New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Vllm Deploy Simple

by vllm-project in vllm-project/vllm-skills

Quick install and deploy vLLM, start serving with a simple LLM, and test OpenAI API.

Apache-2.0Auto-check passedAI & LLM Engineering

Install Vllm Deploy Simple

skills CLI
$ npx skills add vllm-project/vllm-skills --skill vllm-deploy-simple -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install vllm-project/vllm-skills vllm-deploy-simple --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/vllm-project/vllm-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/vllm-skills/skills/vllm-deploy-simple .claude/skills/vllm-deploy-simple && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
vllm-deploy-simple
GitHub stars
103
Token cost
~1.6k tokens
SKILL.md length
555 words
Files
2 (incl. scripts)
Skills in repo
6
Repo updated
First seen
Licence
Apache-2.0

At a glance

Quick install and deploy vLLM, start serving with a simple LLM, and test OpenAI API.

  • Works in 7 steps: Activate the virtual environment (if… → Detect hardware backend… → Install vLLM with appropriate backend… → …
  • Tasks that involve LLM inference and serving
  • SKILL.md covers What this skill does, Prerequisites, Usage and Configuration, plus 3 more sections
  • Runs Shell scripts from its folder; calls curl, uv and python3

What it does

Vllm Deploy Simple is an agent skill from vllm-project/vllm-skills. Quick install and deploy vLLM, start serving with a simple LLM, and test OpenAI API.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including scripts (for example `scripts/quickstart.sh`).

It sits in AI & LLM Engineering, covering LLM inference and serving. It works with vLLM, OpenAI, Python and NVIDIA AI Platform. The repository describes itself as: Agent skills for vLLM. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve LLM inference and serving

Example prompts

  • “/vllm-deploy-simple”

Requirements

  • Python 3
  • A Bash shell

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. Activate the virtual environment (if specified)
  2. Detect hardware backend (CUDA/ROCm/TPU/CPU)
  3. Install vLLM with appropriate backend support
  4. Start the vLLM server in the background
  5. Wait for the server to be ready
  6. Test the API with a sample request
  7. Display the server status

What it can do on your machine

Read from SKILL.md and the folder at commit c996234. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Shell), which the agent can run.

    Shell commands in SKILL.md call:

    • curl
    • uv
    • python3
    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use curl and uv, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Vllm Deploy Simple loads about 1.6k tokens when it runs. Until then it costs about 26 tokens; SKILL.md has 555 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~26
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from vllm-project/vllm-skills at commit c996234, republished under its Apache-2.0 licence (© vllm-project). 555 words, ~1,585 tokens.

Download SKILL.mdSave it as .claude/skills/vllm-deploy-simple/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
vllm-deploy-simple
description
Quick install and deploy vLLM, start serving with a simple LLM, and test OpenAI API.

vLLM Simple Deployment

A simple skill to quickly install vLLM, start a server, and validate the OpenAI-compatible API.

What this skill does

This skill provides a streamlined workflow to:

  • Detect hardware backend (NVIDIA CUDA, AMD ROCm, Google TPU, or CPU)
  • Install vLLM with appropriate backend support
  • Start the vLLM server with configurable model and port
  • Test the OpenAI-compatible API endpoint
  • Validate the deployment is working correctly
  • Support virtual environment isolation

Prerequisites

  • Python 3.10+
  • GPU (NVIDIA CUDA, AMD ROCm) (recommended) or TPU or CPU
  • pip or uv package manager
  • curl (for API testing)
  • Virtual environment (optional but recommended)

Usage

Create a venv

If user did not specify the venv path or asked to deploy in the current environment, create a venv using uv with python 3.12 in the current folder. If uv not found, make a folder in this path and use python to create a virtual environment.

Run the complete workflow (suggested)

If user did not specify the venv path, model, or port, use default options:

bash
# Default deployment options (--venv "." --model "Qwen/Qwen2.5-1.5B-Instruct" --port 8000 --gpu_memory_utilization 0.8)
scripts/quickstart.sh

Or with custom options:

bash
# Use custom virtual environment
scripts/quickstart.sh --venv /path/to/venv

# Use custom model and port
scripts/quickstart.sh --model "Qwen/Qwen2.5-1.5B-Instruct" --port 8000

# Use custom GPU memory utilization
scripts/quickstart.sh --gpu_memory_utilization 0.6

# Combine all options
scripts/quickstart.sh --venv /path/to/venv --model "Qwen/Qwen2.5-1.5B-Instruct" --port 8000 --gpu_memory_utilization 0.8

This will:

  1. Activate the virtual environment (if specified)
  2. Detect hardware backend (CUDA/ROCm/TPU/CPU)
  3. Install vLLM with appropriate backend support
  4. Start the vLLM server in the background
  5. Wait for the server to be ready
  6. Test the API with a sample request
  7. Display the server status
Run individual commands (for step-by-step usage or troubleshooting)

Install vLLM:

bash
scripts/quickstart.sh install
# Or with virtual environment
scripts/quickstart.sh install --venv /path/to/venv

Start the server:

bash
scripts/quickstart.sh start
# Or with custom options
scripts/quickstart.sh start --venv /path/to/venv --model "Qwen/Qwen2.5-1.5B-Instruct" --port 8000 --gpu_memory_utilization 0.8

Test the API:

bash
scripts/quickstart.sh test
# Or with custom port
scripts/quickstart.sh test --port 8000

Stop the server:

bash
scripts/quickstart.sh stop
# Or with virtual environment
scripts/quickstart.sh stop --venv /path/to/venv

Check server status:

bash
scripts/quickstart.sh status

Restart the server:

bash
scripts/quickstart.sh restart
# Or with custom options
scripts/quickstart.sh restart --venv /path/to/venv --port 8000 --gpu_memory_utilization 0.8

Configuration

The script supports the following command-line options:

bash
scripts/quickstart.sh [command] [OPTIONS]

Commands:
  install  - Install vLLM and dependencies
  start    - Start the vLLM server
  stop     - Stop the vLLM server
  test     - Test the OpenAI-compatible API
  status   - Show server status
  restart  - Restart the server
  all      - Run complete workflow (default)

Options:
  --model MODEL                 Model to use (default: Qwen/Qwen2.5-1.5B-Instruct)
  --port PORT                   Port to run server on (default: 8000)
  --venv VENV_PATH              Virtual environment path (default: .)
  --gpu_memory_utilization VRAM GPU memory utilization (default: 0.8)
Hardware Backend Detection

The script automatically detects your hardware and installs the appropriate vLLM version:

  • NVIDIA CUDA: Detected via nvidia-smi command
  • AMD ROCm: Detected via /dev/kfd and /dev/dri devices
  • Google TPU: Detected via TPU_NAME environment variable or gcloud command
  • CPU: Fallback if no GPU/TPU detected

For Google TPU, the script installs vllm-tpu instead of the standard vllm package.

API Testing

The test script sends a simple chat completion request:

bash
curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen/Qwen2.5-1.5B-Instruct",
    "messages": [{"role": "user", "content": "Say hello!"}],
    "max_tokens": 50
  }'
Show full SKILL.md (233 more words)Show less

Troubleshooting

Virtual environment not found:

  • Ensure the path provided with --venv exists and is a valid virtual environment
  • Check that the activation script exists (bin/activate on Linux/macOS or Scripts/activate on Windows)
  • Check and install uv, and create a new virtual environment with uv: uv venv /path/to/venv (suggested); or with pip: python3 -m venv /path/to/venv

Server won't start:

  • Check if the port is already in use: lsof -i :8000
  • Verify GPU availability: nvidia-smi (for NVIDIA) or rocm-smi (for AMD)
  • Check vLLM installation: python -c "import vllm; print(vllm.__version__)"
  • Review server logs at $VENV_PATH/tmp/vllm-server.log

API returns errors:

  • Wait a few seconds for the model to load
  • Check server logs: cat $VENV_PATH/tmp/vllm-server.log
  • Verify the server is running: scripts/quickstart.sh status

Out of memory:

  • Use a smaller model (e.g., Qwen2.5-0.5B-Instruct)
  • Reduce --gpu-memory-utilization parameter
  • Close other GPU-intensive applications

Wrong backend detected:

  • For NVIDIA: Ensure nvidia-smi is in your PATH
  • For AMD: Check that ROCm drivers are properly installed
  • For TPU: Set TPU_NAME environment variable or install gcloud

Notes

  • The server runs in the background and logs to $VENV_PATH/tmp/vllm-server.log
  • The PID is stored in $VENV_PATH/tmp/vllm-server.pid for easy management
  • First run will download the model (~3GB for Qwen2.5-1.5B-Instruct)
  • Subsequent runs will use the cached model
  • The script automatically detects and uses uv if available, otherwise falls back to pip
  • Virtual environment support allows isolation from system Python packages
  • Arguments can be specified in any order (e.g., scripts/quickstart.sh --port 8080 start --venv /path/to/venv)

© vllm-project, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (scripts) in plugins/vllm-skills/skills/vllm-deploy-simple of vllm-project/vllm-skills.

  • SKILL.md
  • scripts/quickstart.sh

Open the folder on GitHubat commit c996234

Compare with similar skills

Vllm Deploy Simple next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Vllm Deploy Simple compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Vllm Deploy Simple this skillvllm-project/vllm-skills103—~1.6kAutomated safety check: PassApache-2.0
Dstack Prototypingdstackai/dstack2.3k—~1.6kAutomated safety check: PassMPL-2.0
Model Serving MinefieldBlackwellboy/model-serving-minefield135—~2.1kAutomated safety check: PassMIT
Graphsignalgraphsignal/graphsignal257—~6.2kAutomated safety check: PassApache-2.0
LLM Torch Profiler Trace AnalysisBBuf/AI-Infra-Auto-Driven-SKILLS911—~2.8kAutomated safety check: PassNone
vLLM Model ServingOrchestra-Research/AI-Research-SKILLs13k6 repos~2.3kAutomated safety check: PassMIT

Similar skills

  • Dstack Prototyping

    dstackai/dstack

    Use with the dstack skill for model-serving work when the image, serving command, resources, backend/fleet choice, or service behavior is not proven.

    2.3k GitHub stars~1.6k tokensUpdated yesterday
    AI & LLM EngineeringAuto-check passed

  • Model Serving Minefield

    Blackwellboy/model-serving-minefield

    Diagnose OpenAI-compatible model-serving failures from symptoms, endpoint reports, explicit configuration files, or logs while preserving evidence status and requiring confirm/refute checks.

    135 GitHub stars~2.1k tokensUpdated today
    AI & LLM EngineeringAuto-check passed

  • Graphsignal

    graphsignal/graphsignal

    Profile AI inference workloads (vLLM, SGLang, TensorRT-LLM, PyTorch, any GPU application) with the Graphsignal profiler and read the results from its local /signals JSON endpoint.

    257 GitHub stars~6.2k tokensUpdated 10 days ago
    AI & LLM EngineeringAuto-check passed

  • LLM Torch Profiler Trace Analysis

    BBuf/AI-Infra-Auto-Driven-SKILLS

    Analyzes Torch Profiler traces from SGLang, vLLM and TensorRT-LLM servers into kernel attribution, overlap and fusion tables.

    911 GitHub stars~2.8k tokensUpdated 3 days ago
    AI & LLM EngineeringAuto-check passed

  • vLLM Model Serving

    Orchestra-Research/AI-Research-SKILLs

    Deploys LLMs with vLLM for high-throughput serving, covering the OpenAI-compatible server, offline batch inference, monitoring and a Docker rollout.

    13k GitHub starsUsed in 6 repos~2.3k tokens
    AI & LLM EngineeringAuto-check passed

  • SGLang Structured Serving

    Orchestra-Research/AI-Research-SKILLs

    Covers serving LLMs with SGLang, whose RadixAttention reuses cached prefixes, and constraining output to JSON, regex or grammar for agent and tool-calling workloads.

    13k GitHub starsUsed in 3 repos~2.9k tokens
    AI & LLM EngineeringAuto-check passed

More from vllm-project/vllm-skills

  • Vllm Bench Random Synthetic

    vllm-project/vllm-skills

    Run vLLM performance benchmark using synthetic random data to measure throughput, TTFT (Time to First Token), TPOT (Time per Output Token), and other key performance metrics.

    103 GitHub stars~1.5k tokensUpdated 6 mo ago
    Auto-check passed

  • Vllm Bench Serve

    vllm-project/vllm-skills

    Benchmark vLLM or OpenAI-compatible serving endpoints using vllm bench serve.

    103 GitHub stars~1.6k tokensUpdated 6 mo ago
    Auto-check passed

  • Vllm Deploy K8s

    vllm-project/vllm-skills

    Deploy vLLM to Kubernetes (K8s) with GPU support, health probes, and OpenAI-compatible API endpoint.

    103 GitHub stars~2k tokensUpdated 6 mo ago
    Auto-check passed

  • Vllm Prefix Cache Bench

    vllm-project/vllm-skills

    This is a skill for benchmarking the efficiency of automatic prefix caching in vLLM using fixed prompts, real-world datasets, or synthetic prefix/suffix patterns.

    103 GitHub stars~1.4k tokensUpdated 6 mo ago
    Auto-check passed

  • Vllm Deploy Docker

    vllm-project/vllm-skills

    Deploy vLLM using Docker (pre-built images or build-from-source) with NVIDIA GPU support and run the OpenAI-compatible server.

    103 GitHub stars~2.5k tokensUpdated 6 mo ago
    Auto-check: notes

Works with

Categories

Questions about Vllm Deploy Simple

What does Vllm Deploy Simple do?

Quick install and deploy vLLM, start serving with a simple LLM, and test OpenAI API. Vllm Deploy Simple is an agent skill from vllm-project/vllm-skills. Quick install and deploy vLLM, start serving with a simple LLM, and test OpenAI API.

When should I use Vllm Deploy Simple?

Vllm Deploy Simple fits situations like: tasks that involve LLM inference and serving.

How do I install Vllm Deploy Simple in Claude Code?

Run `npx skills add vllm-project/vllm-skills --skill vllm-deploy-simple -a claude-code`. Or copy the skill folder (plugins/vllm-skills/skills/vllm-deploy-simple in vllm-project/vllm-skills) into .claude/skills/vllm-deploy-simple in your project. Claude Code loads it when a task matches its description.

How do I install Vllm Deploy Simple in Codex?

Run `npx skills add vllm-project/vllm-skills --skill vllm-deploy-simple -a codex`. Or copy the skill folder (plugins/vllm-skills/skills/vllm-deploy-simple in vllm-project/vllm-skills) into .agents/skills/vllm-deploy-simple in your project. Codex loads it when a task matches its description.

Can I use Vllm Deploy Simple in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add vllm-project/vllm-skills --skill vllm-deploy-simple -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/vllm-deploy-simple, .gemini/skills/vllm-deploy-simple, .github/skills/vllm-deploy-simple and .opencode/skills/vllm-deploy-simple in your project.

What does Vllm Deploy Simple need to run?

Going by SKILL.md and its folder, Vllm Deploy Simple needs a shell for the scripts in its folder and the command-line tools its instructions call (curl, uv, python3 and python). Our summary lists: Python 3; A Bash shell.

Does Vllm Deploy Simple access the network?

SKILL.md contains no URLs. Its commands use curl and uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Vllm Deploy Simple safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Vllm Deploy Simple use?

Vllm Deploy Simple is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Vllm Deploy Simple use?

About 1.6k tokens (SKILL.md is roughly 6.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Vllm Deploy Simple?

Skills that share tags, products or a category with Vllm Deploy Simple: Dstack Prototyping (dstackai/dstack, 2.3k stars), Model Serving Minefield (Blackwellboy/model-serving-minefield, 135 stars), Graphsignal (graphsignal/graphsignal, 257 stars) and LLM Torch Profiler Trace Analysis (BBuf/AI-Infra-Auto-Driven-SKILLS, 911 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Vllm Deploy Simple?

vllm-project (a GitHub organization) maintains it in vllm-project/vllm-skills, which has 103 GitHub stars. The repository holds 6 skills in this directory. The repository was last updated on April 3, 2026.

Source: vllm-project/vllm-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.