Does it support dbt documentation?

Absolutely. It includes patterns for generating detailed descriptions for models and columns, ensuring your data warehouse remains well-documented and searchable.

Is this skill compatible with existing dbt projects?

Yes, it can be used to refactor existing projects or as a guide for starting new ones according to modern analytics engineering standards.

What is the dbt-expert Claude Code skill?

It is a specialized capability that provides Claude with best practices and implementation patterns for dbt (Data Build Tool), helping developers build scalable data transformation pipelines.

How does it help with dbt project organization?

The skill enforces a proven directory structure, separating models into staging (raw data), intermediate (business logic), and marts (final reporting tables).

Can I use this skill to automate data testing?

Yes, it provides templates and patterns for defining schema tests like unique, not_null, and custom dbt_utils tests within your YAML configuration.

dbt Data Modeling Expert

Name: dbt Data Modeling Expert
Author: timequity

bytimequity

Gestión de Bases de Datos

Implements industry-standard dbt patterns for scalable data transformation, automated testing, and comprehensive documentation.

Acerca de

The dbt-expert skill transforms Claude into an advanced analytics engineer, providing structured guidance for building robust data pipelines. It emphasizes a modular project architecture by separating logic into staging, intermediate, and mart layers, which ensures maintainability and scalability. By using this skill, developers can quickly generate standardized SQL models, implement automated schema tests to ensure data integrity, and create detailed documentation, significantly reducing the manual overhead of managing complex data warehouses.

Características Principales

Consistent naming conventions for improved team collaboration and readability
Best-practice SQL modeling patterns with modular CTE structures
Standardized project architecture using Staging, Intermediate, and Mart layers
Automated data quality testing templates including uniqueness and null checks
Comprehensive YAML documentation generation for models and columns
0 GitHub stars

Casos de Uso

Scaling data warehouse operations by onboarding new sources with standardized staging models
Implementing a data quality framework with automated schema and integrity tests
Refactoring legacy SQL scripts into a structured and version-controlled dbt project

Acerca de

Características Principales

Consistent naming conventions for improved team collaboration and readability
Best-practice SQL modeling patterns with modular CTE structures
Standardized project architecture using Staging, Intermediate, and Mart layers
Automated data quality testing templates including uniqueness and null checks
Comprehensive YAML documentation generation for models and columns
0 GitHub stars

Casos de Uso

Scaling data warehouse operations by onboarding new sources with standardized staging models
Implementing a data quality framework with automated schema and integrity tests
Refactoring legacy SQL scripts into a structured and version-controlled dbt project