Can it help with data quality?

Absolutely. It provides templates for generic dbt tests, custom SQL tests, source freshness checks, and integration with packages like dbt_expectations.

dbt-coder is a Claude Code skill that provides patterns and best practices for developing data transformation layers using dbt (data build tool), focusing on model organization and SQL best practices.

How does it handle data warehouse layers?

It implements a standard three-layer architecture: Staging (1:1 with sources), Intermediate (complex joins and logic), and Marts (final consumption-ready tables).

Does it support dbt Macros and Packages?

Yes, it includes patterns for creating reusable Jinja macros and configuring the packages.yml file for tools like dbt_utils and codegen.

Does this skill support incremental models?

Yes, it includes standard patterns for incremental materializations using merge, append, and delete+insert strategies to optimize data processing.

dbt Coder

Name: dbt Coder
Author: majesticlabs-dev

bymajesticlabs-dev

•

数据库管理

Standardizes dbt project architecture and SQL transformation patterns for scalable analytics engineering.

关于

dbt-coder is a specialized skill designed to streamline data transformation workflows within Claude. It provides standardized patterns for dbt (data build tool) development, covering everything from project structure and staging models to complex incremental strategies and SCD Type 2 snapshots. Whether you are building a new data mart or refactoring legacy SQL, this skill ensures consistency through pre-defined macros, testing frameworks, and best practices for model organization across Staging, Intermediate, and Mart layers.

主要功能

Standardized dbt_project.yml and source freshness configurations
Reusable Jinja macro implementations for DRY SQL development
Multi-layer model organization (Staging, Intermediate, Marts)
Advanced materialization strategies including Incremental and Snapshots
18 GitHub stars
Automated testing patterns for data quality and integrity

使用场景

Implementing historical tracking and SCD Type 2 logic using dbt snapshots
Converting raw SQL queries into modular, tested dbt models across transformation layers
Initializing a new dbt project with best-practice folder structures and configurations

关于

主要功能

Standardized dbt_project.yml and source freshness configurations
Reusable Jinja macro implementations for DRY SQL development
Multi-layer model organization (Staging, Intermediate, Marts)
Advanced materialization strategies including Incremental and Snapshots
18 GitHub stars
Automated testing patterns for data quality and integrity

使用场景

Implementing historical tracking and SCD Type 2 logic using dbt snapshots
Converting raw SQL queries into modular, tested dbt models across transformation layers
Initializing a new dbt project with best-practice folder structures and configurations