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.
74
63%
Does it follow best practices?
Impact
91%
2.84xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./scientific-skills/get-available-resources/SKILL.mdResource-aware parallel processing
psutil via uv
0%
100%
Runs detect_resources.py
0%
100%
.claude_resources.json created
100%
100%
Reads suggested_workers
0%
90%
Parallel library used
100%
70%
Detection before processing
66%
100%
n_jobs from resources
50%
90%
Handles sequential fallback
0%
100%
Memory-adaptive data loading strategy
Runs detect_resources.py
0%
33%
.claude_resources.json used
0%
100%
Memory threshold comparison
0%
100%
Dask for memory-constrained
0%
100%
Pandas for memory-abundant
80%
100%
Disk strategy applied
20%
53%
Dynamic branching
66%
100%
GPU-aware training setup
Runs detect_resources.py
0%
100%
Reads available_backends
0%
100%
CUDA device selection
46%
100%
Metal device selection
46%
100%
CPU fallback
100%
100%
CUDA library suggestion
90%
80%
Metal library suggestion
90%
80%
Verbose flag used
0%
100%
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Table of Contents
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.