get-available-resources

Resource detection identifies CPU cores, GPU backends (CUDA, ROCm, Metal), memory, and disk capacity to guide computational strategy. Run the detection script to generate a .claude_resources.json file containing structured resource data and recommendations for parallelization and memory allocation before heavy tasks.

Resource detection identifies CUDA for NVIDIA GPUs, ROCm for AMD GPUs, and Metal for Apple Silicon, outputting the available backend in the structured resource report to inform backend selection for model training and data processing.

Resource detection outputs available memory and disk capacity with strategic recommendations, enabling you to choose in-memory processing for smaller datasets or out-of-core approaches when disk is abundant but RAM is constrained.

Yes. Resource detection reports CPU core count and memory availability, providing guidance on parallelization strategies to prevent under- or over-provisioning when running analyses, training, or large-scale file processing.

Resource detection identifies CPU cores, GPU backends, available memory, disk space, operating system, and Python version, outputting all data in a structured .claude_resources.json file for downstream workflow decisions.

Run resource detection at project initialization and before data analysis, model training, or large-scale file processing to inform parallelization strategy, memory allocation, and backend choices based on available compute resources.