Get Available Resources

Use this skill early in a CS workflow when hardware constraints may change the implementation plan.

Typical Triggers

• choosing between in-memory and out-of-core data processing
• checking whether a model fits on available GPU memory
• sizing parallel workers for preprocessing or evaluation
• deciding whether to run locally, distribute with Dask, or move to Modal
• estimating whether a reproduction or benchmark sweep is realistic on the current machine

Workflow

1. Run scripts/detect_resources.py to collect CPU, GPU, memory, and disk information.
2. Save or inspect the generated JSON report.
3. Translate the report into execution choices.
4. State the constraints explicitly before proposing a heavy workflow.

CS-Focused Decisions

Map resource findings to concrete tool choices:

• fits in RAM: prefer polars, pandas, or standard PyTorch workflows
• exceeds RAM on one machine: consider dask or vaex
• limited local GPU but scalable remote need: consider modal
• multi-GPU or long training runs: plan around pytorch-lightning or distributed tooling
• low disk headroom: avoid materializing large intermediate checkpoints or duplicated datasets

What to Report

By default, summarize:

• CPU cores and whether high parallelism is realistic
• available GPUs, VRAM, and likely model-size limits
• total RAM and whether dataset / checkpoint loading is safe
• free disk space and likely pressure points
• practical recommendations for the current task

Default Output

When no exact format is requested, return:

1. a concise resource summary
2. the main bottlenecks
3. recommended execution strategy
4. any risk to training, evaluation, or large-file processing