# marimo-batch

Prepares marimo notebooks for scheduled batch runs via CLI while preserving interactive UI forms and notebook column layouts.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 3700 installs · ★ 175
- Author: marimo-team
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/marimo-team/skills/tree/HEAD/skills/marimo-batch/SKILL.md
- Updated: 2026-08-19
- HTML: https://affpapa.org/en/nevibecode/skills/marimo-batch

## What it does

This skill refactors a marimo notebook so it executes both interactively and as a headless batch job via `uv run`. Pydantic models define the job parameters. The notebook reads values from UI sliders during interactive sessions and parses command-line flags with `mo.cli_args()` when run as a script.

For secret management, the skill introduces `python-dotenv` and UI-driven `EnvConfig` validation from `wigglystuff`. If tracking is required, it adds `wandb_project` and `wandb_run_name` to the parameter schema. Column definitions remain untouched. Existing layout structures like `@app.cell(column=...)` stay intact throughout the edits.

Hyperparameter sweeps use a reference launcher script named `grid.py` compatible with Hugging Face Jobs, Modal, or RunPod. It runs a dry run by default. Passing `--launch` submits actual jobs, preventing unintended compute charges before verifying parameter combinations.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add marimo-team/skills --skill marimo-batch -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/marimo-batch/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/marimo-batch/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add marimo-team/skills --skill marimo-batch -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/marimo-batch/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add marimo-team/skills --skill marimo-batch -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/marimo-batch/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install marimo-team/skills marimo-batch
```

2. **Manually: put the files in place**
- File: `.github/skills/marimo-batch/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add marimo-team/skills --skill marimo-batch -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/marimo-team/skills --path skills/marimo-batch
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/marimo-batch/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add marimo-team/skills --skill marimo-batch -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/marimo-team/skills/tree/HEAD/skills/marimo-batch/SKILL.md

## When to use it
- prepare a marimo notebook for unattended batch execution
- parse CLI arguments into a validated Pydantic model
- validate API keys in the marimo UI using EnvConfig
- execute a dry run of hyperparameter combinations before running cloud compute

## Example prompts
- Configure this marimo notebook to run as a CLI batch script with uv run.
- Add Pydantic ModelParams and Weights and Biases logging to my marimo notebook.
- Set up EnvConfig for API keys and expose training parameters to CLI arguments.

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: be766aa90eedb914.

BEGIN THIRD-PARTY FILE be766aa90eedb914
````text
---
name: marimo-batch
description: An opintionated skill to prepare a marimo notebook to make it ready for a scheduled run.
---

Pydantic is a great way to declare a source of truth for a batch job, especially for ML. You can declare something like: 

```python
from pydantic import BaseModel, Field

class ModelParams(BaseModel):
    sample_size: int = Field(
        default=1024 * 4, description="Number of training samples per epoch."
    )
    learning_rate: float = Field(default=0.01, description="Learning rate for the optimizer.")
```

You can fill these model params with two methods too, you can imagine a form in the UI. 

```python
el = mo.md("""
{sample_size} 
{learning_rate}
""").batch(
    sample_size=mo.ui.slider(1024, 1024 * 10, value=1024 * 4, step=1024, label="Sample size"),
    learning_rate=mo.ui.slider(0.001, 0.1, value=0.01, step=0.001, label="Learning rate"),
).form()
el
```

But you can also use the CLI from marimo. 

```python
if mo.app_meta().mode == "script":
    if "help" in mo.cli_args() or len(cli_args) == 0:
        print("Usage: uv run git_archaeology.py --repo <url> [--samples <n>]")
        print()
        for name, field in ModelParams.model_fields.items():
            default = f" (default: {field.default})" if field.default is not None else " (required)"
            print(f"  --{name:12s} {field.description}{default}")
        exit()
    model_params = ModelParams(
        **{k.replace("-", "_"): v for k, v in mo.cli_args().items()
    })
else: 
    model_params = ModelParams(**el.value)
```

The user can now run this from the command line via: 

```bash
uv run notebook.py --sample-size 4096 --learning-rate 0.005
```

This is the best of both worlds, you can use the UI to test and iterate, and then use the CLI to run the batch job. Another benefit is that you can run the notebook with settings to make it run quickly to see if there are any bugs in the notebook. 

The user wants to be able to run a notebook using this pattern, so make sure you ask the user which parameters they want to make configurable via the CLI and the proceed to make the changes to the notebook. Make sure you verify the changes with the user before making them. 

## Weights and Biases

It is possible that the user is interested in adding support for weights and biases. Make sure you confirm if this is the case yes/no. If that is the case, make sure these ModelParams are logged. You also want to make sure that the `wandb_project` and `wandb_run_name` are part of the ModelParams is the user wants to go down this route. 

If the user is keen to start a training job for ML, make sure you use [this starting point](references/starting-point.py). Make sure you keep the columns intact in this notebook! 

## Environment Variables
````
END THIRD-PARTY FILE be766aa90eedb914

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