model-deployment-profiler

model-deployment-profiler is an MLOps & model serving skill for AI agents, published by KeploreAI-Lab in the MindAct repository on GitHub. Skillspace rates model-deployment-profiler D (66/100) and it passed the safety scan; the rating is based on static analysis only.

Use this skill whenever deploying a trained model to production. This skill clarifies hardware environment (CUDA GPU, TensorRT, Jetson, CPU-only), model format (PT, ONNX, TensorRT engine, TFLite), precision (FP32, FP16, INT8), and inference speed requirements (FPS target, latency deadline in ms). It then generates a deployment wrapper with a profiling switch that breaks down timing into: preprocessing (image read, resize, channel conversion, normalization), inference, and postprocessing (mask binarization, coordinate rescaling, result sorting, confidence filtering). Trigger when the user says: "deploy the model", "run in production", "inference speed", "TensorRT", "ONNX", "FP16", "how fast is the model", "latency requirement", "preprocessing time", "postprocessing time", "timing breakdown", "benchmark inference", or when asking about running a model on specific hardware.

Grade

D

Quality

66 / 100

Safety

Passed

GitHub stars

85

Unproven: rated by static analysis only, not yet compared head-to-head.

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