Create standardized ML competition project structures with Python templates.
npx skills add https://github.com/KameniAlexNea/gladius-agent --skill ml-setup
Please help me install this Agent Skill. Skill: ml-setup Source: https://github.com/KameniAlexNea/gladius-agent/tree/main/gladius/utils/templates/skills/ml-setup Command: npx skills add https://github.com/KameniAlexNea/gladius-agent --skill ml-setup
This Skill automates the initial setup of a machine learning project for competitions, ensuring a standardized and organized directory structure, along with essential pipeline patterns for cross-validation, metrics, and baseline models.
src/ directory layout with pre-defined modules for configuration, data handling, feature engineering, modeling, metrics, and submission.Run the init.sh script from your competition's root directory to set up the project structure.
High-intent search queries and answers about installing and using this skill.
To set up a machine learning competition project, run an initialization bash script from your root directory to automatically generate a standardized src/ layout with boilerplate modules for data loading, feature engineering, modeling, and metrics.
Scaffolding a Python ML pipeline is best handled by generating pre-defined code templates that implement cross-validation strategies, metric calculation, and baseline models, allowing you to focus immediately on feature engineering.
This project scaffolding is specifically designed for ML competitions, providing specialized modules for configuration, model implementation, and submission generation rather than general-purpose Python application development layouts.
You need bash scripting to execute the project bootstrapping and initialize the directory structure, while Python is required for the core ML workflows and executing the generated pipeline templates.
Bootstrapping an ML competition project creates modules for configuration, data handling, feature engineering, model implementation, metric calculation, and submission generation to ensure a standardized pipeline.
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