How do I install the Nvidia Tensorrt LLM Deployment Review skill?

Run npx skillmds@latest add vincentchuwaichow/nvidia-tensorrt-llm-deployment-review in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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Is Nvidia Tensorrt LLM Deployment Review free to use?

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Who published Nvidia Tensorrt LLM Deployment Review?

VincentChuWaiChow (@vincentchuwaichow) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Nvidia Tensorrt LLM Deployment Review by vincentchuwaichow

Name: Nvidia Tensorrt LLM Deployment Review
Author: VincentChuWaiChow

Nvidia Tensorrt LLM Deployment Review

Use this skill when reviewing TensorRT or TensorRT-LLM deployment artifacts statically — ONNX/PyTorch export pipelines, precision selection (FP16/BF16/INT8/FP8/INT4), calibration cache integrity, dynamic shape profiles, custom plugin loading, engine cache and serialized engine provenance, runtime memory pool sizing. Trigger when the user asks whether a TensorRT build script, calibration pipeline, or trtexec invocation follows NVIDIA's published guidance.

File contents

NVIDIA TensorRT-LLM Deployment Review

Purpose

Static review of TensorRT and TensorRT-LLM deployment pipelines against NVIDIA's TensorRT Developer Guide — ONNX/PyTorch export, FP16/INT8/FP8/INT4 precision, calibration data integrity, dynamic shape profiles, plugin trust boundaries, engine cache provenance. This skill is doc-anchored: it grounds review findings in NVIDIA's published documentation rather than in a certification blueprint, because no NVIDIA certification currently covers this developer-facing surface as a standalone exam objective.

Lean operating rules

Prefer the user's actual TensorRT build scripts, ONNX export code, and calibration pipelines as evidence; otherwise fall back to documentation-based inference.
Treat custom TensorRT plugins loaded from non-pinned sources or unsigned object files as a critical finding — native-code execution surface inside the inference engine.
Treat serialized engines (.engine, .plan) distributed without sha256 verification or provenance attestation as a high finding — silent model substitution.
Treat INT8 / FP8 calibration data containing production user traffic without redaction or retention controls as a high finding — confidentiality and PII surface.
Treat absence of optimization_profiles for variable input shapes as a medium finding — builds either fail at runtime or fall back to padded inference.
Treat hardcoded --workspace or --memory-pool-size values that exceed the deployment GPU's free memory as a medium finding — engine build will OOM in CI.
Treat use of --strict-types without explicit precision tagging on every layer as a low finding — actual precision drifts from intent.
Always emit the exact trtexec, polygraphy run, or tensorrt_llm/build.py commands the user should run — do not execute them.

Response minimum

Return, at minimum:

the scoped target (model source and export pipeline, precision selection and calibration posture, dynamic shape and profile posture, plugin and engine provenance posture, runtime memory and concurrency posture, recommended trtexec/polygraphy invocations) and evidence level,
findings labelled critical / high / medium / low,
recommended NVIDIA-tooling invocations the user should run themselves,
safe next actions and assumptions or blockers.

---name: nvidia-tensorrt-llm-deployment-reviewdescription: Use this skill when reviewing TensorRT or TensorRT-LLM deployment artifacts statically — ONNX/PyTorch export pipelines, precision selection (FP16/BF16/INT8/FP8/INT4), calibration cache integrity, dynamic shape profiles, custom plugin loading, engine cache and serialized engine provenance, runtime memory pool sizing. Trigger when the user asks whether a TensorRT build script, calibration pipeline, or trtexec invocation follows NVIDIA's published guidance.---# NVIDIA TensorRT-LLM Deployment Review## PurposeStatic review of TensorRT and TensorRT-LLM deployment pipelines against NVIDIA's TensorRT Developer Guide — ONNX/PyTorch export, FP16/INT8/FP8/INT4 precision, calibration data integrity, dynamic shape profiles, plugin trust boundaries, engine cache provenance. This skill is doc-anchored: it grounds review findings in NVIDIA's published documentation rather than in a certification blueprint, because no NVIDIA certification currently covers this developer-facing surface as a standalone exam objective.## Lean operating rules- Prefer the user's actual TensorRT build scripts, ONNX export code, and calibration pipelines as evidence; otherwise fall back to documentation-based inference.- Treat custom TensorRT plugins loaded from non-pinned sources or unsigned object files as a critical finding — native-code execution surface inside the inference engine.- Treat serialized engines (`.engine`, `.plan`) distributed without sha256 verification or provenance attestation as a high finding — silent model substitution.- Treat INT8 / FP8 calibration data containing production user traffic without redaction or retention controls as a high finding — confidentiality and PII surface.- Treat absence of `optimization_profiles` for variable input shapes as a medium finding — builds either fail at runtime or fall back to padded inference.- Treat hardcoded `--workspace` or `--memory-pool-size` values that exceed the deployment GPU's free memory as a medium finding — engine build will OOM in CI.- Treat use of `--strict-types` without explicit precision tagging on every layer as a low finding — actual precision drifts from intent.- Always emit the exact `trtexec`, `polygraphy run`, or `tensorrt_llm/build.py` commands the user should run — do not execute them.## Response minimumReturn, at minimum:- the scoped target (model source and export pipeline, precision selection and calibration posture, dynamic shape and profile posture, plugin and engine provenance posture, runtime memory and concurrency posture, recommended trtexec/polygraphy invocations) and evidence level,- findings labelled critical / high / medium / low,- recommended NVIDIA-tooling invocations the user should run themselves,- safe next actions and assumptions or blockers.

Frequently asked questions