Harmonia — ML Dependency Harmony

Harmonia detects GPU, CUDA, driver, OS, Python, and installed ML packages — then reports exactly what's compatible with what. Zero dependencies, works offline.

When To Use This Skill

User asks to set up a PyTorch or ML environment
User hits a dependency error with torch, transformers, torchaudio, torchvision, accelerate, or CUDA
User asks "what version of X works with Y" for ML packages
User asks to check their GPU, CUDA, or driver setup
User says something like "my torch is broken", "CUDA error", "version mismatch", "which torch for my Python"
User is installing local models via Ollama or setting up training

Instructions

Step 1: Install harmonia (if not already installed)

Harmonia — ML Dependency Harmony

Harmonia detects GPU, CUDA, driver, OS, Python, and installed ML packages — then reports exactly what's compatible with what. Zero dependencies, works offline.

When To Use This Skill

User asks to set up a PyTorch or ML environment
User hits a dependency error with torch, transformers, torchaudio, torchvision, accelerate, or CUDA
User asks "what version of X works with Y" for ML packages
User asks to check their GPU, CUDA, or driver setup
User says something like "my torch is broken", "CUDA error", "version mismatch", "which torch for my Python"
User is installing local models via Ollama or setting up training

Harmonia

Harmonia — ML Dependency Harmony

When To Use This Skill

Instructions

Step 1: Install harmonia (if not already installed)

Harmonia

Harmonia — ML Dependency Harmony

When To Use This Skill

Instructions

Step 1: Install harmonia (if not already installed)

Step 2: Choose the right command based on the user's need

Step 3: Interpret the output for the user

Step 4: Help the user fix issues

Rules

Constraints

Pytorch Patterns

Regex Vs Llm Structured Text

Effect

Flags

WPF to WinUI 3 Migration Skill

At Dispatch V2