Can I use SQL directly within Marimo?

Yes, Marimo includes native SQL integration that supports DuckDB and allows you to query existing Python dataframes directly using SQL syntax.

How does Marimo differ from Jupyter notebooks?

Marimo notebooks are reactive, meaning dependent cells automatically re-run when a variable changes. They are also stored as pure Python files rather than JSON, making them Git-friendly.

How does this skill handle interactive UI elements?

The skill provides patterns for using the mo.ui library to create sliders, buttons, and tables that trigger reactive updates across the notebook without requiring callbacks.

Are Marimo notebooks compatible with standard Python tools?

Since Marimo notebooks are saved as .py files, they can be linted, formatted, and executed as standard Python scripts or deployed as standalone web applications.

Marimo Reactive Notebooks

Name: Marimo Reactive Notebooks
Author: btimothy-har

bybtimothy-har

Data Science & ML

Develops and manages reactive Python notebooks that function as version-control-friendly scripts and deployable web apps.

About

This skill empowers Claude to build and maintain marimo notebooks, a modern alternative to Jupyter that uses reactive execution and stores notebooks as pure Python files. It provides comprehensive guidance on marimo's unique directed acyclic graph (DAG) execution model, interactive UI components via mo.ui, and native SQL integration. By enforcing deterministic state and preventing hidden variables, this skill ensures the creation of reproducible data science workflows, interactive dashboards, and maintainable data applications that integrate seamlessly with standard developer tools like Git and uv.

Key Features

Automatic caching and deterministic execution patterns
Native SQL support with DuckDB and dataframe querying
Reactive DAG-based cell execution and state management
Pure Python file format for seamless Git version control
0 GitHub stars
Interactive UI element integration for building data apps

Use Cases

Creating reproducible research notebooks with strict version control
Building interactive data dashboards and internal business tools
Developing integrated SQL and Python data processing pipelines

About

Key Features

Automatic caching and deterministic execution patterns
Native SQL support with DuckDB and dataframe querying
Reactive DAG-based cell execution and state management
Pure Python file format for seamless Git version control
0 GitHub stars
Interactive UI element integration for building data apps

Use Cases

Creating reproducible research notebooks with strict version control
Building interactive data dashboards and internal business tools
Developing integrated SQL and Python data processing pipelines