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Tensorrt Llm

Checked
zechenzhangagi·vlatest·ai-ml·MIT

Provides LLMs guidance and assistance for building AI and machine learning applications.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add orchestra-research/ai-research-skills --skill tensorrt-llm -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

zechenzhangagi

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned zechenzhangagi/ai-research-skills at 773a52944b.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
773a52944b
Manifest
12-inference-serving/tensorrt-llm/SKILL.md
Scanned
4 files, 1,097 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill leverages TensorRT LLM to optimize and accelerate large language model inference. It allows for faster generation speeds and reduced latency compared to standard LLM deployments, particularly beneficial for resource-constrained environments or real-time applications. The skill focuses on deploying and running optimized LLMs using NVIDIA's TensorRT technology.

When to use it

  • Real-time chatbots: Reduce response times in conversational AI applications where low latency is crucial.
  • Edge deployment: Run large language models efficiently on devices with limited computational resources, like embedded systems or mobile phones.
  • High-throughput inference: Increase the number of requests a model can handle per second for services requiring significant scale.
  • Model experimentation: Quickly test and evaluate different LLM configurations and optimizations using TensorRT's capabilities.

Key capabilities

  • LLM Inference Optimization
  • TensorRT Integration
  • Reduced Latency
  • Faster Generation Speed
  • Edge Deployment Support

Example prompts

  • "Run the optimized Llama-2 model for this prompt: 'Write a short story about a cat.'"
  • "Deploy the Falcon-7B model using TensorRT and measure its inference speed."
  • "Generate text with the Mistral-7B model, prioritizing low latency."

Tips & gotchas

  • Requires an NVIDIA GPU for optimal performance. TensorRT is designed to leverage NVIDIA hardware acceleration.