Skip to content
TrustedSkillsbeta
Back to all skills

Marimo Batch

Checked
marimo-team·vlatest·other·Apache-2.0

Marimo Batch efficiently processes multiple images for tasks like object detection and segmentation, accelerating workflows and saving valuable time.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add marimo-team/skills --skill marimo-batch -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

marimo-team

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned marimo-team/skills at 6454470960.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
6454470960
Manifest
skills/marimo-batch/SKILL.md
Scanned
3 files, 579 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

The marimo-batch skill enables you to execute multiple Marimo notebooks sequentially within an AI agent workflow. This allows for complex, multi-step data processing and analysis where the output of one notebook serves as input for another. It facilitates automated pipelines and reduces manual intervention in repetitive tasks.

When to use it

  • Automating a series of data cleaning and transformation steps using multiple Marimo notebooks.
  • Building a pipeline that generates visualizations from data processed by several sequential notebooks.
  • Running different stages of a machine learning project, such as training, evaluation, and deployment, each encapsulated in a separate notebook.
  • Creating reproducible research workflows involving multiple analysis or modeling notebooks.

Key capabilities

  • Sequential execution of Marimo notebooks.
  • Passing output from one notebook to the next.
  • Automated workflow creation using a list of notebook paths.

Example prompts

  • "Run notebook1.marn then notebook2.marn."
  • "Execute the following notebooks in order: /path/to/notebook_a.marn, /path/to/notebook_b.marn, /path/to/notebook_c.marn."
  • "Process data using data_prep.marn and then visualize it with visualization.marn."

Tips & gotchas

Ensure that the notebooks you are batching are designed to accept input from previous steps, or define clear input mechanisms for each notebook within your workflow. Dependencies between notebooks must be clearly defined for proper execution order.