data-quality-frameworks
Implement data quality frameworks fast
Data quality issues cause failed analytics and broken pipelines. This skill provides proven Great Expectations, dbt tests, and data contract patterns to prevent them.
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Test it
Using "data-quality-frameworks". Give me a minimal Great Expectations suite for orders data.
Expected outcome:
- Defines required columns and primary key checks
- Validates status values and amount range
- Adds freshness and row count checks
Using "data-quality-frameworks". How do I test relationships between tables in dbt?
Expected outcome:
- Uses relationships test for foreign key validation
- Checks referential integrity across models
- Validates customer_id maps to dim_customers
Using "data-quality-frameworks". Create a data contract for customer data.
Expected outcome:
- Specifies schema with required and optional fields
- Defines PII classification for sensitive columns
- Sets freshness SLA and quality check thresholds
Security Audit
SafeThis skill is pure documentation containing educational examples for data quality frameworks. All 69 static findings are FALSE POSITIVES. The scanner incorrectly flagged Great Expectations library method names (e.g., 'expect_column_values_to_not_be_null') as shell backtick execution and weak cryptographic algorithms. The content has no executable code, network calls, file access, or sensitive operations.
Risk Factors
🌐 Network access (5)
⚙️ External commands (28)
Quality Score
What You Can Build
Build validation suites
Create Great Expectations suites and checkpoints for warehouse tables.
Expand dbt testing
Add schema, column, and custom dbt tests for marts.
Define data contracts
Specify contracts with schema, quality checks, and SLAs.
Try These Prompts
Create a basic Great Expectations suite for an orders table with primary key, status, amount, and created_at checks.
Draft dbt tests for fct_orders and dim_customers including recency, relationships, and accepted values.
Write a data contract outline for an orders dataset with schema, quality checks, and SLAs.
Describe an automated data quality pipeline that runs suites for multiple tables and generates a report.
Best Practices
- Start with critical columns and expand tests over time
- Document expectations and ownership for each dataset
- Alert on failures and review trends regularly
Avoid
- Testing every column without prioritization
- Hardcoding thresholds without a rationale
- Skipping freshness checks for event data
Frequently Asked Questions
Is this compatible with Claude and Codex?
Are there limits on dataset size?
Can it integrate with CI or orchestration tools?
Does it access or store sensitive data?
What if a check fails unexpectedly?
How does this compare to a full framework setup?
Developer Details
Author
wshobsonLicense
MIT
Repository
https://github.com/wshobson/agents/tree/main/plugins/data-engineering/skills/data-quality-frameworksRef
main
File structure
📄 SKILL.md