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.
SKILL.md
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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
Installs
0
The skill runs scripts/detect_resources.py to automatically detect:
-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
Run early: Execute resource detection at the start of projects or before major computational tasks
Re-run periodically: System resources change over time (memory usage, disk space)
Check before scaling: Verify resources before scaling up parallel workers or data sizes
Document decisions: Keep the .claude_resources.json file in project directories to document resource-aware decisions
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
