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ml-project-migration

Categorize ML project files for Git versus cloud storage migration.

Updated Mar 13, 2026
Search Tags:#git#migration#infrastructure#vm#ml#cloud-storage#dot-folders
One-click install
npx skills add https://github.com/Albatross679/0313temp --skill ml-project-migration
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ml-project-migration
Source: https://github.com/Albatross679/0313temp/tree/main/.claude/skills/ml-project-migration/ml-project-migration
Command: npx skills add https://github.com/Albatross679/0313temp --skill ml-project-migration

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This guide helps teams decide how to structure ML projects when migrating to a new VM or machine, covering what belongs on Git vs cloud storage, how to handle dot-folders and personal configs, and how to plan required infrastructure.

Core Features & Use Cases

  • File Categorization Framework: classify project files into Git, Cloud Storage, Manual, or Skip categories to streamline migration decisions.
  • Dot-Folder & Personal Config Guidance: provide consistent handling for dot-folders like .claude/, .env, and IDE settings to preserve privacy and portability.
  • Infrastructure Planning: guide choices between Docker, venv, or conda and outline minimal infra needs for cloud GPU providers.

Quick Start

Outline your ML project’s migration goals and run through the categorization schema to determine the Git/cloud storage split.

Frequently Asked Questions about ml-project-migration

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

FAQPage Schema
How do I migrate an ML project to a new VM?▼

Classify project files into Git, cloud storage, manual, or skip categories to streamline VM migration decisions and infrastructure planning.

What is the best way to split ML project files between Git and cloud storage?▼

Splitting ML project files between Git and cloud storage requires a categorization schema that determines what belongs in version control versus cloud buckets for efficient portability.

How do I handle dot-folders and personal configs during machine migration?▼

Handle dot-folders and personal configs during machine migration by applying consistent guidance for directories like .claude/ and .env to preserve privacy and portability across environments.

Should I use Docker, venv, or conda for ML infrastructure planning?▼

Infrastructure planning for ML projects involves choosing between Docker, venv, or conda based on portability requirements and outlining minimal infrastructure needs for cloud GPU providers.

When do I need to plan infrastructure for cloud GPU providers?▼

You need infrastructure planning for cloud GPU providers when migrating ML projects to a new machine, requiring decisions on containerization choices and minimal environment setup to ensure portability.

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