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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

Temporary projects where you can experiment with metrics, dimensions, and charts without touching production

Pull requests

Track the pull requests Lightdash opens in your dbt project, with live status, source, and author

Automate with CI/CD

Automate the Lightdash CLI workflow with GitHub Actions or another CI/CD tool

Validating your content

Find and fix the charts, dashboards, and deliveries broken by changes to your project

Content as code

Download charts, dashboards, spaces, agents, and roles as code, edit them, and upload them back

Migrate dashboards between projects

Copy a dashboard and its charts between Lightdash projects or instances with the CLI

Editing dashboards with agents

Bulk edit dashboards and charts with AI coding agents through the download-edit-upload workflow

Agent skills

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

Set up VS Code for Lightdash

Configure YAML validation in VS Code so schema issues in your Lightdash files surface as you edit

Renaming models, metrics, and dimensions

Rename a model, metric, or dimension and update every chart, dashboard, and scheduled delivery that references it

Keyboard shortcuts

Speed up your workflow with keyboard shortcuts in Lightdash

CLI

How to install the Lightdash CLI

Install the Lightdash CLI with Homebrew, NPM, WSL, or a direct binary download

Authenticate the CLI

Log in with browser OAuth, a personal access token, or CI environment variables, and set your active project

Generate semantic layer YAML

Auto-generate and sync schema.yml files so your dbt models appear as Tables in Lightdash

Deploy changes to production

Push your local dbt project to production, or refresh metadata from your connected git repository

Validate content

Find the content in your project that is broken, and the details of each error

Lint your code

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

Compile changes

Add a GitHub Action that compiles your dbt project and catches Lightdash errors on every pull request

Lightdash CLI reference

Every Lightdash CLI command, its flags, and what it does