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bswrundquist
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dbt

Guide dbt project structure, commands, models, materializations, tests, and macros.

Updated Mar 9, 2026
Search Tags:#data modeling#dbt#sql#data transformation#analytics engineering#data warehouse
One-click install
npx skills add https://github.com/bswrundquist/devtools --skill dbt-bswrundquist
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dbt
Source: https://github.com/bswrundquist/devtools/tree/main/src/devtools/templates/claude/user/.claude/skills/dbt
Command: npx skills add https://github.com/bswrundquist/devtools --skill dbt-bswrundquist

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides comprehensive guidance and best practices for working with dbt (data build tool), enabling efficient data transformation and modeling.

Core Features & Use Cases

  • Project Structure: Understand and implement standard dbt project layouts.
  • Command Reference: Quickly access and utilize common dbt commands for development, testing, and documentation.
  • Model Best Practices: Learn to write effective staging, intermediate, and mart models.
  • Materialization Strategies: Choose the appropriate strategy (view, table, incremental, ephemeral) for your models.
  • Testing & Macros: Implement data quality tests and reusable SQL macros.

Quick Start

Use the dbt skill to help me set up a new dbt project with a standard structure and provide an example of a staging model.

Frequently Asked Questions about dbt

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I structure a dbt project for data transformation?▼

A standard dbt project structure organizes data transformation models into staging, intermediate, and marts layers, separating raw data cleaning from business logic. This layout facilitates efficient development and maintainable analytics engineering workflows.

What is the best way to choose dbt materialization strategies for SQL models?▼

Choosing dbt materialization strategies involves selecting between view, table, incremental, and ephemeral based on model size and query frequency. Incremental builds are ideal for large data warehouse tables, while views suit lightweight transformations.

How do I write effective staging and mart models in dbt?▼

Effective dbt models separate staging logic, which renames and casts raw data, from marts that apply business logic. Following these data modeling best practices ensures clean, reusable SQL pipelines for analytics engineering.

How do I implement data quality testing and macros in dbt?▼

Implementing data quality testing in dbt involves defining schema tests like unique or not null, while macros allow reusable SQL snippets. These features ensure reliable data transformation and consistent analytics engineering workflows.

What are the most common dbt commands for development and debugging?▼

Common dbt commands include run for executing data transformation models, test for data quality checks, and compile for generating SQL. Debugging relies on these commands to verify project structure and pipeline execution.

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