wenqu-image

wenqu-image is a skill for Claude Code from gogoingai/wenqu-skills. It costs 106 tokens per session (3,644 once invoked), scanned A, original, MIT.

A Chinese-language skill for planning, generating, checking, uploading, and embedding diagrams, flowcharts, infographics, and explanatory images.

In plain words
What is it for?
It is for creating visual material such as architecture diagrams, process diagrams, flowcharts, and article illustrations.
Why use it?
It gives image requests a defined process, including turning rough placeholders into complete image instructions.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: names the AskUserQuestion tool; mentions Claude Code; mentions Codex.

Part of the wenqu-skills plugin — 6 skills shipped together

Good fit It is for creating visual material such as architecture diagrams, process diagrams, flowcharts, and article illustrations.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/gogoingai/wenqu-skills/wenqu-image
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 gogoingai/wenqu-skills --skill wenqu-image
Clone the repo
git clone --depth 1 https://github.com/gogoingai/wenqu-skills

Made for: Claude Code.

Or install wenqu-skills, the plugin that ships this one along with the rest of its 6 skills.

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 wenqu-image

README.md
[![agentmods](https://agentmods.dev/badge/skills/gogoingai/wenqu-skills/wenqu-image/github.svg)](https://agentmods.dev/skills/gogoingai/wenqu-skills/wenqu-image)
Your own site
<a href="https://agentmods.dev/skills/gogoingai/wenqu-skills/wenqu-image"><img src="https://agentmods.dev/badge/skills/gogoingai/wenqu-skills/wenqu-image/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 wenqu-image

Your own site · 80×15
<a href="https://agentmods.dev/skills/gogoingai/wenqu-skills/wenqu-image"><img src="https://agentmods.dev/badge/skills/gogoingai/wenqu-skills/wenqu-image.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 106 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,644 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.
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 $0.00106 $0.03644
Opus 5 $0.00053 $0.01822
Sonnet 5 $0.00021 $0.00729
Haiku 4.5 $0.00011 $0.00364

Measured 12d ago against content hash 25bcd20b458f, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

wenqu-image 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.

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.

wenqu-image/SKILL.md · 166 lines

How it starts

The opening of the file, as written. The whole thing — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.

内容配图与生图 Skill

📦 项目仓库与源码:https://github.com/gogoingai/wenqu-skills

与调用方的接口约定

其他写作技能(如 wenqu-write)在正文里为图片预留位置时,用两种标记之一:

标记 含义 谁来写
> 🖼️ 待配图:[一句话描述这张图要表达的核心内容] 占位标记,只知道这里需要一张图,还没想好具体画法 调用方(写作阶段)
```(代码块)第一行 # 画图提示:[描述] 完整画图提示,规范已写全,可以直接生成 本技能,或调用方已自行写好

本技能被调用时,先把占位标记转换成完整画图提示,再进入生成流程——两步不能合并,写法未确认前不要直接生成。

完整画图提示必须带风格 YAML frontmatter,格式是 # 画图提示 代码块内、提示正文之前加一段 YAML,风格、参考图路径、版本记录都写进提示本身,不能只存在对话记忆里——不然下次改图或复现时,所用风格、参考图与版本记录都无从解析:


style: 单色马克笔 # 对应风格库表里的风格名;无风格关键词时写"默认极简PPT" ref: mono-marker/mono-marker-02-branch-decision.png # 实际用过的参考图:styles/ 下的相对路径(不含域名),由 wenqu image fetch-ref 解析为受管缓存路径;临时本地路径不得写入;本轮没用 --ref 时保持原值不动;该字段从未用过时才省略 versions: # 历次生成的版本记录;每项必须是 HTTPS CDN URL,含质检不通过/用户否决版本,编号递增,不删除、不覆盖 v1: https://cdn.example.com/article-img-1111aaaa2222bbbb.png # 简要说明 generation: # 与 versions 同编号,记录实际渲染后端;不存密钥或 API 地址 v1: provider: codex model: codex-image-gen

画图提示:[图片标题]

...(提示正文,四条核心原则见下方)

转换占位标记、或用户直接给风格关键词时,都按这个格式写;references/styles/*.md 里该风格追加的专属描述句,直接拼进提示正文,不要另起一段脱离 YAML 之外的"风格说明",避免以后只读提示正文时丢失风格信息。每次实际传入 --ref,把这次真正生效的参考来源回填进 ref 字段:风格资产用 GitHub raw URL,已采用图片用 HTTPS CDN URL;命令中临时使用的 /tmp 文件不得回填。ref 记的是“这次生成实际参考了什么”,不是“这个风格理论上可以参考什么”;没用 --ref 的这一轮,不要动 ref: 字段,保留上一次的记录。只有上传成功、拿到 HTTPS CDN URL 的版本才可写入 versions,同时把本次实际 provider/model 写入同编号 generation;正文的 ![]() 只指向当前采用的那一版,其余版本仅留存在 versions 里供回看对比,具体写入规则见 references/gen-workflow.md 第四步。

用户输入工具

当本技能需要用户确认选择、补充必要信息或授权有副作用的操作时:

  1. 优先使用当前运行时提供的原生用户输入工具,例如 AskUserQuestionrequest_user_inputclarifyask_user 或等价能力。
  2. 若没有此类工具,使用带编号或字母选项的文本问答。
  3. 同一决策阶段中彼此独立的问题可合并提问;后一个问题依赖前一回答时,按优先级逐个问。
  4. 已由用户当前指令、调用方或文章偏好提供的信息,不重复询问。
  5. 文中出现的具体工具名均为示例;应替换为当前运行时的等价能力。

模型配置提问规则

先检查全局配置 ~/.gogoingai/wenqu-skills/image/config.json,再检查本篇 {项目根目录}/wenqu-skills/{文件名}/config/image.json

  1. 两者都没有时,用原生输入工具一次询问默认 provider、模型与画幅;不要索要密钥,仅告知用户运行 wenqu image doctor 查看本机凭证文件路径,然后创建全局非敏感配置。
  2. 有全局配置、本篇没有配置时,在首次为本篇实际生成前询问是否沿用全局选择;拒绝后询问本篇选择并写入文章级配置。
  3. 有本篇配置、或用户已在当前指令/命令中指定 provider/model 时,不重复询问。
  4. 直接生图(非文章场景)只读取全局配置;运行时命令由 wenqu image 提供。

Read the full file on GitHub · 166 lines

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 · 166 lines · 106 tokens per session scan A 25bcd20b458f

Subscribe to this mod's changes

wenqu-image is a skill published in the GitHub repository gogoingai/wenqu-skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 106 tokens to every session and 3,644 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 2026-08-31.

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