get-available-resources

Detect system resources for compute planning by running a script that profiles CPU, GPU, memory, disk, and OS information. It generates a .claude_resources.json file with actionable recommendations for parallelism, memory strategies, and GPU usage to prevent bottlenecks.

Checking CPU and GPU memory before large-scale data analysis is best handled by assessing system resources upfront. This process outputs a structured JSON file detailing hardware specifications and recommending whether to use out-of-core strategies or GPU acceleration.

Resource detection works across macOS, Linux, and Windows environments. It utilizes the psutil dependency to consistently gather OS, CPU, memory, and disk information, ensuring your computational planning remains accurate regardless of the operating system.

Decide between Dask, Zarr, or CUDA backends by analyzing detected system resources. The generated report provides actionable recommendations on parallelism, out-of-core memory strategies, and GPU usage, directly guiding your backend selection for large file processing.

Assess system resources for out-of-core processing when dealing with datasets exceeding available RAM. Detecting CPU, memory, and disk capacity upfront informs your memory strategy and helps avoid resource bottlenecks during compute-heavy tasks.

Parallel processing fails or slows down when computational planning ignores system resource limits. Detecting CPU cores, memory, and disk space upfront prevents resource bottlenecks by providing actionable recommendations for optimal parallelism and memory strategies.