Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add vllm-project/vllm-skills --skill vllm-deploy-simplegit clone --depth 1 https://github.com/vllm-project/vllm-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/vllm-project/vllm-skills/vllm-deploy-simple)<a href="https://agentmods.dev/skills/vllm-project/vllm-skills/vllm-deploy-simple"><img src="https://agentmods.dev/badge/skills/vllm-project/vllm-skills/vllm-deploy-simple/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/vllm-project/vllm-skills/vllm-deploy-simple"><img src="https://agentmods.dev/badge/skills/vllm-project/vllm-skills/vllm-deploy-simple.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00029 | $0.01725 |
| Opus 5 | $0.00015 | $0.00863 |
| Sonnet 5 | $0.00006 | $0.00345 |
| Haiku 4.5 | $0.00003 | $0.00172 |
Grade A, and why
vllm-deploy-simple scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- curl (for API testing) How it starts
The opening of the file, as written. The whole thing — 195 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
# 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:
# 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:
- Activate the virtual environment (if specified)
- Detect hardware backend (CUDA/ROCm/TPU/CPU)
- Install vLLM with appropriate backend support
- Start the vLLM server in the background
- Wait for the server to be ready
- Test the API with a sample request
- Display the server status
Run individual commands (for step-by-step usage or troubleshooting)
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 195 lines · 29 tokens per session scan A 62c8ec91412d
vllm-deploy-simple is a skill published in the GitHub repository vllm-project/vllm-skills (98 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 29 tokens to every session and 1,725 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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