get-available-resources

To check available CPU and GPU resources for model training, run a detection script that identifies system hardware and outputs a machine-readable JSON file with actionable recommendations for computational planning.

Detecting system memory and disk space for data analysis involves scanning hardware constraints to generate a resource report, guiding whether to use in-memory processing or out-of-core techniques based on available capacity.

Yes, you need psutil installed in your Python environment to detect system resources for computational planning, as the detection script relies on this dependency to scan CPU cores, memory, and disk availability.

You can automate GPU availability checks for simulation parallelism by running a detection script that scans hardware resources and generates guided recommendations on whether to leverage GPU acceleration or adjust parallelism strategies.

Data analysis jobs fail with out of memory errors when workloads exceed available system memory, making it necessary to detect resource constraints beforehand and switch to out-of-core processing techniques to prevent failures.

Resource detection outputs a machine-readable .claude_resources.json file containing hardware specifications and guided recommendations, allowing automated workflows to parse system constraints and adjust computational planning accordingly.