Contribute

We love contributions! Whether it’s fixing bugs, improving documentation, or adding new features, your help makes dbt-colibri better for everyone.

Feature Requests & Bug Reports

Open an issue describing your feature or bug.
Include examples, screenshots, and motivation.
Collaborators will review and discuss feasibility.

Note

If you would like to implement the feature or bug yourself, please let us know. We will be happy to help you get started.

Setting up your development environment

Fork the repository

Visit the dbt-colibri GitHub repository and click “Fork”.

Clone your fork locally


git clone https://github.com/{your-username}/dbt-colibri.git
cd dbt-colibri

Create new environment and install dependencies


uv sync

Debug locally

Using VSCode debugger, you can run the development server by clicking the “Run and Debug”. Example of a launch configuration, that runs colibri with manifest and catalog files in the bigquery directory:


 {
    "name": "Debug: BigQuery Dialect",
    "type": "debugpy",
    "request": "launch",
    "module": "src.dbt_colibri.cli.cli",
    "args": [
        "generate",
        "--manifest",
        "tests/test_data/bigquery/manifest.json",
        "--catalog",
        "tests/test_data/bigquery/catalog.json",
        "--output-dir",
        "output/bigquery"
    ],
    "console": "integratedTerminal"
    },

Open a pull request (PR)

Go to your fork on GitHub and click Compare & pull request.
Make sure the PR targets the main branch of the original dbt-colibri repository. Please add a detailed description of the changes you made.

Tip

You can install your working branch of dbt-colibri into your dbt project’s environment using.


cd {your-dbt-project}
uv pip install -e {path-to-your-working-branch}