Get Available Resources

---
name: get-available-resources
description: This skill should be used at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs, memory, disk space). It creates a JSON file with resource information and strategic recommendations that inform computational approach decisions such as whether to use parallel processing (joblib, multiprocessing), out-of-core computing (Dask, Zarr), GPU acceleration (PyTorch, JAX), or memory-efficient strategies. Use this skill before running analyses, training models, processing large datasets, or any task where resource constraints matter.
license: MIT license
metadata: {"version": "1.0", "skill-author": "K-Dense Inc."}
---

# Get Available Resources

## Overview

Detect available computational resources and generate strategic recommendations for scientific computing tasks. This skill automatically identifies CPU capabilities, GPU availability (NVIDIA CUDA, AMD ROCm, Apple Silicon Metal), memory constraints, and disk space to help make informed decisions about computational approaches.

## When to Use This Skill

Use this skill proactively before any computationally intensive task:

- **Before data analysis**: Determine if datasets can be loaded into memory or require out-of-core processing
- **Before model training**: Check if GPU acceleration is available and which backend to use
- **Before parallel processing**: Identify optimal number of workers for joblib, multiprocessing, or Dask
- **Before large file operations**: Verify sufficient disk space and appropriate storage strategies
- **At project initialization**: Understand baseline capabilities for making architectural decisions

**Example scenarios:**
- "Help me analyze this 50GB genomics dataset" → Use this skill first to determine if Dask/Zarr are needed… load the full skill through Skill Me