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-dockergit 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-docker)<a href="https://agentmods.dev/skills/vllm-project/vllm-skills/vllm-deploy-docker"><img src="https://agentmods.dev/badge/skills/vllm-project/vllm-skills/vllm-deploy-docker/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-docker"><img src="https://agentmods.dev/badge/skills/vllm-project/vllm-skills/vllm-deploy-docker.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 | $0.00035 | $0.02493 |
| Opus 5 | $0.00017 | $0.01247 |
| Sonnet 5 | $0.00007 | $0.00499 |
| Haiku 4.5 | $0.00003 | $0.00249 |
Grade B, and why
vllm-deploy-docker scanned grade B with 2 findings 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.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
sudo groupadd docker Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- Optional: `curl` for API tests How it starts
The opening of the file, as written. The whole thing — 208 lines — stays where its author put it; the contents beside it link to each section on GitHub.
vLLM Docker Deployment
A Claude skill describing how to deploy vLLM with Docker using the official pre-built images or building the image from source supporting NVIDIA GPUs with CUDA. Instructions include NVIDIA CUDA support, example docker run and a minimal docker-compose snippet, recommended flags, and troubleshooting notes. For AMD, Intel, or other accelerators, please refer to the vLLM documentation for alternative deployment methods.
What this skill does
- Deploy vLLM with docker using pre-built images (recommended for most users) or build from source for custom configurations
- Provide example commands for running the OpenAI-compatible server with GPU access and mounted Hugging Face cache
- Point to build-from-source instructions when a custom image or optional dependencies are needed
- Explain common flags:
--ipc=host, shared cache mounts, andHF_TOKENhandling
Prerequisites
- Docker Engine installed (Docker 20.10+ recommended)
- NVIDIA GPU(s) with appropriate drivers and CUDA toolkit installed
- Optional:
curlfor API tests - A Hugging Face token if pulling private models or to avoid rate-limits:
HF_TOKEN
Quickstart using Pre-built Image (recommended)
Run a vLLM OpenAI-compatible server with GPU access, mounting the HF cache and forwarding port 8000:
docker run --rm --gpus all \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=$HF_TOKEN" \
-p 8000:8000 \
--ipc=host \
vllm/vllm-openai:latest \
--model Qwen/Qwen2.5-1.5B-Instruct
--gpus allexposes all GPUs to the container. Adjust if you need specific GPUs.--ipc=hostor an appropriately large--shm-sizeis recommended so PyTorch and vLLM can share host shared memory.- Mounting
~/.cache/huggingfaceavoids re-downloading models inside the container.
Note: vLLM and this skill recommend using the latest Docker image (
vllm/vllm-openai:latest). For legacy version images, you may refer to the Docker Hub image tags.
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 · 208 lines · 35 tokens per session scan B d78564b560a3
vllm-deploy-docker is a skill published in the GitHub repository vllm-project/vllm-skills (98 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 35 tokens to every session and 2,493 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 2 findings (asks for root, 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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