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Vllm Deployment

Checked
stakpak·vlatest·ai-ml·MIT

Automates vLLM model deployment and scaling across infrastructure for optimized performance and resource utilization.

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 stakpak/community-paks --skill vllm-deployment -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

stakpak

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 stakpak/community-paks at d9bbde9885.

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

Commit
d9bbde9885
Manifest
vllm-deployment/SKILL.md
Scanned
1 file, 223 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 facilitates the deployment of large language models (LLMs) using the vLLM inference engine. It automates the process of setting up and configuring a vLLM server, allowing for efficient and scalable LLM serving with features like optimized throughput and reduced latency. The skill handles tasks such as downloading model weights and managing hardware resources.

When to use it

  • You need to serve an LLM in production and require high throughput.
  • You want to experiment with different LLMs without manually configuring infrastructure.
  • You're looking for a simplified way to deploy vLLM on your existing infrastructure.
  • You are developing an application that requires low-latency responses from an LLM.

Key capabilities

  • Automated vLLM server setup and configuration
  • Model weight downloading and management
  • Hardware resource allocation
  • Optimized inference throughput
  • Reduced latency for LLM responses

Example prompts

  • "Deploy the Llama-2 7B model using vLLM."
  • "Set up a vLLM server with 8 GPUs."
  • “Download and serve Mistral 7B via vLLM.”

Tips & gotchas

Ensure you have sufficient hardware resources (GPUs) available to support the LLM being deployed. The skill's performance is directly tied to the underlying infrastructure’s capabilities.