cuml-machine-learning

The skill description and code excerpts present a coherent, purpose-aligned tool for GPU-accelerated machine learning using cuML with CPU fallbacks. There are no evident security risks such as credential handling, data exfiltration, or supply-chain mechanisms within the provided content. Trustworthiness hinges on external installation paths for cuML/cuDF (typically via official registries and NVIDIA-/ RAPIDS-provided channels); if those installations use trusted sources, the risk remains low. Overall, the footprint is benign and proportionate to the stated purpose.