marimo

Develops and manages reactive Python notebooks for UI and CLI execution.\n

Updated Jul 18, 2026

Search Tags:#reactivity #wasm #notebooks #data-science #batch-processing #marimo

Ready to run

npx skills add https://github.com/arthrod/conejo-skills --skill marimo-arthrod

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: marimo
Source: https://github.com/arthrod/conejo-skills/tree/main/skills/marimo
Command: npx skills add https://github.com/arthrod/conejo-skills --skill marimo-arthrod

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires marimo, pydantic, python-dotenv, rich, wigglystuff, and includes references (resource) components.

What problem does it solve?

This Skill bridges the gap between interactive data exploration and production-grade batch execution, allowing you to maintain a single codebase for both UI-driven analysis and automated CLI-based workflows.

Core Features & Use Cases

Reactive Notebooks: Create interactive data applications where UI elements like sliders and buttons automatically trigger cell updates.
Batch-Ready Execution: Seamlessly transition from interactive editing to scheduled batch runs using Pydantic-based parameter validation and CLI argument parsing.
Anywidget Integration: Build custom, reusable UI components using vanilla JavaScript and CSS directly within your notebook.
WASM Compatibility: Validate and prepare notebooks for browser-based execution via Pyodide, ensuring your data tools run anywhere.

Quick Start

Use the marimo skill to check the WASM compatibility of the notebook file named analysis.py.

Frequently Asked Questions about marimo

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

FAQPage Schema

How do I convert a reactive Python notebook into an automated CLI batch processing script?▼

To convert a reactive Python notebook into an automated CLI batch script, you can use Pydantic-based parameter validation and CLI argument parsing to transition from interactive editing to scheduled batch runs.

How can I validate Python notebook code for WebAssembly environments?▼

To validate Python notebook code for WebAssembly environments, you can check the notebook file against WASM compatibility rules, ensuring it is prepared for browser-based execution via Pyodide.

How do I build custom interactive UI components inside a Python data science notebook?▼

To build custom interactive UI components inside a Python data science notebook, you can use Anywidget integration to write reusable vanilla JavaScript and CSS directly within your reactive cells.

Can I use the same Python codebase for both interactive browser-based UI and scheduled batch runs?▼

Yes, you can use the same Python codebase for both interactive browser-based UI and scheduled batch runs by maintaining reactive notebooks that support automated CLI execution.

What is the best way to create reactive data applications where UI elements automatically trigger cell updates?▼

The best way to create reactive data applications where UI elements trigger cell updates is to build reactive Python notebooks where sliders and buttons automatically propagate changes across cells.

Do I need Pydantic to run reactive notebooks in a batch processing workflow?▼

Yes, you need Pydantic to run reactive notebooks in a batch processing workflow because it provides the parameter validation required to seamlessly transition interactive notebooks into automated CLI batch execution.