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get-available-resources

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

Updated Jul 1, 2026
Search Tags:#memory#gpu#cpu#parallel-processing#disk-space#resource-detection#scientific-computing
One-click install
npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill get-available-resources-jasrajtulsi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: get-available-resources
Source: https://github.com/jasrajtulsi/GRAD-SCOPE/tree/main/.claude/skills/get-available-resources
Command: npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill get-available-resources-jasrajtulsi

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

It helps you quickly inspect the machine before running heavy scientific workloads so you can avoid choosing an approach that exceeds available CPU, GPU, memory, or disk capacity.

Core Features & Use Cases

  • CPU and Parallelism Detection: Measures core counts and helps choose an appropriate number of workers for joblib, multiprocessing, or Dask.
  • GPU and Backend Detection: Identifies NVIDIA, AMD, or Apple Silicon acceleration options for PyTorch, JAX, or TensorFlow.
  • Memory and Disk Planning: Reports available RAM and storage so you can decide whether to load data in memory or use out-of-core workflows.
  • Use Case: Before training a large model or processing a multi-gigabyte dataset, run this Skill to get a resource snapshot and recommendations that shape the implementation plan.

Quick Start

Ask the skill to detect the current system resources and summarize the best compute strategy for your scientific task.

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 CPU and GPU resources before training a model?▼

Detecting available CPU and GPU resources before model training involves locally inspecting system state to measure core counts and identify NVIDIA, AMD, or Apple Silicon acceleration backends for frameworks like PyTorch, JAX, or TensorFlow.

What is the best way to check memory and disk space for large dataset processing?▼

Checking memory and disk space for large dataset processing involves reporting available RAM and storage capacity, which helps you decide whether to load data directly into memory or implement out-of-core workflows.

How do I determine the optimal number of workers for parallel processing?▼

To determine the optimal worker count for parallel processing, you measure available CPU cores to generate structured recommendations for configuring joblib, multiprocessing, or Dask workers.

Does this resource detection require psutil to inspect system state?▼

Yes, this resource detection process requires the psutil dependency to locally inspect system state and accurately assess CPU, GPU, memory, and disk limits for scientific workloads.

When should I assess compute resources before running heavy scientific workloads?▼

You should assess compute resources before running heavy scientific workloads to avoid choosing a model training or data analysis approach that exceeds available CPU, GPU, memory, or disk capacity.

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