Updated 6mo ago
by vllm-project
vllm-skills
Agent skills for vLLM
102 GitHub stars (102)
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- 6 skills (6 skills found in its SKILL.md files)
- 28 forks (28 forks)
- 6mo ago last update (Last updated 6mo ago)
Install
Skills CLI
npx skills add vllm-project/vllm-skills
Asks which skills and agents to install. Just one: npx skills add vllm-project/vllm-skills --skill vllm-bench-random-synthetic — add -g for every project.
Claude Code
git clone https://github.com/vllm-project/vllm-skillscp -r "vllm-skills/plugins/vllm-skills/skills/vllm-bench-random-synthetic" ~/.claude/skills/
Copies the vllm-bench-random-synthetic skill into ~/.claude/skills/ for every project (use .claude/skills/ for one project). Repeat for the other skills listed below.
Plugin
/plugin marketplace add vllm-project/vllm-skills
Run inside Claude Code if the repository ships a plugin marketplace, then pick plugins from /plugin.
Skills can run code on your machine — read the SKILL.md first.
What it does
In the author's own words, from each skill's SKILL.md.
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vllm-bench-random-synthetic
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. Use when the user wants to quickly test vLLM serving performance without downloading external datasets.
plugins/vllm-skills/skills/vllm-bench-random-synthetic/SKILL.md on GitHub (opens in a new tab) -
vllm-bench-serve
Benchmark vLLM or OpenAI-compatible serving endpoints using vllm bench serve. Supports multiple datasets (random, sharegpt, sonnet, HF), backends (openai, openai-chat, vllm-pooling, embeddings), throughput/latency testing with request-rate control, and result saving. Use when benchmarking LLM serving performance, measuring TTFT/TPOT, or load testing inference APIs.
plugins/vllm-skills/skills/vllm-bench-serve/SKILL.md on GitHub (opens in a new tab) -
vllm-deploy-docker
Deploy vLLM using Docker (pre-built images or build-from-source) with NVIDIA GPU support and run the OpenAI-compatible server.
plugins/vllm-skills/skills/vllm-deploy-docker/SKILL.md on GitHub (opens in a new tab) -
vllm-deploy-k8s
Deploy vLLM to Kubernetes (K8s) with GPU support, health probes, and OpenAI-compatible API endpoint. Use this skill whenever the user wants to deploy, run, or serve vLLM on a Kubernetes cluster, including creating deployments, services, checking existing deployments, or managing vLLM on K8s.
plugins/vllm-skills/skills/vllm-deploy-k8s/SKILL.md on GitHub (opens in a new tab) -
vllm-deploy-simple
Quick install and deploy vLLM, start serving with a simple LLM, and test OpenAI API.
plugins/vllm-skills/skills/vllm-deploy-simple/SKILL.md on GitHub (opens in a new tab) -
vllm-prefix-cache-bench
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. Use when the user asks to benchmark prefix caching hit rate, caching efficiency, or repeated-prompt performance in vLLM.
plugins/vllm-skills/skills/vllm-prefix-cache-bench/SKILL.md on GitHub (opens in a new tab)
No skill matches that filter.
Review before you install
A skill is a set of instructions and scripts your agent will follow, and it can run code on your machine. Read the SKILL.md and any scripts first — this listing is not a security review.
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