skills.rest
gracefullight
gracefullightCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

get-available-resources

Detect CPU, GPU, memory, and disk resources and generate computational strategy recommendations.

Updated Feb 27, 2026
Search Tags:#parallel processing#memory#scientific computing#gpu#CPU#resource detection#Dask
One-click install
npx skills add https://github.com/gracefullight/cnn --skill get-available-resources-gracefullight
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: get-available-resources
Source: https://github.com/gracefullight/cnn/tree/main/.agents/skills/get-available-resources
Command: npx skills add https://github.com/gracefullight/cnn --skill get-available-resources-gracefullight

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires psutil, and includes scripts (resource) components.

What problem does it solve? Before running computationally intensive scientific tasks, you often don't know whether the machine can handle parallel processing, GPU acceleration, or in-memory dataset loading. This Skill detects available system resources and produces a JSON report with strategic recommendations so you can choose the right computational approach upfront. ## Core Features & Use Cases - Hardware Detection: Identifies CPU cores, RAM, disk space, and GPUs across NVIDIA CUDA, AMD ROCm, and Apple Silicon Metal backends. - Strategic Recommendations: Generates guidance on parallel worker counts, memory strategies (Dask, Zarr), GPU libraries (PyTorch, JAX), and large data handling. - Use Case: Before analyzing a 50GB genomics dataset, run the detection script to learn that only 8GB of RAM is available, then follow the recommendation to use Dask for out-of-core processing instead of loading everything into pandas. ## Quick Start Run the resource detection script to generate a .claude_resources.json report and tell me which computational strategy to use for my analysis.

Frequently Asked Questions about get-available-resources

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I detect available GPUs in Python for machine learning?▼

Run the detect_resources.py script, which checks for NVIDIA GPUs via nvidia-smi, AMD GPUs via rocm-smi, and Apple Silicon via sysctl and system_profiler. It reports available backends like CUDA, ROCm, or Metal in the output JSON.

How to check CPU cores and memory before parallel processing?▼

Use the detection script to get physical and logical core counts plus available RAM via psutil. The recommendations section suggests an optimal worker count for joblib, multiprocessing, or Dask based on detected cores.

Does GPU detection work on Apple Silicon Macs?▼

Yes, the script detects Apple M1 through M4 chips on macOS and reports Metal as the available backend with unified memory. It suggests libraries like pytorch-mps, tensorflow-metal, and jax-metal for acceleration.

When should I use Dask instead of pandas for large datasets?▼

Use Dask when your dataset size exceeds roughly half of available memory or when the memory strategy recommendation is memory_constrained or moderate_memory. The generated report compares available RAM and flags when out-of-core processing with Dask, Zarr, or h5py is needed.

Why is my GPU not detected by the script?▼

GPU detection fails when drivers or utilities like nvidia-smi or rocm-smi are not installed or not in the system PATH. Verify the tools run manually in a terminal and ensure the GPU is not exclusively held by another process.

Related Skills

Explore More Data & Analytics Skills→
atrawogatrawog

gpu

Monitor GPU status, VRAM, and loaded models for Ollama inference.

Community
Intermediate
jasrajtulsijasrajtulsi

get-available-resources

Detect CPU, GPU, memory, and disk resources for scientific workloads.

Community
Intermediate
Ven0m0Ven0m0
6

cpu-gpu-performance

Monitor CPU and GPU utilization and throttle resource usage during intensive operations.

Community
Advanced
jholhewresjholhewres
3

gcloud

Manage Google Cloud Platform resources via the gcloud CLI.

Community
Intermediate