Scientific Get Available Resources

What This Does

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.

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Quick Start

Step 1: Create a Project Folder

mkdir -p ~/Projects/get-available-resources

Step 2: Download the Template

Click Download above, then:

mv ~/Downloads/CLAUDE.md ~/Projects/get-available-resources/

Step 3: Start Claude Code

cd ~/Projects/get-available-resources
claude

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How This Skill Works

Resource Detection

The skill runs scripts/detect_resources.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. 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-optimized 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:

python scripts/detect_resources.py

Optional arguments:

• -o, --output <path>: Specify custom output path (default: .claude_resources.json)
• -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 code
import json

with open('.claude_resources.json', 'r') as f:
    resources = json.load(f)

# Check parallel processing strategy
if 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 strategy
if resources['recommendations']['memory_strategy']['strategy'] == 'memory_constrained':
    # Use Dask, Zarr, or H5py for out-of-core processing
    import dask.array as da
    # Load data in chunks

# Check GPU availability
if 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 = 10

if dataset_size_gb > memory_available_gb * 0.5:
    # Dataset is large relative to memory, use Dask
    import dask.dataframe as dd
    df = dd.read_csv('large_file.csv')
else:
    # Dataset fits in memory, use pandas
    import 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
)

For GPU acceleration:

import torch

if 'CUDA' in resources['gpu']['available_backends']:
    device = torch.device('cuda')
elif 'Metal' in resources['gpu']['available_backends']:
    device = torch.device('mps')
else:
    device = torch.device('cpu')

model = model.to(device)

Dependencies

The detection script requires the following Python packages:

uv pip install psutil

All other functionality uses Python standard library modules (json, os, platform, subprocess, sys, pathlib).

Platform Support

• macOS: Full support including Apple Silicon (M1/M2/M3/M4) GPU detection
• Linux: Full support including NVIDIA (nvidia-smi) and AMD (rocm-smi) GPU detection
• Windows: Full support including NVIDIA GPU detection

Best Practices

1. Run early: Execute resource detection at the start of projects or before major computational tasks
2. Re-run periodically: System resources change over time (memory usage, disk space)
3. Check before scaling: Verify resources before scaling up parallel workers or data sizes
4. Document decisions: Keep the .claude_resources.json file in project directories to document resource-aware decisions
5. Use with versioning: Different machines have different capabilities; resource files help maintain portability

Troubleshooting

GPU not detected:

• Ensure GPU drivers are installed (nvidia-smi, rocm-smi, or system_profiler for Apple Silicon)
• Check that GPU utilities are in system PATH
• Verify GPU is not in use by other processes

Script execution fails:

• Ensure psutil is installed: uv pip install psutil
• Check Python version compatibility (Python 3.6+)
• Verify script has execute permissions: chmod +x scripts/detect_resources.py

Inaccurate memory readings:

• Memory readings are snapshots; actual available memory changes constantly
• Close other applications before detection for accurate "available" memory
• Consider running detection multiple times and averaging results