Scientific Resource Detection & Strategy

About

Proactively detect available computational resources to generate strategic recommendations for scientific computing workflows. This skill automatically identifies CPU specifications, GPU availability (NVIDIA CUDA, AMD ROCm, Apple Silicon Metal), memory constraints, and disk space to help users make informed decisions about their computational approach. By generating a structured resource report, it guides decisions on whether to employ parallel processing via Dask or joblib, utilize GPU acceleration with PyTorch or JAX, or implement memory-efficient out-of-core strategies for massive datasets.

Key Features

• Strategy guidance for memory-efficient computing using Zarr, Dask, or H5py
• Automated detection of CPU cores, GPU backends (CUDA/ROCm/Metal), RAM, and disk space
• 16 GitHub stars
• Identification of appropriate GPU-accelerated libraries based on detected hardware
• Structured JSON output for programmatic integration with analysis pipelines
• Context-aware recommendations for parallel processing and optimal worker counts

Use Cases

• Optimizing parallel simulations by identifying the ideal number of logical CPU cores
• Configuring machine learning environments by detecting available GPU backends and drivers
• Analyzing massive datasets to determine if they require out-of-core processing strategies