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

# Quickstart

> Get started with SPIN

Follow these steps to get started with SPIN:

<Steps>
  <Step title="Sign up">
    Go to [spin.siftd.ai](https://spin.siftd.ai/) and create your account.
  </Step>

  <Step title="Connect a SPIN runtime">
    SPIN runtimes execute your notebook code securely within your infrastructure.

    Then add the runtime to your workspace:

    * Go to **Runtimes** in your workspace
    * Click **Add Runtime**
    * Run the spin-runtime using Docker (copy the command from the UI, which includes the `SPIN_TOKEN`):
      ```bash theme={null}
      docker run -d --name spin-runtime -p 8888:8888 \
           -v spin-runtime-data:/opt/spin/var \
           -e SPIN_TOKEN=your-secure-token \
           ghcr.io/siftd/spin-runtime:latest-prod
      ```
    * Provide the runtime URL:
      ```
      http://localhost:8888/
      ```

    Note: For testing, you can also spin up a runtime hosted by SiftD with a few clicks. For production use, we recommend a self-hosted runtime.

    Learn more in the [SPIN runtime guide](/guide/spin-runtime).
  </Step>

  <Step title="Configure connections">
    Connections let your notebooks securely access external systems like monitoring tools, cloud providers, and APIs.

    To add your first connection:

    * Go to **Connections** in your workspace
    * Click **Create Connection**
    * Choose a connection type (Splunk, Datadog, AWS, etc.)
    * Fill in the required credentials and settings

    Learn more about [connection configuration](/guide/connections).
  </Step>

  <Step title="Create your first notebook">
    Now you're ready to create your first operational notebook:

    1. Go to **Notebooks** in your workspace
    2. Click **Create Notebook**
    3. Give it a descriptive name, like "Database Performance Investigation"
    4. Start adding cells:
       * **Markdown cells** for documentation and context
       * **Python cells** for custom logic and data processing
       * **Integration cells** (Splunk, Datadog, etc.) for tool-specific queries
       * **Shell cells** for command-line operations
  </Step>

  <Step title="Run and share your notebook">
    Execute your notebook cells individually or run the entire notebook:

    * Click the play button on individual cells
    * Use **Run All** to execute the complete workflow
    * Review outputs and results inline
    * Share the notebook with team members for collaboration

    SPIN tracks execution sessions, allowing multiple team members to collaborate on the same investigation and hand off work seamlessly.
  </Step>
</Steps>

## Next Steps

* Explore [integration guides](/integrations) for your specific tools
* Learn about [form cells](/guide/form-cells) for dynamic notebooks
* Set up [AI assistance](/guide/ai-integration) for notebook creation and analysis