skills.rest
Estrella-231
Estrella-231COMMUNITY
Explore all skills by this author →
GitHub

get-available-resources

Detect CPU, GPU, RAM, and disk resources and generate hardware-aware strategy recommendations.

Updated May 24, 2026
Search Tags:#parallel processing#scientific computing#disk space#system resources#gpu acceleration#memory planning#cpu detection
One-click install
npx skills add https://github.com/Estrella-231/Mathematical_modeling_tongmeng --skill get-available-resources-estrella-231
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: get-available-resources
Source: https://github.com/Estrella-231/Mathematical_modeling_tongmeng/tree/main/.agents/skills/get-available-resources
Command: npx skills add https://github.com/Estrella-231/Mathematical_modeling_tongmeng --skill get-available-resources-estrella-231

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

It solves uncertainty about whether your machine can handle a computational task efficiently by detecting CPU, GPU, memory, and disk constraints before you start.

Core Features & Use Cases

  • Resource Detection: Captures CPU core counts, RAM availability, disk free space, OS/Python details, and GPU availability across NVIDIA (CUDA), AMD (ROCm), and Apple Silicon (Metal).
  • Decision-Ready Output: Writes a JSON report named .claude_resources.json that includes context-aware recommendations for parallelism, memory strategy, GPU usage, and large-data handling.
  • Use Case: Before running a heavy simulation or preprocessing pipeline in a large modeling project, you can quickly determine whether to use Dask/Zarr for out-of-core processing or multiprocessing/joblib for speed.

Quick Start

Run the resource detection at the beginning of your run so the skill creates a .claude_resources.json file with hardware-aware recommendations for your workflow.

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 system resources before running a scientific computing workload?▼

Available system resources for scientific computing are detected by capturing CPU core counts, RAM availability, disk free space, and GPU availability across NVIDIA CUDA, AMD ROCm, and Apple Silicon Metal, outputting a JSON report with parallelism and memory strategy recommendations.

Can I detect GPU acceleration availability for AMD ROCm and Apple Silicon Metal?▼

Yes, GPU acceleration availability is detected across NVIDIA CUDA, AMD ROCm, and Apple Silicon Metal platforms to determine if your machine supports GPU-accelerated computation selection for heavy workloads.

What's the best way to decide between multiprocessing and out-of-core processing for large data?▼

The best way to decide between multiprocessing and out-of-core processing is to evaluate detected RAM and disk constraints, generating recommendations for Dask/Zarr or multiprocessing/joblib based on your hardware capabilities.

Do I need psutil installed to detect CPU and memory constraints for parallel processing?▼

Yes, psutil is a required dependency for detecting CPU core counts and RAM availability, enabling the generation of hardware-aware parallel processing and memory planning strategy recommendations.

How does hardware-aware memory planning work for model training pipelines?▼

Hardware-aware memory planning for model training works by analyzing available RAM and disk space to recommend appropriate memory strategies and libraries, writing context-aware suggestions to a JSON artifact for your workflow.

When should I not use GPU acceleration for my computational task?▼

You should not use GPU acceleration when the detection report indicates incompatible GPU platforms or insufficient memory, recommending CPU-based parallel execution or out-of-core processing instead for your specific workload.

Related Skills

Explore More Software Engineering Skills→
gaebalaigaebalai
2

raspi-can-bootstrap

Automate CAN bootstrapping for Raspberry Pi vehicle diagnostics environments.

Community
Advanced
robotlearning123robotlearning123

zarr-python

Manage large N-dimensional arrays with chunking and compression.

Community
Advanced
xiechyxiechy
3

get-available-resources

Detect host CPU, GPU, memory, and disk resources into a .claude_resources.json file.

Community
Intermediate
hxk622hxk622
8

get-available-resources

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

Community
Intermediate