> ## Documentation Index
> Fetch the complete documentation index at: https://docs.capy.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Quickstart

> Get started with Capy in 5 minutes — create an account, connect a GitHub repository, start your first AI coding task, and review the generated code.

<Steps>
  <Step title="Create your account">
    Go to [capy.ai](https://capy.ai), sign up, and choose the Capy Pro tier that matches your expected usage.
  </Step>

  <Step title="Connect a repository">
    From the dashboard, click **Connect GitHub** and install the Capy GitHub App. Choose which repositories to grant access to.
  </Step>

  <Step title="Let Captain set up your dev environment">
    Open **Settings → Project → Dev environment** and click **Set up with Captain**. Captain inspects your repos and writes the install scripts, commands, and VM size every run will use — see [Dev environment](/configs/dev-environment).
  </Step>

  <Step title="Start a task">
    Click **Start task**, pick your repo, and choose a model. Claude Sonnet is a good default.

    Describe what you want. Be specific - reference files, mention patterns, explain the expected behavior. Here are some real examples:

    *"Add a `/health` endpoint to the Express server in `src/server.ts` that returns `{ status: 'ok', uptime: process.uptime() }`."*

    *"The login form at `src/components/LoginForm.tsx` doesn't show validation errors. Add inline error messages for empty email and invalid password format."*

    *"Refactor the user service in `src/services/user.ts` to use the repository pattern like we do in `src/services/order.ts`."*
  </Step>

  <Step title="Review and create a PR">
    Check the diff view to see what changed. When you're happy with it, click **Create PR** and merge on GitHub.
  </Step>
</Steps>

For complex work, start with Captain instead - it'll read your codebase and plan the approach before coding starts.