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AMGrobelnik
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aii_get_hardware

Detect CPUs, RAM, GPU/VRAM, and disk space, including cgroup limits.

1|Updated Feb 27, 2026
Search Tags:#performance#resources#detection#hardware#gpu#cpu#system info
One-click install
npx skills add https://github.com/AMGrobelnik/ai-inventor-old3 --skill aii-get-hardware
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: aii_get_hardware
Source: https://github.com/AMGrobelnik/ai-inventor-old3/tree/main/.claude/skills/aii_get_hardware
Command: npx skills add https://github.com/AMGrobelnik/ai-inventor-old3 --skill aii-get-hardware

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill identifies the available hardware resources on the system, including CPUs, RAM, GPU, and disk space, which is crucial for optimizing performance-sensitive code.

Core Features & Use Cases

  • Hardware Detection: Reports on CPU count, model, RAM capacity, disk usage, and GPU availability/VRAM.
  • Container Awareness: Detects resource limits imposed by containerized environments (e.g., Docker, Kubernetes).
  • Use Case: Before running a computationally intensive machine learning model training job, use this Skill to understand the available GPU memory and CPU cores to configure the job for optimal performance.

Quick Start

Run the bash script to detect available hardware.

Frequently Asked Questions about aii_get_hardware

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

FAQPage Schema
How do I detect available system hardware resources like CPU and GPU?▼

You can detect available system hardware by running a bash script that reports CPU count and model, RAM capacity, disk usage, and GPU availability or VRAM. This hardware detection reports the resources present on your machine.

Does hardware detection work inside Docker or Kubernetes containers?▼

Hardware detection works inside Docker or Kubernetes by reading cgroup limits to account for containerized environments. This container awareness ensures reported CPU and RAM reflect the actual resource limits imposed by your deployment platform.

Why do I need to check GPU VRAM and CPU cores before running machine learning models?▼

Checking GPU VRAM and CPU cores before running machine learning models helps you configure computationally intensive training jobs for optimal performance. Understanding available hardware resources allows you to allocate tasks to appropriate hardware and prevent allocation errors.

What system information is reported when detecting hardware for performance-sensitive code?▼

System information reported during hardware detection includes CPU count and model, RAM capacity, disk usage, and GPU availability with VRAM details. This system info is essential for optimizing the execution of performance-sensitive code.

Can I use a bash script to find available disk space and RAM capacity?▼

Yes, you can use a bash script to find available disk space and RAM capacity. The script detects and reports these hardware resources, along with CPU and GPU system info, to help you optimize performance-sensitive code execution.

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