dbt

Name: dbt
Availability: InStock
Author: Mako

Automate parsing, compiling, and running dbt Core models across dev and prod environments.

11|6|Updated Nov 15, 2025

Search Tags:#automation #testing #dbt #sql #data-modeling #transformations #data-warehouse

Ready to run

npx skills add https://github.com/mako-ai/mako --skill dbt-mako-ai

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: dbt
Source: https://github.com/mako-ai/mako/tree/main/api/src/agent-skills/dbt
Command: npx skills add https://github.com/mako-ai/mako --skill dbt-mako-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

dbt streamlines the creation, validation, and deployment of data models by coordinating parsing, compiling, and executing dbt workflows, reducing manual scripting and ensuring consistency across environments.

Core Features & Use Cases

Automated validation of models via parsing, compilation, and execution steps like dbt_parse, dbt_compile_model, and dbt_run_model.
Structured project governance with sources, seeds, tests, and snapshots, enabling reliable DAGs and versioned analytics logic.
Use Case: A data team maintains a dev/prod dbt project across multiple environments; engineers edit models, run tests, and generate artifacts for dashboards.

Quick Start

Run the end-to-end dbt workflow by invoking the agent to parse, compile, and run a dev project.

Frequently Asked Questions about dbt

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

FAQPage Schema

How do I automate dbt model testing and deployment across dev and prod environments?▼

Automating dbt model testing and deployment involves coordinating parsing, compiling, and executing dbt workflows to ensure consistent data models across dev and prod environments. This reduces manual scripting by automating validation steps like dbt_parse and dbt_run_model.

What is the best way to validate dbt transformations before running them in a data warehouse?▼

Validating dbt transformations before running them requires structured steps like dbt_parse and dbt_compile_model. This process checks project governance, sources, seeds, and tests to ensure reliable DAGs and versioned analytics logic before actual execution.

Can I run dbt seeds and snapshots as part of an automated data modeling workflow?▼

Yes, you can run dbt seeds and snapshots as part of an automated data modeling workflow. The process supports parsing, compiling, and running models, tests, seeds, and snapshots to maintain structured project governance and reliable analytics.

Does dbt automation work with existing SQL data warehouse projects?▼

Yes, dbt automation works with existing SQL data warehouse projects by applying dbt Core workflows across dev and prod environments. It integrates sources, tests, and snapshots to generate artifacts for dashboards without altering your underlying SQL logic.

Why do I need to parse and compile dbt models before executing transformations?▼

Parsing and compiling dbt models before executing transformations ensures structured project governance and reliable DAGs. This verification loop validates versioned analytics logic and environment configurations, preventing execution errors and ensuring end-to-end reliability.