dbt-transformation-patterns
Helps your agent organize data, test, and document models. It does incremental data processing to optimize resource usage.
What it does
This tool helps organize and optimize how you transform data using dbt. It provides guidance on structuring your data, testing it, and making it efficient for large volumes.
When to use it
- ✓When building a data project with dbt.
- ✓When you need to organize your data into logical steps.
- ✓To ensure the quality and documentation of your data.
Example of what to ask your AI
“How can I organize my data models in dbt to be easy to understand and maintain?”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool suggests how to divide your data into layers (like 'staging', 'intermediate', 'marts'), how to name them, and how to implement tests to ensure the data is correct. It also helps set up how data is updated.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill dbt-transformation-patternsOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/dbt-transformation-patterns/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
dbt Transformation Patterns
Production-ready patterns for dbt (data build tool) including model organization, testing strategies, documentation, and incremental processing.
Use this skill when
- Building data transformation pipelines with dbt
- Organizing models into staging, intermediate, and marts layers
- Implementing data quality tests and documentation
- Creating incremental models for large datasets
- Setting up dbt project structure and conventions
Do not use this skill when
- The project is not using dbt or a warehouse-backed workflow
- You only need ad-hoc SQL queries
- There is no access to source data or schemas
Instructions
- Define model layers, naming, and ownership.
- Implement tests, documentation, and freshness checks.
- Choose materializations and incremental strategies.
- Optimize runs with selectors and CI workflows.
- If detailed patterns are required, open
resources/implementation-playbook.md.
Resources
resources/implementation-playbook.mdfor detailed dbt patterns and examples.
Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
FAQ
What is dbt?
dbt (data build tool) is a tool that helps data analysts and engineers transform data in their data warehouses using SQL and Python commands.
What are 'model layers'?
These are logical divisions within your dbt project that help organize code and data flow, such as separating raw data from processed data ready for analysis.
What are 'incremental models'?
These are models that process only new or changed data since the last run, which is much faster and more efficient for large data volumes.
Details
Original description by the author
Production-ready patterns for dbt (data build tool) including model organization, testing strategies, documentation, and incremental processing.
Popularity
- GitHub stars
- 48k
- Ranking position
- #2731
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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