Agent skill · research science · aiskillstore
get-available-resources
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.
Why this skill is useful
Generates a JSON file with detailed resource information and strategic recommendations that the AI wouldn't reliably generate on its own.
What it needs
Requires python installed locally. About 5k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
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 "Train a neural network on this data" → Use this skill to detect available GPUs and backends "Process 10,000 files in parallel" → Use this skill to determine optimal worker count "Run a computationally intensive simulation" → Use this skill to understand resource constraints How This Skill Works Resource Detection The skill runs scripts/detectresources.py to automatically detect: 1. CPU Information Physical and logical core counts Processor architecture and model CPU frequency information 2. GPU Information NVIDIA GPUs: Detects via nvidia-smi, reports VRAM, driver version, compute capability AMD GPUs: Detects via rocm-smi Apple Silicon: Detects M1/M2/M3/M4 chips with Metal support and unified memory 3. Memory Information Total and available RAM Current memory usage percentage Swap space availability 4. Disk Space Information Total and available disk space for working directory Current usage percentage 5. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/get-available-resources