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 cohere-ai/vllm-skills --skill install-vllmgit clone --depth 1 https://github.com/cohere-ai/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/cohere-ai/vllm-skills/install-vllm)<a href="https://agentmods.dev/skills/cohere-ai/vllm-skills/install-vllm"><img src="https://agentmods.dev/badge/skills/cohere-ai/vllm-skills/install-vllm/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/cohere-ai/vllm-skills/install-vllm"><img src="https://agentmods.dev/badge/skills/cohere-ai/vllm-skills/install-vllm.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 | $0.00063 | $0.02489 |
| Opus 5 | $0.00032 | $0.01244 |
| Sonnet 5 | $0.00013 | $0.00498 |
| Haiku 4.5 | $0.00006 | $0.00249 |
Grade C, and why
install-vllm 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 12d 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.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
command -v uv || (echo "uv not found — install via: curl -LsSf https://astral.sh/uv/install.sh | sh" && exit 1) Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
command -v uv || (echo "uv not found — install via: curl -LsSf https://astral.sh/uv/install.sh | sh" && exit 1) How it starts
The opening of the file, as written. The whole thing — 207 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Install vLLM (NVIDIA CUDA)
All installs are editable (-e .) from the current vLLM repo checkout.
Prerequisites
Verify uv is installed before proceeding:
command -v uv || (echo "uv not found — install via: curl -LsSf https://astral.sh/uv/install.sh | sh" && exit 1)
Step 1: Create and activate the environment
Run from the root of the vLLM repo:
uv venv --python 3.12 --seed --managed-python
source .venv/bin/activate
Step 2: Choose the precompiled wheel
Always prompt the user for which precompiled wheel to use, unless they have already specified one. The precompiled wheel determines which C++/CUDA binaries are loaded at runtime. Using the wrong wheel (e.g. a nightly from the branch tip when you need the stable release) can cause silent correctness regressions. The Python source code comes from the editable checkout regardless of which wheel is chosen.
2a) Detect the current branch and infer the default version
BRANCH=$(git rev-parse --abbrev-ref HEAD) # e.g. "v0.19.0-branch" or "main"
CPU_ARCH=$(uname -m) # x86_64 or aarch64
Map branch names to release versions:
v<X>.<Y>.<Z>-branch→ release<X>.<Y>.<Z>(e.g.v0.19.0-branch→0.19.0)mainor any other branch → no matching release; default to the branch-tip nightly wheel
2b) Prompt the user for which wheel to use
Always show the user these options and wait for their answer before proceeding. Do not assume a default — the user must explicitly choose:
- Branch-tip nightly — built from the latest commit on the current branch.
Uses
VLLM_USE_PRECOMPILED=1(auto-resolved from the branch). - Matching stable release — the GitHub release wheel for the version that
matches the branch (if one exists). Uses
VLLM_PRECOMPILED_WHEEL_LOCATION. - Specific release — a user-specified version (e.g. "use the v0.18.2
wheel"). Uses
VLLM_PRECOMPILED_WHEEL_LOCATION.
Include the detected branch name and inferred version in the prompt so the user has full context.
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.
- 12d ago First seen · 207 lines · 63 tokens per session scan C e3f3b7467a89
install-vllm is a skill published in the GitHub repository cohere-ai/vllm-skills (6 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 63 tokens to every session and 2,489 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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