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Found 2 Skills
Use when designing or reviewing concurrent Python code — selecting between asyncio, threads, or multiprocessing; structuring cancellation and deadline propagation; bounding fan-out and backpressure. Also use when diagnosing race conditions, deadlocks, slow throughput, or thread/task leaks under load.
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.