Why should I run this skill before starting a scientific task?

Running this skill prevents resource exhaustion and system crashes by recommending the best computational strategy based on your actual hardware, such as using chunked data loading for large files.

Does it support GPU detection on Mac?

Yes, it specifically detects Apple Silicon (M1/M2/M3/M4) chips and recommends Metal-accelerated backends like PyTorch MPS or TensorFlow-Metal.

Can I use the output of this skill directly in my code?

Yes, the skill generates a .claude_resources.json file that can be easily loaded into Python scripts to dynamically set parameters like n_jobs or device types.

Are there any external dependencies required?

The underlying detection script requires the 'psutil' Python package, which can be installed via 'uv pip install psutil' or standard pip.

What happens if no GPU is detected?

The skill will identify the lack of GPU acceleration and provide alternative recommendations for CPU-optimized libraries and sequential or parallel processing strategies.

System Resource Profiler

Name: System Resource Profiler
Author: Microck

byMicrock

•

Data Science & ML

Analyzes hardware capabilities and provides strategic recommendations for computationally intensive scientific tasks.

About

The System Resource Profiler is designed to optimize scientific computing workflows by identifying available hardware resources before executing heavy tasks. It automatically detects CPU cores, GPU availability (NVIDIA, AMD, and Apple Silicon), RAM constraints, and disk space to generate a strategic JSON report. This report informs critical architectural decisions, such as whether to use parallel processing with Dask, GPU acceleration with PyTorch, or memory-efficient out-of-core computing, ensuring your code runs efficiently within the limits of your environment.

Key Features

Real-time monitoring of available RAM and disk space with usage thresholds
81 GitHub stars
Comprehensive detection of CPU cores, architecture, and logical processors
Multi-vendor GPU identification for NVIDIA (CUDA), AMD (ROCm), and Apple Silicon (Metal)
Context-aware recommendations for parallel processing libraries like joblib and Dask
Hardware-specific acceleration guidance for machine learning frameworks like PyTorch and JAX

Use Cases

Optimizing worker counts and parallelization strategies for large-scale data processing
Selecting the correct hardware backend (CUDA, ROCm, or MPS) before training machine learning models
Determining if datasets require out-of-core processing techniques based on available memory

About

Key Features

Real-time monitoring of available RAM and disk space with usage thresholds
81 GitHub stars
Comprehensive detection of CPU cores, architecture, and logical processors
Multi-vendor GPU identification for NVIDIA (CUDA), AMD (ROCm), and Apple Silicon (Metal)
Context-aware recommendations for parallel processing libraries like joblib and Dask
Hardware-specific acceleration guidance for machine learning frameworks like PyTorch and JAX

Use Cases

Optimizing worker counts and parallelization strategies for large-scale data processing
Selecting the correct hardware backend (CUDA, ROCm, or MPS) before training machine learning models
Determining if datasets require out-of-core processing techniques based on available memory