Convert marimo notebooks into CLI-executable batch jobs with Pydantic parameter configuration.
npx skills add https://github.com/legout/pi-config --skill marimo-batch-legout
Please help me install this Agent Skill. Skill: marimo-batch Source: https://github.com/legout/pi-config/tree/main/installed-skills/marimo-batch Command: npx skills add https://github.com/legout/pi-config --skill marimo-batch-legout
This skill bridges the gap between interactive data exploration and production-grade batch processing by enabling CLI-based execution of marimo notebooks.
Ask the assistant to configure the current notebook for batch processing by defining ModelParams and adding the necessary CLI argument parsing logic.
High-intent search queries and answers about installing and using this skill.
You convert a marimo notebook into a CLI batch job by defining ModelParams using Pydantic and adding argument parsing logic. This enables automated command-line execution for machine learning and data processing tasks.
Running hyperparameter grid searches from marimo notebooks is achieved by transforming them into CLI batch jobs. This allows automated execution of multiple training runs with varying hyperparameters on a compute cluster.
Yes, you can use Pydantic models to configure parameters for marimo batch execution. Defining ModelParams with Pydantic allows seamless switching between UI-driven interactive environments and CLI-driven batch execution modes.
Yes, marimo batch execution supports machine learning training pipelines. You can develop a training pipeline interactively, then use the CLI to launch multiple runs with varying hyperparameters on a compute cluster.
Yes, you need environment variable management to run marimo notebooks as batch jobs. Integration with environment variable management and compute provider APIs is required for scalable job submission and execution.
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