marimo-team/skills▌

Generate interactive anywidget components for marimo notebooks with vanilla JavaScript and CSS. \n \n Create custom widgets by defining _esm (JavaScript render function) and _css (styled for light and dark modes) within an anywidget.AnyWidget subclass \n Use traitlets to define reactive state properties that sync between Python and JavaScript via model.get() , model.set() , and change listeners \n Wrap widgets with mo.ui.anywidget() for display in marimo cells and access state via widget.value d

For general marimo notebook conventions (cell structure, PEP 723 metadata, output rendering, marimo check, variable naming, etc.), refer to the marimo-notebook skill. This skill focuses specifically on mapping Streamlit concepts to marimo equivalents.

Add "Open in molab" badge(s) linking to marimo notebooks. The badge can be added to any target: a GitHub README, documentation site, blog post, webpage, or any other markdown/HTML file.

Verify marimo notebook compatibility with WebAssembly environments and identify incompatible dependencies or code patterns. \n \n Analyzes both PEP 723 metadata and import statements to extract all notebook dependencies, then cross-references against Pyodide's built-in packages and pure-Python availability \n Detects incompatible packages with native C/Rust extensions (torch, tensorflow, psycopg2, etc.) and suggests WASM-friendly alternatives \n Scans code for WASM-blocking patterns including su

Convert Jupyter notebooks to marimo Python scripts with CLI-driven transformation and cleanup guidance. \n \n Use uvx marimo convert <notebook.ipynb> -o <notebook.py> to generate marimo-compatible .py files without local installation \n Run marimo check before and after manual edits to catch syntax and compatibility issues \n Common cleanup tasks include removing Jupyter artifacts ( %magic commands, display() calls), verifying package metadata, and ensuring final cell expressions rende

Prepare marimo notebooks for scheduled batch execution with CLI parameters and optional experiment tracking. \n \n Converts UI-based parameters into Pydantic models that support both interactive forms and command-line argument parsing \n Enables dual-mode execution: iterate with the UI, then run batch jobs via CLI with arguments like --sample-size 4096 --learning-rate 0.005 \n Optionally integrates Weights and Biases for experiment logging and parameter tracking \n Includes EnvConfig for managin

Create Python-based interactive notebooks with marimo's reactive cell architecture. \n \n Notebooks are pure Python files with @app.cell decorated functions representing cells; dependencies and inputs/outputs are automatically managed through function parameters \n Supports three execution modes: script mode (non-interactive testing), interactive browser editing, and CLI execution via uv run marimo \n Use mo.app_meta().mode == \"script\" to detect execution context and adapt data sources without

Turn a research paper into an interactive marimo notebook. For general marimo notebook conventions (cell structure, PEP 723 metadata, output rendering, marimo check, variable naming, etc.), refer to the marimo-notebook skill.