What is the benefit of marimo's DAG structure?

The Directed Acyclic Graph (DAG) structure ensures there are no circular dependencies and that the execution order is determined by data flow rather than cell position, making notebooks more reliable and reproducible.

Can I deploy marimo notebooks as standalone web applications?

Yes, marimo notebooks can be executed as scripts or deployed as interactive web apps. This skill provides the patterns and best practices for that deployment process.

How does marimo handle SQL queries?

Marimo has built-in support for SQL via DuckDB, allowing you to query dataframes and variables directly within your notebook and display the results reactively.

What makes marimo different from Jupyter notebooks?

Unlike Jupyter's linear execution, marimo is reactive. When you change a variable in one cell, all dependent cells automatically update, ensuring your notebook state is always consistent.

Marimo Development

Name: Marimo Development
Author: CodingKaiser

byCodingKaiser

データサイエンスとML

Builds reactive Python notebooks, interactive dashboards, and data-driven applications using the marimo framework.

概要

This skill empowers Claude to act as a marimo expert, guiding users through the creation of reactive Python notebooks that function both as reproducible scripts and deployable web apps. It provides specialized knowledge on marimo's unique directed acyclic graph (DAG) execution model, interactive UI element integration, and DuckDB-powered SQL workflows. Whether you are converting legacy Jupyter notebooks, building complex interactive dashboards, or optimizing notebook performance, this skill ensures best practices for state management, reactive execution, and responsive layout design.

主な機能

Automated Jupyter notebook to marimo conversion strategies
0 GitHub stars
Reactive programming model implementation and DAG optimization
Interactive UI component integration including sliders, buttons, and tables
Native DuckDB SQL integration for reactive data exploration
Advanced layout management using hstack, vstack, and tabbed interfaces

ユースケース

Converting non-linear Jupyter notebooks into reproducible Python scripts
Building interactive data science dashboards and shareable web apps
Developing SQL-based data analysis tools with interactive filtering

概要

主な機能

Automated Jupyter notebook to marimo conversion strategies
0 GitHub stars
Reactive programming model implementation and DAG optimization
Interactive UI component integration including sliders, buttons, and tables
Native DuckDB SQL integration for reactive data exploration
Advanced layout management using hstack, vstack, and tabbed interfaces

ユースケース

Converting non-linear Jupyter notebooks into reproducible Python scripts
Building interactive data science dashboards and shareable web apps
Developing SQL-based data analysis tools with interactive filtering