What dependencies are required to run the detection?

The core detection script requires the 'psutil' Python package, while other checks use standard system utilities and the Python standard library.

How does this help Claude write better code?

By knowing your exact hardware, Claude can recommend specific libraries like Dask for low-memory environments or PyTorch-MPS for Mac users, rather than generic solutions.

Which GPU architectures does this skill detect?

The skill identifies NVIDIA GPUs via nvidia-smi, AMD GPUs via rocm-smi, and Apple Silicon (M1/M2/M3/M4) chips via Metal support.

Can I use the output file in my own scripts?

Yes, the skill generates a standard '.claude_resources.json' file that you can load into your own Python scripts to programmatically set parameters like n_jobs or device types.

Scientific Resource Scout

Name: Scientific Resource Scout
Author: Activer007

byActiver007

0•

Data Science & ML

Detects and analyzes system hardware to provide strategic architectural recommendations for computationally intensive scientific tasks.

Scientific Resource Scout is a specialized tool designed to bridge the gap between AI code generation and local hardware constraints. By automatically auditing CPU cores, GPU capabilities (NVIDIA, AMD, and Apple Silicon), RAM availability, and disk space, this skill enables Claude to make informed decisions about library selection and processing strategies. Whether you are training models, processing genomics data, or running simulations, it generates a structured JSON report with specific recommendations for parallel processing (Joblib/Dask), out-of-core computing (Zarr), and hardware acceleration (CUDA/MPS), ensuring your code is optimized for your specific environment from the start.

Key Features

01Automated identification of GPU backends including CUDA, ROCm, and Metal

02Cross-platform hardware detection for Windows, macOS (including Apple Silicon), and Linux

03Structured JSON output for programmatic integration into data pipelines

040 GitHub stars

05Strategic recommendations for parallel worker counts and memory management

06Proactive detection of disk space and swap constraints for large-scale operations

Use Cases

01Determining if a large dataset requires Dask/Zarr or can fit into local RAM

02Automatically configuring PyTorch or JAX backends based on detected GPU hardware

03Optimizing multi-process worker counts for high-performance computing simulations

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add activer007/ordinary-claude-skills get-available-resources

For use in Claude.ai and ChatGPT

Download Skill