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get-available-resources

Detect CPU, GPU, memory, and disk resources and output a JSON report.

4|Updated Mar 2, 2026
Search Tags:#scientific computing#resource detection#parallelism#memory strategy#compute planning#disk capacity#gpu availability
One-click install
npx skills add https://github.com/shushuzn/Rairos --skill get-available-resources-shushuzn
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: get-available-resources
Source: https://github.com/shushuzn/Rairos/tree/main/skills/get-available-resources
Command: npx skills add https://github.com/shushuzn/Rairos --skill get-available-resources-shushuzn

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

It helps you avoid slowdowns and crashes by detecting your CPU/GPU, memory, and disk constraints before starting a heavy scientific computing workflow, then translating those limits into practical execution recommendations.

Core Features & Use Cases

  • Detects CPU capabilities (physical/logical cores, architecture, and optional frequency) to guide parallelism decisions.
  • Detects GPU availability across NVIDIA (CUDA), AMD (ROCm), and Apple Silicon (Metal) to choose the right acceleration backend.
  • Analyzes memory and disk headroom to recommend out-of-core versus in-memory strategies and appropriate storage formats.
  • Generates a JSON resource report (including strategic recommendations) written to .claude_resources.json so downstream planning can be data-driven.
  • Use case: Before running a large genomics analysis, it determines whether Dask/Zarr are needed based on available RAM and disk capacity, and suggests a worker count aligned to your CPU.

Quick Start

Use the get-available-resources skill to create a .claude_resources.json report for your current machine before starting your next large computation.

Frequently Asked Questions about get-available-resources

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

FAQPage Schema
How do I check available compute resources before starting scientific computing workflows?▼

To check available compute resources before scientific computing, you can detect CPU cores, GPU backends, memory headroom, and disk capacity. This generates a JSON snapshot to prevent resource-related failures and guide parallel worker sizing during heavy workloads.

What is the best way to determine parallelism limits for data analysis?▼

Determining parallelism limits involves detecting physical and logical CPU cores alongside memory headroom. This approach produces recommendations for parallel worker sizing, ensuring your data analysis operates within system constraints without crashing.

Does resource detection support GPU backends like CUDA, ROCm, and Metal?▼

Resource detection supports GPU availability across NVIDIA CUDA, AMD ROCm, and Apple Silicon Metal backends. Identifying your specific acceleration backend helps choose the correct GPU libraries for model training and parallel processing tasks.

When do I need an out-of-core memory strategy for large file operations?▼

You need an out-of-core memory strategy for large file operations when available RAM is insufficient for your dataset. By analyzing memory and disk capacity, you can decide between out-of-core and in-memory strategies and select appropriate storage formats.

Can I use psutil to plan compute resources for project initialization?▼

You can use psutil to plan compute resources for project initialization by profiling system constraints. It generates a JSON resource report that guides approach selection based on CPU, GPU, memory, and disk capacity limitations.

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