Get Available Resources

Before you spin up that neural network training run or load a 50GB dataset, this does the smart thing: checks what you actually have to work with. It scans for CPU cores, GPU availability (NVIDIA, AMD, or Apple Silicon), memory, and disk space, then writes a JSON file with concrete recommendations like "use 6 workers for joblib" or "this dataset needs Dask because you only have 8GB free." The recommendations are genuinely helpful, telling you whether to go parallel, use out-of-core processing, or tap into GPU acceleration based on what it finds. It's the kind of sanity check that prevents you from writing code that immediately falls over because you assumed more resources than you have.

skills.sh installs: 289
GitHub stars: 33.2K
last updated: Aug 21, 2026

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Install

$ npx -y skills add davila7/claude-code-templates --skill get-available-resources --agent claude-code

Installs into .claude/skills of the current project.

Files

Get Available Resources

Overview

Detect available computational resources and generate strategic recommendations for scientific computing tasks. This skill automatically identifies CPU capabilities, GPU availability (NVIDIA CUDA, AMD ROCm, Apple Silicon Metal), memory constraints, and disk space to help make informed decisions about computational approaches.

When to Use This Skill

Use this skill proactively before any computationally intensive task:

Before data analysis: Determine if datasets can be loaded into memory or require out-of-core processing
Before model training: Check if GPU acceleration is available and which backend to use
Before parallel processing: Identify optimal number of workers for joblib, multiprocessing, or Dask
Before large file operations: Verify sufficient disk space and appropriate storage strategies
At project initialization: Understand baseline capabilities for making architectural decisions

SKILL.md on GitHub