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marimo

Edit and run reactive Python notebooks stored as plain .py files.

Updated Aug 23, 2026
Search Tags:#python#cli#data-analysis#marimo#ui-widgets#cell-editing#reactive-notebook
One-click install
npx skills add https://github.com/swat9013/dotfiles --skill marimo-swat9013
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: marimo
Source: https://github.com/swat9013/dotfiles/tree/main/.claude-global/skills/marimo
Command: npx skills add https://github.com/swat9013/dotfiles --skill marimo-swat9013

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

marimo provides a discipline and toolset for creating, editing, and running reactive Python notebooks as plain .py files so that interactive analyses and dashboards are Git-friendly, reproducible, and editable without Jupyter.

Core Features & Use Cases

  • Reactive cell model: explicit function-argument dependencies and return-based global variable definitions produce a DAG-driven execution order.
  • Interactive UI primitives: sliders, tables, dropdowns, and buttons that expose values via .value and trigger dependent cell re-execution.
  • Developer workflows & CLI: edit, sandbox, run, convert, export, and lint commands to integrate notebooks into data analysis pipelines and deployment flows.
  • Use case: convert a Jupyter analysis into a marimo script, edit cells safely to avoid mutation issues, run as a read-only web report, and export to HTML for sharing.

Quick Start

Open notebook.py for editing and run it as a read-only web app using the marimo CLI.

Frequently Asked Questions about marimo

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I edit and run reactive Python notebooks as plain scripts?▼

Edit and run reactive Python notebooks as plain .py files using a CLI that supports cell-level reactive behavior and UI widgets. Cells execute based on a DAG-driven order, making analyses reproducible and Git-friendly without Jupyter.

How do reactive notebook cells manage dependencies and execution order?▼

Reactive notebook cells use explicit function-argument dependencies and return-based global variable definitions to produce a DAG-driven execution order. This ensures that changing one cell automatically re-executes only the dependent cells downstream.

Can I convert a Jupyter notebook into a reactive Python script for deployment?▼

Yes, you can convert a Jupyter analysis into a reactive Python script using the convert CLI command. You can then run it as a read-only web report or export it to HTML for lightweight deployment and sharing.

Does this reactive notebook environment support interactive UI widgets for data analysis?▼

Yes, reactive notebooks support interactive UI primitives like sliders, tables, dropdowns, and buttons. Widgets expose values via a .value attribute and automatically trigger the re-execution of dependent cells.

What are the limitations of using reactive Python notebooks?▼

Reactive Python notebooks require explicit return-based global variable declarations and non-mutation design patterns. This means standard in-place mutation of variables will not trigger reactive updates correctly, requiring disciplined coding habits.

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