TensorRT Wrapper

Development Updated 2026.08.30

Paste the following prompt into your AI chat to install this skill:

Please install @user_922b1001/tensorrt-wrap according to https://skillhub.cn/install/skillhub.md

About this skill

Problem context

TensorRT often appears in inference deployment pipelines, handling model conversion and runtime acceleration. When scripts, CI systems, or toolchains call TensorRT directly, details such as command assembly, environment checks, and output handling can leak into application code. The name Tensorrt Wrap and the tool, github, and automation tags suggest an engineering-facing wrapper rather than a complete end-user platform.

Working model and limits

The available description is limited, so the skill should be read as a wrapper around TensorRT. Specific parameters, model formats, target platforms, and runtime dependencies are not detailed. It fits cases where TensorRT-related operations are embedded in a github workflow, local script, or automation task. It should not be assumed to provide batch conversion, service deployment, performance tuning, or multi-backend management without additional documentation.

Use Cases

Run TensorRT-related commands inside GitHub workflows and reduce hand-written script details.
Wrap TensorRT as a tool entry point for local debugging or repeated automation tasks.
Keep TensorRT-related steps centralized in a development environment instead of scattering them through business code.
Lightly wrap TensorRT operations in GitHub tasks to form a reusable command entry point.

Best For

Engineers maintaining GitHub Actions who need to wrap TensorRT-related commands into automation.
Backend engineers building inference tooling who want fewer hand-written TensorRT call details.
Platform engineers keeping CI/CD scripts tidy who need a reusable entry point for TensorRT operations.
Research engineers debugging inference pipelines locally who need a quick TensorRT tool entry point.