get-available-resources

Detect system resources by running a resource detection script that analyzes CPU, memory, disk space, and GPU availability. It generates a .claude_resources.json file with structured details and recommendations to guide your computational planning and architecture decisions.

Yes, you can check GPU availability across macOS, Linux, and Windows. The resource detection process scans hardware environments to identify GPU presence, helping you determine if your system meets the computational requirements for machine learning workloads.

The best way to generate a machine-readable system profile is to run a resource detection utility that outputs a .claude_resources.json file. This structured report analyzes hardware capabilities and provides recommendations for parallelism, memory strategies, and data handling.

Yes, the resource detection script requires the psutil dependency to analyze disk space, CPU, and memory. This system monitoring library gathers hardware metrics across supported operating systems to generate accurate computational planning reports.

Run system resource detection at project initialization or before heavy data analysis. This evaluates your CPU, memory, disk, and GPU constraints beforehand, ensuring your software architecture decisions and resource budgeting align with available computational capacity.

Computational planning fails when system resources are insufficient because parallel processing requires adequate CPU cores, memory, and disk space. Resource detection identifies these hardware constraints and recommends appropriate parallelism and memory strategies to prevent execution failures.