Software Development Skills

Load and run a TensorRT engine (.plan / .engine) from C++ using the TensorRT 11 / 10.x modern Runtime API, avoiding the deprecated TRT 8.x binding-index APIs that older guidance still promotes. Use whenever the user asks about loading or running a TensorRT .plan/.engine from C++, even on "minimal example" requests — without this skill the default reply uses deprecated enqueueV2-style code. Also use when the user hits "Engine plan file is generated on an incompatible device", deserializeCudaEngine returns nullptr, gets an enqueueV2 / IStreamReader deprecation warning, or wants to stream a .plan via IStreamReaderV2. Triggers: TensorRT C++ inference, load TensorRT plan C++, run .plan from C++, IRuntime example, deserializeCudaEngine, enqueueV3, enqueueV2 deprecated, setTensorAddress, getBindingIndex, IStreamReaderV2, libnvinfer C++. NOT for building engines (trt-onnx-quickstart), Python deploy, plugins, multi-GPU.

Build and verify a TensorRT engine from a Hugging Face model ID or ONNX file, with numerical parity checked against ONNX Runtime. Use when the user imports a non-LLM model to TensorRT, needs a verified engine from ONNX, hits trtexec "unsupported operator", must verify the engine matches ONNX numerically, debugs a polygraphy parity failure (large max abs diff at FP16), or configures multi-input dynamic shapes. Triggers: convert ONNX to TensorRT, Hugging Face to TensorRT, trtexec onnx, trtexec unsupported operator, optimum-cli export, polygraphy parity check, polygraphy run --trt --onnxrt, parity check failed, max abs diff, verify engine matches ONNX, --minShapes, dynamic shapes trtexec, multi-input shape profile, FP16 engine, INT64 warning. Adjacent skills: trt-torch-quickstart (PyTorch frontend), trt-cpp-runtime-quickstart (C++ engine load). LLM token generation belongs in TensorRT-LLM, not here.

Validate and analyze TensorRT performance data from paired layer-info JSON and profile/latency JSON files. Use when asked to inspect TensorRT, TRT, torch-tensorrt, or ONNX-TensorRT perf reports, verify that layer/profile JSON files are valid and from the same model, infer basic model information, find likely fusion or latency optimization opportunities, and produce a concise Markdown performance report or structured JSON data.

Migrate a TensorRT build from weak typing (deprecated 10.12, removed 11.0) to strong typing — across Python INetworkDefinition builders, the trtexec CLI, and C++ builder code. Use when a TRT 11 upgrade breaks a weakly-typed build. Triggers: weakly typed to strongly typed, kSTRONGLYTYPED, weak typing deprecated, kFP16/kINT8 removed, setPrecision rejected, setComputePrecision deprecated, do I still need --stronglyTyped, how to add the kSTRONGLYTYPED flag, ModelOpt autocast, INT8 on TRT 11. NOT for ONNX import (trt-onnx-quickstart), Torch-TRT (trt-torch-quickstart), or C++ deploy (trt-cpp-runtime-quickstart).

Compile a PyTorch model to a TensorRT engine via Torch-TensorRT — AOT or JIT — under the new strong-typing default. Use when the user compiles PyTorch to TensorRT without ONNX, hits "enabledprecisions should not be used when useexplicittyping=True", sees Dynamo graph breaks or PyTorch fallback, debugs ABI errors at import torchtensorrt, or needs the compatible torch / torchtensorrt / tensorrt-cu13 version pins for TensorRT 11. Triggers: torchtensorrt, torchtensorrt.dynamo.compile, torch.compile backend torchtensorrt, pytorch to tensorrt, ExportedProgram, Dynamo graph break, useexplicittyping, enabledprecisions, torchtensorrt.Input, minblocksize, truncatedouble, tensorrt-cu13, version pinning, version compatibility. Adjacent skills: trt-onnx-quickstart, trt-cpp-runtime-quickstart. LLM token generation belongs in TensorRT-LLM.