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 shen-shanshan/vllm-dev-skills --skill vllm-model-tutorialgit clone --depth 1 https://github.com/shen-shanshan/vllm-dev-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/shen-shanshan/vllm-dev-skills/vllm-model-tutorial)<a href="https://agentmods.dev/skills/shen-shanshan/vllm-dev-skills/vllm-model-tutorial"><img src="https://agentmods.dev/badge/skills/shen-shanshan/vllm-dev-skills/vllm-model-tutorial/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/shen-shanshan/vllm-dev-skills/vllm-model-tutorial"><img src="https://agentmods.dev/badge/skills/shen-shanshan/vllm-dev-skills/vllm-model-tutorial.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 | $0.00246 | $0.01668 |
| Opus 5.5 | $0.00098 | $0.00667 |
| Sonnet 5.5 | $0.00049 | $0.00334 |
| Haiku 4.5 | $0.00025 | $0.00167 |
Grade A, and why
vllm-model-tutorial 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 1mo 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 — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
vLLM Model Tutorial Generator
Generate a comprehensive model technical tutorial document for a given model supported by vLLM.
Workflow
Step 1: Identify the Model
Extract the model name from the user's request. Normalize common aliases:
- "Qwen3-VL" / "Qwen3VL" → Qwen3-VL
- "Qwen2.5-VL" → Qwen2.5-VL
- "DeepSeek-V3" / "DSv3" → DeepSeek-V3
- "InternVL3" / "InternVL 3" → InternVL3
- "Llama 4" → Llama 4
- "GPT-OSS" → GPT-OSS
If the model name is ambiguous, ask the user to clarify.
Step 2: Research the Model
Gather information from multiple sources. This is the most critical step — thorough research determines document quality.
2a. Find Technical Reports and Papers
Search for the model's official technical report, paper, or blog post:
- Use WebSearch:
"{model_name} technical report arxiv"or"{model_name} paper" - Use WebFetch to read the paper/report and extract architecture details, innovations, benchmarks
- For model series: also find reports for predecessor models to build the evolution timeline
2b. Gather Model Family Information
Build the model family comparison context:
- Search for the full model series evolution (e.g., Qwen-VL → Qwen2-VL → Qwen2.5-VL → Qwen3-VL)
- For each variant: collect parameter counts, release dates, key innovations, performance benchmarks
- Find HuggingFace and ModelScope links for each variant (search
huggingface.co/{model_id}) - Collect technical report / paper links for each variant
2c. Analyze Model Architecture
Extract detailed architecture information from papers, docs, and the LLM Architecture Gallery:
- Overall architecture design (encoder-decoder, decoder-only, cross-attention)
- Key components: attention mechanism (MHA/GQA/MQA/MLA), FFN type (dense/MoE), normalization, activation
- For VLMs: ViT architecture, visual token projection, multimodal fusion strategy
- Context length, vocabulary size, hidden dimensions, layer counts
- Special tokens, chat template, generation config
- Reference:
https://sebastianraschka.com/llm-architecture-gallery/for comparative context
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.
- outputs/deepseek_ocr.md 43 KB
- outputs/deepseek_v4_1_flash.md 72 KB
- outputs/deepseek_v4_rocm.md 55 KB
- outputs/ernie45_vl.md 46 KB
- outputs/gemma3.md 50 KB
- outputs/kimi_vl.md 50 KB
- outputs/llama4.md 51 KB
- outputs/minicpm_v.md 49 KB
- outputs/minimax_m3.md 39 KB
- outputs/qwen3_vl.md 53 KB
- outputs/step3_vl.md 38 KB
- references/model-research-guide.md 8.7 KB
- references/style-guide.md 12 KB
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.
- 1mo ago First seen · 132 lines · 246 tokens per session scan A 80b687d2e064
vllm-model-tutorial is a skill published in the GitHub repository shen-shanshan/vllm-dev-skills (22 stars, last pushed yesterday), licensed Apache-2.0. It adds 246 tokens to every session and 1,668 once invoked, about $0.0010 per session on Opus 5.5. 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 2026-08-30.
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