uv-bootstrap-ml-knowledge-authoring

To scaffold a new Machine Learning knowledge skill, use this tool to automatically generate the predefined repository directory structure and a template SKILL.md file, ensuring consistent house style and taxonomy placement.

ML skill authoring enforces strict naming conventions where all new skill names must start with the uv- prefix, ensuring repository consistency and immediate identification across the machine learning knowledge base.

Predefined categories for ML knowledge repository skills include model-architecture, training, distributed, serving, paper, kernel, and agents, directing new skills into a consistent taxonomy for organized domain knowledge.

Yes, you need Python 3 and standard library access to execute the scripts that generate ML knowledge skill templates, enforcing repository conventions and scaffolding the directory structure.

The best way to ensure new ML skills adhere to house style is using a scaffolding tool that provides a checklist for essential SKILL.md sections and content quality guidelines during template generation.