marimo

Create, edit, and debug reactive marimo notebooks with UI elements.

2|1|Updated Nov 30, 2025
One-click install
npx skills add https://github.com/nibzard/skills-kit --skill marimo
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: marimo
Source: https://github.com/nibzard/skills-kit/tree/main/skills/marimo
Command: npx skills add https://github.com/nibzard/skills-kit --skill marimo

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automates the creation, debugging, and interactive enhancement of reactive Python notebooks using the marimo framework, enabling consistent patterns and best practices.

Core Features & Use Cases

  • Create new marimo notebooks with a clear, documented structure.
  • Debug reactive execution, manage dependencies, and optimize performance.
  • Add interactive UI elements and patterns to build dashboards, reports, or ML workflows.
  • Reuse code patterns and utilities to accelerate project setup and maintainability.
  • Convert traditional notebooks to marimo format for progressive migration.

Quick Start

Install marimo in your project and follow the patterns to scaffold a new notebook, add cells with @app.cell decorators, and run the app to interactively explore data.

Frequently Asked Questions about marimo

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

FAQPage Schema
How do I create reactive Python notebooks for dynamic dashboards?

You can create reactive Python notebooks for dynamic dashboards by using marimo to scaffold a documented structure with @app.cell decorators, adding interactive UI elements, and running the app to explore data.

What is the best way to convert traditional notebooks to reactive marimo format?

Converting traditional notebooks to reactive marimo format enables progressive migration by applying structured guidance and reusable code patterns to transform standard cells into a clean, UI-enabled data workflow.

How do I debug reactive execution and manage dependencies in marimo notebooks?

Debugging reactive execution in marimo notebooks involves using provided error-handling recommendations to manage dependencies, optimize performance, and enforce consistent best-practice patterns across your data workflow.

Do I need external dependencies to build interactive ML workflows with marimo?

Building interactive ML workflows with marimo requires no external dependencies beyond the marimo framework itself and common data science libraries already present in your environment.

Can I add UI elements directly inside Python notebooks for data analysis?

You can add interactive UI elements directly inside Python notebooks using marimo patterns to build reports and data analysis dashboards without needing separate frontend tools.

Why should I use reactive notebooks instead of traditional Python notebooks for data workflows?

Reactive notebooks enforce a clean, UI-enabled data workflow by automating dependency management and execution order, overcoming the hidden-state and out-of-order execution issues common in traditional Python notebooks.