> For the complete documentation index, see [llms.txt](https://docs.pipekit.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.pipekit.io/get-started/author.md).

# Author Workflows

A 5-minute path for workflow authors joining a Pipekit Organization that's already set up.

If you don't have a Pipekit account yet, follow [Evaluate Pipekit Cloud](/get-started/evaluate-cloud.md) instead. If you're the platform engineer setting Pipekit up for a team, follow [Operate Pipekit](/get-started/operate.md).

## Prerequisites

* A Pipekit account in an existing Organization, invited by an Org Admin.
* The name of a [Cluster](/concepts/cluster.md) you have access to (ask your admin).
* A computer running macOS, Linux, or Windows.

## 1. Install the CLI

```bash
# macOS / Linux (Homebrew)
brew install pipekit/tap/cli

# Windows (Scoop)
scoop bucket add pipekit https://github.com/pipekit/scoop.git
scoop install pipekit/cli
```

For NixOS, Docker, and binary downloads, see [Reference > CLI > Install](/reference/cli.md#installation).

## 2. Log in

```bash
pipekit login
```

If your Organization has [SSO](/organizations-and-access/sso.md) configured, the CLI redirects you to your identity provider. Otherwise enter your Pipekit username and password. The CLI stores your access token at `~/.pipekit/token`.

## 3. List the clusters you can submit to

```bash
pipekit list clusters
```

This confirms which clusters your account has access to. Note the `CLUSTER NAME` you'll use in the next step.

## 4. Submit a workflow

Clone the Pipekit Examples repo and submit a sample workflow:

```bash
git clone https://github.com/pipekit/examples.git
cd examples
pipekit submit --cluster-name=<your-cluster-name> examples/dag-diamond/workflow.yaml
```

The CLI prints a link to the [Run](/concepts/run.md) in the Pipekit UI.

## 5. View the Run

Open the link from step 4 (or browse to <https://pipekit.io/pipes>).

You'll see:

* The [Run Graph](/using-pipekit/runs/run-graph.md): DAG view of the workflow's tasks.
* [Pod Logs](/using-pipekit/runs/pod-logs.md) per container.
* The [submitted YAML](/using-pipekit/runs/workflow-yaml.md).
* Status, duration, and triggering metadata.

## What's next

* **Submit your own workflows**: point `pipekit submit` at any Argo `Workflow` YAML.
* **Stop, terminate, or restart Runs**: see [CLI > Run Actions](/reference/cli.md#run-actions) or do it from the UI.
* **Submit from Python with Hera**: see [Reference > Python SDK](/reference/python-sdk.md).
* **Author CronWorkflows**: see [Cron](/using-pipekit/pipes/cron.md).
* **Use an AI agent** to debug failed Runs or author new workflows. See [AI](/ai.md).

## Best practice

Set `namespace` and `serviceAccountName` on every workflow you submit. If you omit them, Pipekit submits to the cluster's `default` namespace and `default` service account, which usually lack the permissions Argo needs. Ask your platform engineer what namespace and service account your team is supposed to use.