Can I use this for custom business logic testing?

Absolutely. The skill helps draft custom SQL tests located in the tests/ directory that identify failing rows based on specific business rules.

What types of dbt tests can this skill generate?

The skill generates schema tests (unique, not_null, etc.), dbt_utils tests, custom SQL data tests, and source freshness checks.

Is this compatible with all dbt-supported databases?

Yes, it follows standard dbt syntax which is compatible with Snowflake, BigQuery, Redshift, Databricks, and other supported adapters.

Does it support the dbt_utils package?

Yes, it provides implementation patterns for advanced dbt_utils tests such as accepted_range, recency, and cardinality_equality.

How does it assist with data freshness?

It generates YAML configurations for sources that include warn_after and error_after thresholds based on your specific latency requirements.

dbt Test Generator

Name: dbt Test Generator
Author: armanzeroeight

byarmanzeroeight

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数据库管理

Generates comprehensive dbt test suites including schema validations, data quality checks, and source freshness monitors.

关于

The dbt Test Generator skill streamlines the creation of robust data validation frameworks for analytics engineering projects. It automates the generation of essential schema tests, integrates advanced validation logic via dbt_utils, and establishes source freshness checks to ensure data reliability. By providing standardized implementation patterns for both YAML-based configurations and custom SQL tests, this skill helps data engineers maintain high quality standards, enforce referential integrity, and catch data anomalies before they impact downstream reporting.

主要功能

Configures automated source freshness checks and warning thresholds
Implements advanced dbt_utils tests for range, recency, and cardinality
Creates custom SQL-based tests for complex business logic validation
Generates core schema tests like unique, not_null, and relationships
19 GitHub stars
Provides optimized CLI commands for targeted test execution

使用场景

Automating data quality validation for new staging and mart models
Enforcing referential integrity across complex data warehouse schemas
Setting up monitoring for stale data in upstream source systems

关于

主要功能

Configures automated source freshness checks and warning thresholds
Implements advanced dbt_utils tests for range, recency, and cardinality
Creates custom SQL-based tests for complex business logic validation
Generates core schema tests like unique, not_null, and relationships
19 GitHub stars
Provides optimized CLI commands for targeted test execution

使用场景

Automating data quality validation for new staging and mart models
Enforcing referential integrity across complex data warehouse schemas
Setting up monitoring for stale data in upstream source systems