marimo

Create reactive Python notebooks with automatic cell re-execution.

226|35|Updated Feb 7, 2026

Search Tags:#python #dashboard #interactive #data analysis #sql #reactive #notebook

Ready to run

npx skills add https://github.com/DAAF-Contribution-Community/daaf --skill marimo-daaf-contribution-community

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: marimo
Source: https://github.com/DAAF-Contribution-Community/daaf/tree/main/.claude/skills/marimo
Command: npx skills add https://github.com/DAAF-Contribution-Community/daaf --skill marimo-daaf-contribution-community

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a streamlined environment for developing interactive and reproducible data analysis notebooks using Python, eliminating the complexities of traditional notebook state management and enabling rapid iteration.

Core Features & Use Cases

Reactive Execution: Cells automatically re-run when their dependencies change, ensuring consistency.
Pure Python Notebooks: Notebooks are stored as .py files, making them version-control friendly.
Interactive UI Elements: Build dashboards and interactive applications directly within notebooks without callbacks.
Data Handling: Seamlessly integrate SQL queries, Polars DataFrames, and various plotting libraries.
Deployment Options: Easily run notebooks as web apps, scripts, or export them to static HTML/WASM.
Use Case: Develop an interactive dashboard to explore sales data, allowing users to filter by date, region, and product, with visualizations updating in real-time.

Quick Start

Create a new marimo notebook and add a slider UI element to control a displayed value.

Frequently Asked Questions about marimo

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

FAQPage Schema

How do I build a reactive Python notebook for data analysis?▼

Yes, you can build interactive dashboards directly within reactive Python notebooks using built-in UI elements like sliders. These elements update visualizations in real-time without requiring explicit callback functions, streamlining the data exploration process.

Can I execute SQL queries and manipulate data frames in a Python notebook?▼

You can execute SQL queries and manipulate Polars DataFrames seamlessly within reactive Python notebooks. This integrated data handling allows rapid querying and transformation, with results automatically updating across connected notebook cells during exploration.

How do I version control Python notebooks for reproducible research?▼

You can version control Python notebooks by saving them as pure `.py` files. This plain text format eliminates JSON overhead, making reactive notebooks fully diffable and version-control friendly for collaborative reproducible research.

Does marimo support deploying notebooks as web apps or static HTML exports?▼

marimo supports deploying reactive Python notebooks as interactive web apps, runnable scripts, or static HTML/WASM exports. This allows you to transition data analysis from local exploration to shareable deployment without modifying core logic.

What is the best way to handle notebook state management in Python?▼

The best way to handle notebook state management is using reactive execution, which automatically tracks cell dependencies and re-runs affected code. This eliminates hidden state issues common in traditional notebooks, ensuring consistent data analysis.