get-available-resources

You can check available CPU cores and memory before running parallel processing jobs by detecting system resources to prevent wasted compute runs. This skill reports CPU architecture, memory availability, and swap usage, then outputs a JSON file with recommendations for parallelization levels and memory-safe strategies.

Detecting GPU backends for accelerating large dataset analysis involves checking availability across NVIDIA/CUDA, AMD/ROCm, and Apple Silicon/Metal. This skill scans your machine for compatible GPU acceleration options and provides actionable recommendations for choosing optimal execution paths.

Planning memory strategy and disk capacity for out-of-core scientific workloads requires evaluating working-directory storage against RAM limits. This skill captures current memory and swap usage alongside disk capacity, recommending streaming or out-of-core processing libraries like Dask, Zarr, or H5py when needed.

Psutil works for detecting system resources to prepare memory-safe computation plans by capturing CPU counts, memory availability, and disk capacity. Using this dependency, the skill generates a context-aware report recommending parallelization, memory strategies, and GPU acceleration approaches.

Model training fails from overcommitting resources during large parallel batch processing when jobs exceed available CPU, GPU, or RAM limits. Detecting your machine's compute capacity beforehand prevents this by recommending an efficient strategy tailored to your specific resource constraints.

You can use resource detection before scaling parallel jobs for large genomics datasets to avoid resource exhaustion. The skill evaluates CPU, memory, and disk capacity, then suggests appropriate parallelization levels and memory-safe libraries to handle large-data workloads efficiently.