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marimo-batch

Convert marimo notebooks into CLI batch workflows with Pydantic-validated parameters.

Updated Jan 10, 2026
Search Tags:#pydantic#cli#machine learning#batch#notebook#marimo
One-click install
npx skills add https://github.com/RDCrystalLab/agentic-plugins --skill marimo-batch-rdcrystallab
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: marimo-batch
Source: https://github.com/RDCrystalLab/agentic-plugins/tree/main/plugins/marimo/skills/marimo-batch
Command: npx skills add https://github.com/RDCrystalLab/agentic-plugins --skill marimo-batch-rdcrystallab

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps transform interactive marimo notebooks into configurable batch jobs, reducing friction between experimentation and repeatable execution.

Core Features & Use Cases

  • CLI Parameterization: Converts notebook settings into validated command-line parameters using Pydantic models.
  • Experiment Workflow Support: Bridges marimo UI iteration with scheduled script execution, including optional environment configuration and Weights & Biases tracking.
  • Use Case: Prepare a machine learning notebook for production runs by making training parameters configurable while preserving the interactive notebook experience.

Quick Start

Ask the marimo-batch skill to make my notebook runnable from the command line with configurable parameters.

Frequently Asked Questions about marimo-batch

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I convert a marimo notebook into a batch execution workflow?▼

To convert a marimo notebook into a batch execution workflow, you transform interactive notebook settings into validated command-line parameters using Pydantic models, enabling reproducible CLI execution for scheduled runs.

How do I parameterize marimo notebook settings for command-line execution?▼

You parameterize marimo notebook settings for command-line execution by defining Pydantic models that validate inputs, allowing configurable training parameters to be passed directly via the CLI.

Can I run machine learning experiments from marimo notebooks without losing the interactive UI?▼

Yes, you can run machine learning experiments from marimo notebooks via CLI while preserving the interactive notebook structure, bridging UI iteration with scheduled script execution for production runs.

Do I need Pydantic to configure batch jobs for marimo notebooks?▼

Yes, Pydantic-based parameter models are required to validate and configure batch jobs for marimo notebooks, ensuring reproducible data workflows and environment configurations.

Does marimo batch execution support experiment tracking integration?▼

Marimo batch execution supports optional experiment tracking integration, including Weights and Biases tracking and environment configuration, to manage machine learning workflows effectively.

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