marimo-notebook

Create reactive Python notebooks that run as executable scripts.

3|Updated Apr 2, 2026
One-click install
npx skills add https://github.com/legout/pi-config --skill marimo-notebook-legout
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: marimo-notebook
Source: https://github.com/legout/pi-config/tree/main/installed-skills/marimo-notebook
Command: npx skills add https://github.com/legout/pi-config --skill marimo-notebook-legout

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires marimo, numpy, torch, polars, pytest, and includes references (resource) components.

What problem does it solve?

This skill addresses the limitations of traditional notebook formats by enabling the creation of reactive, script-based Python notebooks that are version-control friendly and production-ready.

Core Features & Use Cases

  • Reactive Execution: Automatically re-runs dependent cells when variables or UI inputs change, eliminating manual state management.
  • Script-First Workflow: Notebooks are saved as standard Python files, allowing for easy integration with IDEs, linters, and testing frameworks like pytest.
  • Interactive UI Components: Easily embed sliders, buttons, and custom anywidget components to create data-driven dashboards and applications.

Quick Start

Use the marimo-notebook skill to initialize a new reactive notebook file for your data analysis project.

Frequently Asked Questions about marimo-notebook

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

FAQPage Schema
How do I create reactive Python notebooks that also work as executable scripts?

Reactive Python notebooks that function as executable scripts are created by saving files in a standard Python format. This approach eliminates hidden state and allows the notebook to run directly via command line.

Can I use pytest to test my data science notebooks?

You can use pytest to test data science notebooks by saving them as standard Python files. This script-first workflow allows standard testing frameworks to execute notebook cells directly without special plugins.

What is the best way to build interactive data dashboards in a Python notebook?

The best way to build interactive data dashboards in a Python notebook is using reactive execution with embedded UI components. Sliders and custom anywidget components automatically update dependent cells when inputs change.

Does marimo support version control for data analysis projects?

Marimo supports version control by saving notebooks as standard Python files. This single-file architecture allows standard IDE integration, linter compatibility, and seamless Git tracking for data analysis projects.

How do I manage dependencies for reactive notebooks using numpy and torch?

Dependencies like numpy and torch for reactive notebooks are managed using uv for environment control. This ensures consistent package resolution and execution control across complex data visualization workflows.

Why should I use reactive execution instead of traditional notebook formats?

Reactive execution eliminates manual state management by automatically re-running dependent cells when variables or UI inputs change. Traditional notebook formats often suffer from hidden state errors that reactive scripts prevent.