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.
8.7
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0
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Tools & Utilities
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Excellent skill with comprehensive documentation. The description clearly conveys when and why to use this skill, making it trivial for a CLI agent to invoke appropriately. The documentation provides complete task knowledge including output format, strategic recommendation logic, and concrete usage examples across multiple scenarios (data loading, parallel processing, GPU acceleration). Structure is very clear with logical sections, though it's somewhat lengthy for a single SKILL.md file - minor improvement would be moving some detailed examples to separate files. Novelty is strong: detecting system resources and generating contextual computational strategy recommendations is genuinely useful and would require significant tokens/complexity for a CLI agent to implement from scratch. The skill intelligently bridges the gap between hardware capabilities and software library selection (Dask vs pandas, CUDA vs Metal, etc.), providing actionable decision-making support that goes beyond simple resource reporting. Minor points: the skill is somewhat straightforward conceptually (resource detection is well-established), but the strategic recommendations layer adds meaningful value.
davila7
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