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research-project-init

Scaffold an ML research repository with templated configuration and MLflow-ready structure.

Updated Mar 24, 2026
Search Tags:#automation#template#research#scaffold#mlflow#repo-structure
One-click install
npx skills add https://github.com/lucasflores/agent-skills --skill research-project-init
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: research-project-init
Source: https://github.com/lucasflores/agent-skills/tree/main/.apm/skills/research-project-init
Command: npx skills add https://github.com/lucasflores/agent-skills --skill research-project-init

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Scaffold a complete ML research project repository from user inputs.

Core Features & Use Cases

  • Creates a standardized repo layout with templated files (pyproject.toml, apm.yml, README, and config templates).
  • Provides a ready-to-use MLflow-ready scaffold for new experiments across projects.
  • Includes a minimal Python package and test placeholders to validate import and basic workflows.

Quick Start

Provide project name and preferences to generate the repository skeleton.

Frequently Asked Questions about research-project-init

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

FAQPage Schema
How do I scaffold a new ML research project repository with a standardized structure?▼

Scaffolding an ML research project repository requires providing a project name, target directory, Python version, extra dependencies, and author details to render a standardized layout with templated configuration files and MLflow-ready scaffolding.

What files are included when generating an ML research repo template?▼

An ML research repo template includes a standardized layout with pyproject.toml, apm.yml, README, config templates, a minimal Python package, and test placeholders to validate imports and basic workflows.

Can I use this scaffolding tool to set up MLflow tracking for new experiments?▼

Yes, this scaffolding tool provides a ready-to-use MLflow-ready scaffold designed specifically for setting up new experiments across multiple research projects.

What information do I need to provide to create a standardized repo layout for machine learning research?▼

Creating a standardized repo layout requires providing the project name, target directory, extra dependencies, Python version, and author details to render templates into a ready-to-use repository.

What is the best way to automate a Python version-specific ML project structure with config templates?▼

Automating a Python version-specific ML project structure involves providing the required Python version and extra dependencies to render config templates like pyproject.toml into a ready-to-use repository.

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