{
  "id": "nvidia-tensorrt-llm-deployment-review",
  "name": "NVIDIA TensorRT-LLM Deployment Review",
  "type": "skill",
  "provider": "nvidia",
  "harnesses": [
    "codex",
    "copilot",
    "claude-code",
    "cursor",
    "gemini",
    "kiro"
  ],
  "summary": "Doc-anchored static review of TensorRT and TensorRT-LLM deployment pipelines against the NVIDIA TensorRT Developer Guide and TensorRT-LLM documentation — ONNX/PyTorch export, precision selection, calibration integrity, dynamic shapes, plugin trust boundaries, engine cache provenance.",
  "source_type": "original",
  "official_docs": [
    "https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/",
    "https://docs.nvidia.com/deeplearning/tensorrt/quick-start-guide/",
    "https://docs.nvidia.com/deeplearning/tensorrt/best-practices/",
    "https://docs.nvidia.com/deeplearning/tensorrt-llm/",
    "https://docs.nvidia.com/deeplearning/tensorrt/api/"
  ],
  "security_notes": "TensorRT custom plugins load arbitrary native code into the inference process; any plugin pulled from a non-vetted source is an RCE primitive. Serialized TensorRT engines (`.engine`, `.plan`) are not signed by default — silent substitution of an engine yields silent model substitution. INT8 calibration data is unredacted production traffic by definition and is a confidentiality risk if it leaks. The skill never executes `trtexec`, `polygraphy`, or `tensorrt_llm/build.py` — it outputs the recommended invocation as text.",
  "last_verified": "2026-05-10",
  "path": "skills/nvidia/nvidia-tensorrt-llm-deployment-review/",
  "category": "platform",
  "certifications": [],
  "author": "github: VincentChuWaiChow",
  "version": "0.1.0"
}