marimo▌

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.

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