vllm-setup

A setup workflow for running vLLM, a server that provides model responses, with an open-weight coding model on a multi-GPU Amazon EC2 computer.

In plain words
What is it for?
Use it to prepare a four-GPU EC2 node, install vLLM and its dependencies, and serve a coding model using multiple GPUs.
Why use it?
It handles the machine checks and software setup needed before serving the model, including fixes specific to the referenced Deep Learning AMI.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/aws-samples/sample-claude-code-multi-model/vllm-setup
Any agent
npx skills add aws-samples/sample-claude-code-multi-model --skill vllm-setup
Clone the repo
git clone --depth 1 https://github.com/aws-samples/sample-claude-code-multi-model

Made for: Claude Code, Codex.

Per session 164 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,532 The whole file, excluding the scripts and references it only reads on demand.
Security scan C 2 findings. Scan, not verified.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00164 $0.02532
Opus 5 $0.00082 $0.01266
Sonnet 5 $0.00033 $0.00506
Haiku 4.5 $0.00016 $0.00253

Measured 2d ago against content hash af3c66b80ec9, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade C, and why

vllm-setup scanned grade C 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 2d 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.

Cloud metadata endpointhighServer-side request forgery

One request to 169.254.169.254 can return temporary IAM credentials.

TOKEN=$(curl -sf -X PUT "http://169.254.169.254/latest/api/token" -H "X-aws-ec2-metadata-token-ttl-seconds: 60")

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

TOKEN=$(curl -sf -X PUT "http://169.254.169.254/latest/api/token" -H "X-aws-ec2-metadata-token-ttl-seconds: 60")
.claude/skills/vllm-setup/SKILL.md · 164 lines

The source is not reproduced here

Licensed MIT-0

The repository is licensed MIT-0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.

Read it on GitHub

Changes

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.

  1. 2d ago First seen · 164 lines · 164 tokens per session scan C af3c66b80ec9

Subscribe to this mod's changes

vllm-setup is a skill published in the GitHub repository aws-samples/sample-claude-code-multi-model (10 stars, last pushed 28d ago), licensed MIT-0. It adds 164 tokens to every session and 2,532 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it C with 2 findings (cloud metadata endpoint, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.