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get-available-resources

Detect CPU, GPU, memory, and disk resources and output a timestamped .claude_resources.json file.

Updated Aug 23, 2026
Search Tags:#scientific computing#parallel computing#resource detection#memory planning#gpu discovery#disk capacity#dask zarr
One-click install
npx skills add https://github.com/gabrielvuksani/wotann --skill get-available-resources-gabrielvuksani
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: get-available-resources
Source: https://github.com/gabrielvuksani/wotann/tree/main/skills/scientific/get-available-resources
Command: npx skills add https://github.com/gabrielvuksani/wotann --skill get-available-resources-gabrielvuksani

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill prevents slowdowns and failed runs by identifying CPU, GPU, memory, and disk constraints before you start computationally intensive scientific work.

Core Features & Use Cases

  • Detects compute environment: Reports OS details, CPU core counts, RAM/swap availability, disk capacity, and available GPU backends (CUDA, ROCm, Metal).
  • Generates strategy recommendations: Produces guidance for choosing parallelism level, memory-efficient data handling (e.g., Dask/Zarr/HDF5), GPU acceleration libraries, and large-file approaches based on detected limits.
  • Creates a reusable JSON artifact: Writes a .claude_resources.json file you can read in subsequent steps to make resource-aware decisions.

Use cases include initializing a new analysis pipeline, deciding between in-memory vs out-of-core processing for large datasets, selecting worker counts for joblib/multiprocessing/Dask, and choosing GPU tooling appropriate to the detected hardware.

Quick Start

Run the skill to generate a .claude_resources.json file, then use its recommendations to decide whether to run in parallel, switch to Dask/Zarr chunking, or use GPU acceleration for your planned workload.

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 CPU, GPU, and memory resources before running scientific computing workloads?▼

To detect available resources for scientific computing workloads, run this skill to profile CPU cores, RAM, disk capacity, and GPU backends. It generates a timestamped JSON file containing structured hardware data and strategy recommendations for parallelism and memory planning.

What's the best way to decide between in-memory processing and out-of-core chunking for large datasets?▼

Deciding between in-memory processing and out-of-core chunking requires knowing your RAM and disk limits. This skill detects available memory and disk capacity, then recommends memory-efficient data handling strategies like Dask or Zarr based on the detected hardware constraints.

How do I select the right number of workers for Dask or multiprocessing on my system?▼

Selecting the right number of workers for Dask or multiprocessing depends on your CPU core count. This skill detects available CPU cores and generates parallelism level recommendations in a structured JSON artifact to guide your worker count configuration.

Can I check for available CUDA, ROCm, or Metal GPU acceleration backends on macOS, Linux, and Windows?▼

Yes, you can check for available GPU acceleration backends across macOS, Linux, and Windows. The skill detects supported GPU frameworks like CUDA, ROCm, and Metal, then provides guidance on choosing appropriate GPU tooling for your detected hardware.

Do I need psutil installed to detect system resources and generate compute strategy recommendations?▼

Yes, you need psutil installed as a dependency to detect system resources. The skill uses this library to query OS details, CPU counts, and memory availability, outputting a reusable JSON file to drive GPU acceleration and parallel execution planning decisions.

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