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vllm-technical-blog-writer

vllm-technical-blog-writer is a skill for Claude Code, Codex from shen-shanshan/vllm-dev-skills. It costs 104 tokens per session (1,614 once invoked), scanned A, original, Apache-2.0.

A writing guide for creating or completing Chinese technical blog posts about vLLM, an open-source system for serving AI models. It follows an author's established Zhihu writing style and requires research before drafting.

In plain words
What is it for?
Use it to write a new vLLM article or finish a Markdown outline or draft, including technical explanations and diagrams when needed.
Why use it?
It reduces the work of researching vLLM's current code and matching an existing author's structure and tone. It also helps avoid relying on outdated knowledge.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

not rated 17repo +1 3d ago A scan SkillSpector: pass 104 tokens original Apache-2.0

Good fit Use it to write a new vLLM article or finish a Markdown outline or draft, including technical explanations and diagrams when needed.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/shen-shanshan/vllm-dev-skills/vllm-technical-blog-writer
View source ↗ shen-shanshan/vllm-dev-skills zhuanlan.zhihu.com
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.

Any agent
npx skills add shen-shanshan/vllm-dev-skills --skill vllm-technical-blog-writer
Clone the repo
git clone --depth 1 https://github.com/shen-shanshan/vllm-dev-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for vllm-technical-blog-writer

README.md
[![agentmods](https://agentmods.dev/badge/skills/shen-shanshan/vllm-dev-skills/vllm-technical-blog-writer/github.svg)](https://agentmods.dev/skills/shen-shanshan/vllm-dev-skills/vllm-technical-blog-writer)
Your own site
<a href="https://agentmods.dev/skills/shen-shanshan/vllm-dev-skills/vllm-technical-blog-writer"><img src="https://agentmods.dev/badge/skills/shen-shanshan/vllm-dev-skills/vllm-technical-blog-writer/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.

agentmods 80×15 button for vllm-technical-blog-writer

Your own site · 80×15
<a href="https://agentmods.dev/skills/shen-shanshan/vllm-dev-skills/vllm-technical-blog-writer"><img src="https://agentmods.dev/badge/skills/shen-shanshan/vllm-dev-skills/vllm-technical-blog-writer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 104 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,614 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original 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 5.1 $0.00104 $0.01614
Opus 5 $0.00052 $0.00807
Sonnet 5 $0.00021 $0.00323
Haiku 4.5 $0.00010 $0.00161

Measured 12d ago against content hash 05146ff8a6eb, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

vllm-technical-blog-writer 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 12d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (outputs/vLLM多模态推理｜ViT_Full_CUDA_Graph/images/generate-multi-model-performance-gains.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/vllm-technical-blog-writer/SKILL.md · 126 lines

How it starts

The opening of the file, as written. The whole thing — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.

vLLM Technical Blog Writer

Write technically accurate vLLM articles that sound like the author, not a generic tutorial generator.

Required references

Before writing:

  1. Read references/style-profile.md.
  2. Read references/diagram-guide.md when the article needs any diagram.
  3. Treat the user's draft, outline, references, and explicit wording as higher priority than the default patterns in those files.

Select the workflow

  • New article: the user supplies a topic but no article draft. Follow "Create a new article".
  • Complete an article: the user supplies an outline, template, or partially written Markdown article. Follow "Complete an existing article".
  • If the request mixes both, preserve supplied content and treat it as an existing article.

Research rules

Research before drafting. Do not rely on model memory for current vLLM behavior.

  1. Inspect the latest main branch of vllm-project/vllm, unless the user specifies a release, commit, or PR.
  2. Record the analyzed commit SHA or exact version in working notes. Mention it in the article only when version context matters.
  3. Find the actual entry points, core classes/functions, data structures, and call path. Read implementations rather than inferring behavior from names.
  4. Cross-check with first-party sources in this order:
    • vLLM source code and tests
    • vLLM official documentation, design docs, PRs, issues, and discussions
    • upstream papers and dependency documentation
    • high-quality third-party material
  5. Prefer merged code and current docs. Clearly label behavior that exists only in an open PR, proposal, or older release.
  6. Keep a source list while researching. Every benchmark number, version-sensitive statement, or non-obvious claim must be traceable.
  7. If sources disagree, use the code as the source of truth and explain the version difference.
  8. Never invent code paths, API names, benchmark data, design rationale, or citations.

Read the full file on GitHub · 126 lines

Files

What ships with it

27 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.

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. 12d ago First seen · 126 lines · 104 tokens per session scan A 05146ff8a6eb

Subscribe to this mod's changes

vllm-technical-blog-writer is a skill published in the GitHub repository shen-shanshan/vllm-dev-skills (17 stars, last pushed 3d ago), licensed Apache-2.0. It adds 104 tokens to every session and 1,614 once invoked, about $0.0005 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 2026-08-30.

Claude CodeWriting and docsagentvLLM
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