Why should I run this before data analysis?

It helps determine if your dataset fits in memory or requires 'out-of-core' libraries like Dask or Zarr based on current system constraints.

How do I use the output of the resource detection?

The skill creates a .claude_resources.json file that can be read by your scripts to automatically set worker counts, device backends, or data loading strategies.

Does it support Apple Silicon Macs?

Yes, it provides full support for M1 through M4 chips, detecting Metal support and unified memory for optimized processing.

What dependencies are required to use this skill?

The detection script requires the 'psutil' Python package, while the rest of the functionality relies on the Python standard library.

What hardware can this skill detect?

It identifies CPU cores and architecture, RAM availability, disk space, and specific GPUs including NVIDIA CUDA, AMD ROCm, and Apple Silicon Metal.

Scientific Resource Detector

Name: Scientific Resource Detector
Author: BbgnsurfTech

byBbgnsurfTech

•

Data Science & ML

Detects system hardware capabilities to provide strategic recommendations for computationally intensive scientific tasks.

About

The Scientific Resource Detector is a specialized skill designed to profile your computational environment before beginning heavy data science or machine learning workloads. It automatically identifies CPU cores, GPU backends (including NVIDIA, AMD, and Apple Silicon), memory availability, and disk space to generate a strategic JSON report. This report helps Claude and the user decide on the most efficient processing architecture, such as choosing between parallelization with Dask, GPU acceleration with PyTorch, or memory-efficient out-of-core computing.

Key Features

Cross-platform GPU identification for NVIDIA (CUDA), AMD (ROCm), and Apple Silicon (Metal)
3 GitHub stars
Automated detection of CPU cores, architecture, and logical processors
Generates standardized JSON reports for automated workflow configuration
Intelligent strategic recommendations for parallel processing and memory management
Real-time memory and disk space profiling with usage snapshots

Use Cases

Identifying the optimal hardware backend (MPS, CUDA, or CPU) for training neural networks
Configuring the ideal number of worker processes for high-throughput simulations
Determining whether to load large datasets into memory or use Dask for chunked processing

About

Key Features

Cross-platform GPU identification for NVIDIA (CUDA), AMD (ROCm), and Apple Silicon (Metal)
3 GitHub stars
Automated detection of CPU cores, architecture, and logical processors
Generates standardized JSON reports for automated workflow configuration
Intelligent strategic recommendations for parallel processing and memory management
Real-time memory and disk space profiling with usage snapshots

Use Cases

Identifying the optimal hardware backend (MPS, CUDA, or CPU) for training neural networks
Configuring the ideal number of worker processes for high-throughput simulations
Determining whether to load large datasets into memory or use Dask for chunked processing