Prepare Marimo notebooks for scheduled batch execution with CLI arguments.
npx skills add https://github.com/DanSnow/dotfiles --skill marimo-batch
Please help me install this Agent Skill. Skill: marimo-batch Source: https://github.com/DanSnow/dotfiles/tree/main/private_dot_claude/skills/marimo-batch Command: npx skills add https://github.com/DanSnow/dotfiles --skill marimo-batch
This Skill streamlines the process of preparing Marimo notebooks for scheduled batch execution, making it easier to manage and run ML training jobs.
python-dotenv and provides a UI for managing environment variables, including API keys.Use the marimo-batch skill to configure the attached notebook.py for CLI execution by specifying the learning rate and sample size.
High-intent search queries and answers about installing and using this skill.
To run Marimo notebooks for batch processing, this skill integrates Pydantic models with CLI arguments, allowing you to parameterize and execute notebooks non-interactively via command-line interfaces.
Yes, you can use Pydantic models to parameterize ML training jobs in Marimo by mapping model fields to CLI arguments and UI controls, enabling both interactive tuning and automated batch execution.
Marimo integrates with Weights and Biases by optionally logging model parameters and training metrics directly from the notebook, streamlining experiment tracking during scheduled batch runs.
The best way to manage environment variables in Marimo notebooks is using built-in python-dotenv integration, which provides a UI for managing environment variables and API keys during interactive and automated runs.
Yes, this approach supports interactive UI parameter tuning through Marimo UI elements like sliders and forms, alongside automated non-interactive batch execution driven by CLI arguments for ML model training.
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