Get Available Resources

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

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: