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get-available-resources

Detect CPU, GPU, memory, and disk resources and output structured JSON recommendations.

4|1|Updated Jun 18, 2025
Search Tags:#memory#performance-tuning#cpu#disk-space#resource-detection#system-resources#gpu-detection
One-click install
npx skills add https://github.com/HolobiomicsLab/Toolomics --skill get-available-resources-holobiomicslab
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: get-available-resources
Source: https://github.com/HolobiomicsLab/Toolomics/tree/main/mcp_host/skills/scientific-skills/scientific-skills/get-available-resources
Command: npx skills add https://github.com/HolobiomicsLab/Toolomics --skill get-available-resources-holobiomicslab

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Detects system resources (CPU cores, memory, disk space) and available GPUs to guide choices for data processing and modeling tasks, reducing waste and avoiding overcommitment.

Core Features & Use Cases

  • Resource detection for CPU, memory, disk, and GPUs (NVIDIA, AMD, Apple Silicon)
  • Generates actionable recommendations for parallelism, memory strategies, and GPU usage
  • Works at task start to inform tooling choices (Dask, joblib, PyTorch, etc.)

Quick Start

Run the resource detection script at the start of any computation to generate a resources report.

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 system resources like CPU, memory, and GPU for data modeling?▼

Run a resource detection script at the start of your computation to identify available CPU, memory, disk, and GPU resources. It outputs structured JSON with hardware specs and actionable recommendations for parallelism, memory handling, and GPU usage.

What is the best way to check GPU availability and prevent memory overcommitment before running compute tasks?▼

The best way to prevent memory overcommitment is to run a resource detection script that assesses available memory and GPU capacity before computation. It generates actionable recommendations for memory handling and parallelism based on detected hardware.

Does this resource detection script work with AMD and Apple Silicon GPUs, or only NVIDIA?▼

Yes, the resource detection script supports NVIDIA, AMD, and Apple Silicon GPUs. It identifies available GPU hardware across these platforms and provides recommendations on GPU usage for your data analysis and modeling tasks.

How do I get recommendations for parallelism and GPU usage when starting a data analysis task?▼

Run the resource detection script at the start of your computation. It evaluates CPU, memory, and GPU resources to generate actionable recommendations for parallelism, memory strategies, and GPU usage, guiding choices for tools like Dask, joblib, or PyTorch.

Do I need to install psutil to detect disk space and CPU cores for computational strategy optimization?▼

Yes, you need to install the psutil dependency to detect disk space and CPU cores. The script relies on psutil to scan system hardware and generate structured JSON outputs for computational strategy optimization.

Why should I detect system resources before selecting data processing tools like Dask or PyTorch?▼

Detecting system resources before selecting tools like Dask or PyTorch reduces waste and avoids overcommitment. By identifying available CPU, memory, and GPU resources upfront, you can make informed choices about parallelism, memory handling, and GPU usage for your data processing tasks.

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