Skip to content
TrustedSkillsbeta
Back to all skills

Vllm Deploy Docker

Listed
vllm-project·vlatest·cloud·Apache-2.0

Helps with deployment, Docker as part of deploying and managing cloud infrastructure workflows.

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 vllm-project/vllm-skills --skill vllm-deploy-docker -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

vllm-project

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 failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No pipe-to-shell installers: failed

    Runs code fetched at install time in 1 place.

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

    What it found

    • plugins/vllm-skills/skills/vllm-deploy-docker/SKILL.md:103pip install straight from a URL
      RUN uv pip install --system git+https://github.com/huggingface/transformers.git

  • 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.

  • Pinned to a commit: passed

    Scanned vllm-project/vllm-skills at c99623410c.

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

Commit
c99623410c
Manifest
plugins/vllm-skills/skills/vllm-deploy-docker/SKILL.md
Scanned
1 file, 184 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 allows AI agents to deploy vLLM, a fast and easy-to-use library for LLM inference, within Docker containers. It simplifies the process of setting up and running vLLM models by automating container creation and management. This enables efficient serving of large language models with optimized performance and resource utilization.

When to use it

  • Rapid prototyping: Quickly deploy and test different vLLM configurations without complex manual setup.
  • Scalable inference: Easily scale LLM inference services by leveraging Docker's containerization capabilities.
  • Reproducible environments: Ensure consistent model serving across various platforms with standardized Docker images.
  • Resource optimization: Efficiently manage GPU resources and optimize performance for demanding LLM workloads.

Key capabilities

  • Automated Docker container creation
  • vLLM integration and configuration
  • Simplified deployment process
  • Scalable inference infrastructure

Example prompts

  • "Deploy vLLM with the Llama-2 7B model in a Docker container."
  • "Create a Docker image for vLLM using my custom configuration file."
  • “Run a vLLM inference server inside a Docker container, exposing port 8000.”

Tips & gotchas

Ensure you have Docker installed and running before attempting to use this skill. The specific commands and configurations required may vary depending on your environment and the desired vLLM model.