect specializing in data layer design from scratch, technology selection, schema modeling, and scalable database architectures.
database-admin
Expert database administrator specializing in modern cloud databases, automation, and reliability engineering.
data-structure-protocol
Give agents persistent structural memory of a codebase — navigate dependencies, track public APIs, and understand why connections exist without re-reading the whole repo.