Can I use SQL directly in marimo?

Yes, marimo has built-in support for SQL using DuckDB. You can query DataFrames or external files and the results are automatically integrated into the reactive notebook environment.

What makes marimo notebooks different from Jupyter?

Unlike Jupyter, marimo is reactive; when you update a variable in one cell, all dependent cells automatically re-run. This eliminates the 'hidden state' problem and ensures reproducibility.

How does this skill handle UI elements?

The skill provides patterns for mo.ui components like sliders, dropdowns, and forms. It follows best practices such as separating UI definitions from their .value access to maintain reactivity.

Does this skill support common Python visualization libraries?

Yes, it includes optimized patterns for Matplotlib, Plotly, and Altair, ensuring that charts are returned as the final expression in a cell for proper display.

Marimo Notebook Essentials

Name: Marimo Notebook Essentials
Author: CodingKaiser

byCodingKaiser

数据科学与机器学习

Develops reactive Python notebooks and interactive dashboards using the marimo framework.

关于

This skill equips Claude with the specialized knowledge to build, optimize, and debug marimo notebooks, a reactive alternative to traditional Jupyter notebooks. It provides patterns for implementing reactive cell structures, managing state without hidden variables, and integrating interactive UI elements like sliders, forms, and tables. By adhering to marimo's unique reactivity rules and layout systems, this skill enables the creation of reproducible data applications, high-performance visualizations with Altair or Polars, and integrated SQL workflows via DuckDB.

主要功能

Reactive cell patterns and variable propagation rules
Native SQL integration with DuckDB support
CLI workflow management for editing and running notebook apps
Advanced data visualization using Plotly, Altair, and Matplotlib
Interactive UI component implementation (sliders, forms, tables)
0 GitHub stars

使用场景

Building interactive data science dashboards with real-time feedback
Prototyping internal tools and data explorers with reactive Python UI
Creating reproducible research notebooks that eliminate hidden state

关于

主要功能

Reactive cell patterns and variable propagation rules
Native SQL integration with DuckDB support
CLI workflow management for editing and running notebook apps
Advanced data visualization using Plotly, Altair, and Matplotlib
Interactive UI component implementation (sliders, forms, tables)
0 GitHub stars

使用场景

Building interactive data science dashboards with real-time feedback
Prototyping internal tools and data explorers with reactive Python UI
Creating reproducible research notebooks that eliminate hidden state