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
The 'get-available-resources' skill is designed to assist in detecting and reporting the computational resources available on a system prior to initiating any computationally intensive scientific tasks. By identifying key resources such as CPU cores, GPUs, memory, and disk space, this skill helps users make informed decisions regarding their computational strategies. This is particularly crucial for tasks that require specific resource constraints, ensuring that analyses and computations are optimized based on the available hardware capabilities.
Run the skill at the beginning of any computational task to detect and report system resources.
The skill supports NVIDIA GPUs, AMD GPUs, and Apple Silicon GPUs.
The skill requires a compatible operating system (macOS, Linux, Windows) and Python.
The skill generates a JSON file named '.claude_resources.json' containing detailed resource information.
Yes, it provides recommendations for memory-efficient strategies based on the detected memory constraints.
Quick Setup:
.claude/skills/Repository
K-Dense-AI/claude-scientific-skills