get-available-resources

What problem does it solve?

Scientific computing tasks often fail or run inefficiently due to unknown or mismanaged system resources. This Skill automatically detects your system's capabilities and provides strategic recommendations, preventing wasted time and resources by ensuring your computational approach matches your environment.

Core Features & Use Cases

• Automated Resource Detection: Identifies CPU capabilities (cores, architecture), GPU availability (NVIDIA, AMD, Apple Silicon), total and available memory, and disk space.
• Strategic Recommendations: Generates tailored advice for parallel processing (e.g., optimal worker count), memory management (e.g., out-of-core computing), and GPU acceleration (e.g., suggested libraries).
• JSON Output: Creates a .claude_resources.json file in your working directory, making resource information and recommendations easily accessible for programmatic use.
• Use Case: Before training a large neural network, use this skill to determine if a GPU is available, what type it is, and how much memory is free. Then, adjust your model's batch size or choose the appropriate deep learning framework (e.g., PyTorch-MPS for Apple Silicon) to maximize performance.

Quick Start

Run the detection script to generate a resource report: python scripts/detect_resources.py Then, review the .claude_resources.json file for strategic recommendations.