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 magnus919/agent-skills --skill vllmgit clone --depth 1 https://github.com/magnus919/agent-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/magnus919/agent-skills/vllm)<a href="https://agentmods.dev/skills/magnus919/agent-skills/vllm"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/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/magnus919/agent-skills/vllm"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/vllm.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 63 Potential security issue detected. Manual review is recommended.Fix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
- medium Excessive Agency · line 35 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00196 | $0.03875 |
| Opus 5 | $0.00098 | $0.01937 |
| Sonnet 5 | $0.00039 | $0.00775 |
| Haiku 4.5 | $0.00020 | $0.00387 |
Grade A, and why
vllm scanned grade A with 0 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 7d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.
vLLM Inference Serving
Use this skill to operate vLLM as a production inference server: deploy it with Docker or Kubernetes, configure the model and engine (quantization, tensor parallelism, KV cache, context length), serve the OpenAI-compatible API surface, benchmark throughput and latency with comparable evidence, tune continuous batching, operate the GPUs underneath, and upgrade or roll back safely. This is a tool skill for one named engine. Serving methodology — engine selection, quantization trade-offs, deployment plans, regression triage — belongs to ml-engineering; local single-node GGUF serving with the llama.cpp stack belongs to llama-cpp. This skill owns the day-to-day operation of vLLM itself.
Operating contract
- Record the deployment before tuning it. Capture the vLLM version or image digest, model and revision, quantization, parallelism,
max-model-len, KV cache settings, batching limits, GPU inventory, and workload. The serving config template exists for exactly this. - Confirm the target, scope, and rollback path before acting. Read-only discovery (health probes,
/metrics,nvidia-smi) may proceed without confirmation. Mutations — restarting a server, changing serving args, scaling replicas, upgrading the image — require an explicit human directive naming the deployment. - A server that responds is not a server that serves.
/healthreturning 200 proves liveness, not that the model loaded or that inference works. Verify at the delivery boundary:/v1/modelsreports the served model and a representative request returns generated tokens. - Benchmark before and after every change. vLLM flags, defaults, and behavior change between releases; an unmeasured tuning change is a guess. Compare only matched conditions (version, model, GPU, context, batch, workload) and record the evidence in the benchmark run record.
- Keep evidence bounded. Summarize logs, configs, and metrics; never dump full server logs,
.envfiles, or HF tokens into chat.--enable-log-requestswith debug logging can leak prompt content; keep request logging off or redacted in shared sessions.
What ships with it
13 files 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.
- evals/evals.json 12 KB
- README.md 4.9 KB
- references/00-source-index.md 3.7 KB
- references/01-deployment.md 5.3 KB
- references/02-model-configuration.md 4.8 KB
- references/03-openai-api.md 3.2 KB
- references/04-benchmarking.md 3.8 KB
- references/05-batching-and-tuning.md 3.8 KB
- references/06-gpu-ops-and-lifecycle.md 4.9 KB
- scripts/vllm-health 7.2 KB
- templates/benchmark-run-record.md 2.4 KB
- templates/serving-config.md 2.6 KB
- tests/test_vllm_health.py 8.4 KB runs code
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.
- 7d ago First seen · 153 lines · 196 tokens per session scan A a90c5f21dc2b
vllm is a skill published in the GitHub repository magnus919/agent-skills (76 stars, last pushed yesterday), licensed MIT. It adds 196 tokens to every session and 3,875 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-05.
Other skills, from other repositories
tensorrt-llm
High-throughput LLM inference on NVIDIA GPUs.
lambda-labs
On-demand GPU cloud instances for ML training.
map
Build and commit a Cortex function knowledge graph — maps structural dependencies and domain intent relationships across all AI functions in the project. Supports --reduce (default on) for transitive reduction of the dependency graph.
context-add
Guided wizard to capture or update organizational Snowflake/Cortex standards into .cocoplus/context/ .md. Presents a menu of 6 standard types, collects answers via multi-turn dialogue, and commits the file.
test
Enter the Test phase of CocoBrew. Reads spec.md test requirements, generates test cases, executes SQL validation and quality checks, records results in test.md. Can be re-run without full rebuild. Requires Build phase completion.
map-diff
Analyze the impact of staged git changes against the committed Cortex function knowledge graph — shows which downstream functions are affected before you commit.