Tensorrt Llm
CheckedProvides LLMs guidance and assistance for building AI and machine learning applications.
Install on your platform
Showing Claude Code. One command installs this skill into any of the agents below.
- 1
Run this in your project
terminal$ npx skills add orchestra-research/ai-research-skills --skill tensorrt-llm -a claude-code Uses the open-source
skillsCLI and needs Node.js. Add-gto install it for your user instead of this project.
What we know about this skill
Publisher
orchestra-research
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Install target
Live repository
Not pinned. Installing fetches whatever the repository holds at the time you run the command.
Automated safety checks
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
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No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.
Pinned to a commit: passed
Scanned orchestra-research/ai-research-skills at 773a52944b.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.
- Commit
- 773a52944b
- Scanned
- 4 files, 1,097 lines
About this skill
What it does
This skill enables AI agents to deploy and manage Large Language Models (LLMs) using NVIDIA TensorRT-LLM, optimizing inference speed and resource efficiency. It streamlines the integration of high-performance model serving directly into orchestration workflows for production-grade applications.
When to use it
- Deploying LLMs that require low-latency inference in edge or cloud environments.
- Optimizing existing model pipelines to reduce memory footprint and increase throughput.
- Integrating NVIDIA-accelerated AI models into automated agent workflows via the Orchestrate platform.
- Managing the lifecycle of TensorRT-optimized models without manual infrastructure configuration.
Key capabilities
- Direct deployment of LLMs using the NVIDIA TensorRT-LLM engine.
- Automatic optimization for inference performance and hardware utilization.
- Seamless integration within the Orchestra AI agent framework.
- Simplified model serving setup for production environments.
Example prompts
- "Deploy the latest Llama 3 model using TensorRT-LLM optimized settings through this skill."
- "Configure an inference pipeline that prioritizes low latency for a customer support chatbot."
- "Optimize my current text generation workflow by switching to NVIDIA TensorRT-LLM backend."
Tips & gotchas
Ensure your environment supports NVIDIA GPUs, as TensorRT-LLM relies on CUDA for maximum performance gains. While this skill handles deployment and optimization, verify model compatibility with the specific TensorRT version being used before running production workloads.