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Tensorrt Llm

Checked
ovachiever·vlatest·ai-ml

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 ovachiever/droid-tings --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

ovachiever

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 ovachiever/droid-tings at 7acd12a754.

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

Commit
7acd12a754
Manifest
skills/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

The ovachiever-tensorrt-llm skill optimizes large language model (LLM) inference using NVIDIA TensorRT. It allows for significantly faster LLM processing by leveraging hardware acceleration and optimized kernels. This results in reduced latency and increased throughput when running LLMs, particularly beneficial for real-time applications or resource-constrained environments.

When to use it

  • Accelerating chatbot responses: Reduce the time it takes for a chatbot to generate replies, improving user experience.
  • Real-time text generation: Enable faster content creation and processing in scenarios like code completion or automated writing tools.
  • Deploying LLMs on edge devices: Run large language models efficiently on devices with limited computational resources.
  • Batch processing of text data: Speed up the analysis and transformation of large volumes of textual information.

Key capabilities

  • LLM inference optimization
  • NVIDIA TensorRT integration
  • Hardware acceleration
  • Reduced latency
  • Increased throughput

Example prompts

  • "Optimize this LLM for faster response times."
  • "Deploy this language model using TensorRT."
  • "Can you accelerate the processing of this text generation task?"

Tips & gotchas

  • Requires an NVIDIA GPU with TensorRT support.
  • Performance gains depend on the specific LLM and hardware configuration.