Which GPU architectures are supported for detection?

The skill detects NVIDIA GPUs via nvidia-smi, AMD GPUs via rocm-smi, and Apple Silicon via system profiling tools.

Does this skill work on Apple Silicon Macs?

Yes, it fully supports Apple Silicon (M1, M2, M3, M4) and detects Metal support, unified memory, and appropriate backends like pytorch-mps.

How does the skill help with memory management?

It checks available RAM and recommends specific strategies like chunking or out-of-core processing if the dataset size exceeds safe memory limits.

Why is this skill recommended at project initialization?

Running it early allows the AI to make informed architectural decisions, such as choosing between pandas or Dask, before writing significant amounts of code.

Computational Resource Profiler

Name: Computational Resource Profiler
Author: Microck

byMicrock

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Data Science & ML

Detects and reports system hardware capabilities to provide optimized strategies for data science and intensive computing tasks.

The Computational Resource Profiler skill allows Claude to perform a comprehensive audit of local hardware environments before initiating resource-heavy operations. By identifying specific CPU core counts, GPU backends (including NVIDIA CUDA, AMD ROCm, and Apple Silicon Metal), and memory availability, it generates a structured JSON report with actionable recommendations. This ensures that subsequent tasks—such as training machine learning models or processing large datasets—automatically utilize the most efficient libraries and parallelization strategies tailored to the specific machine, preventing memory errors and maximizing performance.

Key Features

01Generates strategic library recommendations for Dask, PyTorch, JAX, and Zarr

02Calculates optimal parallel worker counts based on logical core availability

03Produces machine-readable JSON for programmatic integration into analysis scripts

04Cross-platform support for NVIDIA, AMD, and Apple Silicon (M1-M4) accelerators

05Comprehensive hardware detection for CPU, GPU, RAM, and disk space

0681 GitHub stars

Use Cases

01Selecting the correct hardware acceleration backend for deep learning model training

02Configuring the optimal number of parallel workers for high-performance computing tasks

03Determining if large datasets require out-of-core processing via Dask or Zarr vs. local memory

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add microck/ordinary-claude-skills get-available-resources

For use in Claude.ai and ChatGPT

Download Skill