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This is the software lifecycle around your Lightdash project: version control, the CLI, preview environments, CI/CD, content as code, and the editor and agent tooling that plug into it. Whether you’re changing models by hand or letting an AI coding agent do it, the same lifecycle applies — preview your changes in an isolated project, validate them, and ship them through a pull request rather than editing production directly.

Preview projects

**Preview projects** are temporary Lightdash projects where you can safely experiment with your metrics, dimensions and charts without affecting your production project.

Pull requests

Track the pull requests Lightdash opens in your dbt project from write-backs, the SQL runner, the source editor, and AI agents — including their live status, source, and author — all from one place in your project settings.

Automate with CI/CD

You can use Github actions or other CI/CD tools to automate the CLI workflow

Validating your content

Broken content is not useful content. We've built tools in Lightdash to help validate your content so you can keep it useful and we'll notify you when it isn't.

Content as code

Download your charts, dashboards, spaces, agents, and roles as code, make changes, then upload them back to your Lightdash project with the CLI. Content as code is also how you copy content from an existing project into a new one.

Editing dashboards with agents

Use AI coding agents to bulk edit dashboards and charts using the download-edit-upload workflow.

Agent skills

Use AI coding agents like Cursor, Claude Code, Codex, and Antigravity to build and maintain your Lightdash semantic layer.

Set up VS Code for Lightdash

Configure YAML validation in VS Code so schema and formatting issues in your Lightdash model, chart, and dashboard files are flagged as you edit.

Renaming models, metrics, and dimensions

CLI

How to install the Lightdash CLI

The Lightdash CLI is the recommended way to develop your dbt + Lightdash project. It makes development faster and easier, as well as giving you options for building more powerful automation to manage your Lightdash instance.

Authenticating your CLI

Autogenerate Lightdash-ready YAML files for your models

Use the lightdash generate command to auto-generate and sync schema.yml files for your dbt models so they appear as tables in Lightdash.

Deploy changes to production

Push your local dbt project to your production Lightdash project with lightdash deploy, or refresh metadata from your connected git repository with lightdash refresh.

Lightdash Validate

You can check to see if your changes break any of the content in your project using `lightdash validate`. This command will run the validator which will return a list of content in your project that is broken and details about the errors.

How to use lightdash lint

Validate your Lightdash Code files against JSON schemas before deploying to catch errors early.

Test changes with Lightdash compile

If you've connected Lightdash to GitHub, you can setup a `github action` and get Lightdash to compile your project and test your changes whenever you open a pull request.

Lightdash CLI reference

The Lightdash CLI is the recommended way to develop your Lightdash project. It makes development faster and easier, as well as giving you options for building more powerful automation to manage your Lightdash projects.