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.
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
{"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
: Verify sufficient disk space and appropriate storage strategies
Before large file operations
At project initialization: Understand baseline capabilities for making architectural decisions
Example scenarios:
"Help me analyse 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/detect_resources.py to automatically detect:
Apple Silicon: Detects M1/M2/M3/M4 chips with Metal support and unified memory
Memory Information
Total and available RAM
Current memory usage percentage
Swap space availability
Disk Space Information
Total and available disk space for working directory
Current usage percentage
Operating System Information
OS type (macOS, Linux, Windows)
OS version and release
Python version
Output Format
The skill generates a .claude_resources.json file in the current working directory containing:
{"timestamp":"2025-10-23T10:30:00","os":{"system":"Darwin","release":"25.0.0","machine":"arm64"},"cpu":{"physical_cores":8,"logical_cores":8,"architecture":"arm64"},"memory":{"total_gb":16.0,"available_gb":8.5,"percent_used":46.9},"disk":{"total_gb":500.0,"available_gb":200.0,"percent_used":60.0},"gpu":{"nvidia_gpus":[],"amd_gpus":[],"apple_silicon":{"name":"Apple M2","type":"Apple Silicon","backend":"Metal","unified_memory":true},"total_gpus":1,"available_backends":["Metal"]},"recommendations":{"parallel_processing":{"strategy":"high_parallelism","suggested_workers":6,"libraries":["joblib","multiprocessing","dask"]},"memory_strategy":{"strategy":"moderate_memory","libraries":["dask","zarr"],"note":"Consider chunking for datasets > 2GB"},"gpu_acceleration":{"available":true,"backends":["Metal"],"suggested_libraries":["pytorch-mps","tensorflow-metal","jax-metal"]},"large_data_handling":{"strategy":"disk_abundant","note":"Sufficient space for large intermediate files"}}}
Strategic Recommendations
The skill generates context-aware recommendations:
Parallel Processing Recommendations:
High parallelism (8+ cores): Use Dask, joblib, or multiprocessing with workers = cores - 2
Moderate parallelism (4-7 cores): Use joblib or multiprocessing with workers = cores - 1
Sequential (< 4 cores): Prefer sequential processing to avoid overhead
Memory Strategy Recommendations:
Memory constrained (< 4GB available): Use Zarr, Dask, or H5py for out-of-core processing
Moderate memory (4-16GB available): Use Dask/Zarr for datasets > 2GB
Memory abundant (> 16GB available): Can load most datasets into memory directly
GPU Acceleration Recommendations:
NVIDIA GPUs detected: Use PyTorch, TensorFlow, JAX, CuPy, or RAPIDS
AMD GPUs detected: Use PyTorch-ROCm or TensorFlow-ROCm
Apple Silicon detected: Use PyTorch with MPS backend, TensorFlow-Metal, or JAX-Metal
No GPU detected: Use CPU-optimised libraries
Large Data Handling Recommendations:
Disk constrained (< 10GB): Use streaming or compression strategies
Moderate disk (10-100GB): Use Zarr, H5py, or Parquet formats
Disk abundant (> 100GB): Can create large intermediate files freely
Usage Instructions
Step 1: Run Resource Detection
Execute the detection script at the start of any computationally intensive task:
-v, --verbose: Print full resource information to stdout
Step 2: Read and Apply Recommendations
After running detection, read the generated .claude_resources.json file to inform computational decisions:
# Example: Use recommendations in codeimport json
withopen('.claude_resources.json', 'r') as f:
resources = json.load(f)
# Check parallel processing strategyif resources['recommendations']['parallel_processing']['strategy'] == 'high_parallelism':
n_jobs = resources['recommendations']['parallel_processing']['suggested_workers']
# Use joblib, Dask, or multiprocessing with n_jobs workers# Check memory strategyif resources['recommendations']['memory_strategy']['strategy'] == 'memory_constrained':
# Use Dask, Zarr, or H5py for out-of-core processingimport dask.array as da
# Load data in chunks# Check GPU availabilityif resources['recommendations']['gpu_acceleration']['available']:
backends = resources['recommendations']['gpu_acceleration']['backends']
# Use appropriate GPU library based on available backend
Step 3: Make Informed Decisions
Use the resource information and recommendations to make strategic choices:
For data loading:
memory_available_gb = resources['memory']['available_gb']
dataset_size_gb = 10if dataset_size_gb > memory_available_gb * 0.5:
# Dataset is large relative to memory, use Daskimport dask.dataframe as dd
df = dd.read_csv('large_file.csv')
else:
# Dataset fits in memory, use pandasimport pandas as pd
df = pd.read_csv('large_file.csv')
For parallel processing:
from joblib import Parallel, delayed
n_jobs = resources['recommendations']['parallel_processing'].get('suggested_workers', 1)
results = Parallel(n_jobs=n_jobs)(
delayed(process_function)(item) for item in data
)
