Get Available Resources

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')