| name | gpu-discovery |
| description | Detect GPU hardware and CUDA environment, output a structured report for downstream skills. Use this skill before any GPU-intensive task (training, inference, rendering) to determine available hardware and choose appropriate model sizes, batch sizes, and device settings.
|
| compatibility | {"requires":["Bash","Python (>= 3.8)"],"dependencies":[]} |
GPU Discovery
Detect the GPU environment and produce a machine-readable report that other skills can consume
to make hardware-aware decisions (model size, batch size, device selection).
Workflow
1. Run detection script → 2. Read report → 3. Choose configuration
Step 1: Run Detection Script
Run the detection script — it has zero external dependencies (stdlib only):
python scripts/detect_gpu.py
The script writes gpu_info.json and prints a human-readable summary to stdout.
Step 2: Read the Report
Read gpu_info.json. The schema:
GPU available:
{
"mode": "gpu",
"gpu_count": 1,
"gpus": [{
"index": 0,
"name": "NVIDIA GeForce RTX 4090",
"memory_total_mb": 24564,
"memory_free_mb": 22000,
"cuda_version": "12.8",
"driver_version": "560.35.03"
}]
}
No GPU:
{
"mode": "cpu",
"gpu_count": 0,
"gpus": []
}