cs-xiaohuang-skill

cs-xiaohuang-skill is a skill for Codex from ChenShuo2004/cs-skills. It costs 153 tokens per session (1,933 once invoked), scanned A, original, MIT.

A design and illustration guide for 小黄 (Xiao Huang), a fixed brand character with a warm visual identity. It covers creating, editing, and adapting that character, as well as illustrating Chinese articles and other knowledge content.

In plain words
What is it for?
Use it to create character sheets, poses, expressions, stickers, scene illustrations, 2D or 3D versions, collaborations, style changes, and identity repairs. Use it also to plan or generate 16:9 illustrations for Chinese articles, posts, notes, and workflows.
Why use it?
It keeps the character recognisable when changing poses, scenes, materials, or art styles. It also helps turn abstract ideas in written content into planned, understandable illustrations.

Skill for Codex

Written for Codex: agents/openai.yaml present. Also seen: mentions Codex.

Good fit Use it to create character sheets, poses, expressions, stickers, scene illustrations, 2D or 3D versions, collaborations, style changes, and identity repairs. Use it also to plan or generate 16:9 illustrations for Chinese articles, posts, notes, and workflows.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/chenshuo2004/cs-skills/cs-xiaohuang-skill
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 ChenShuo2004/cs-skills --skill cs-xiaohuang-skill
Clone the repo
git clone --depth 1 https://github.com/ChenShuo2004/cs-skills

Made for: 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 cs-xiaohuang-skill

README.md
[![agentmods](https://agentmods.dev/badge/skills/chenshuo2004/cs-skills/cs-xiaohuang-skill/github.svg)](https://agentmods.dev/skills/chenshuo2004/cs-skills/cs-xiaohuang-skill)
Your own site
<a href="https://agentmods.dev/skills/chenshuo2004/cs-skills/cs-xiaohuang-skill"><img src="https://agentmods.dev/badge/skills/chenshuo2004/cs-skills/cs-xiaohuang-skill/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 cs-xiaohuang-skill

Your own site · 80×15
<a href="https://agentmods.dev/skills/chenshuo2004/cs-skills/cs-xiaohuang-skill"><img src="https://agentmods.dev/badge/skills/chenshuo2004/cs-skills/cs-xiaohuang-skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 153 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,933 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 $0.00153 $0.01933
Opus 5 $0.00077 $0.00966
Sonnet 5 $0.00031 $0.00387
Haiku 4.5 $0.00015 $0.00193

Measured 11d ago against content hash be4b4df8cae8, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

cs-xiaohuang-skill 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 11d 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.

cs-xiaohuang-skill/SKILL.md · 80 lines

How it starts

The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.

小黄 IP 与正文配图

小黄就是“有温度”的固定品牌角色。这个 skill 是它的唯一入口:既把中文内容的判断、变化、关系或隐喻画成正文配图,也稳定生成、编辑和延展小黄的品牌 IP 资产。

始终遵守:可以换动作、场景、媒介和画法;不可以换掉角色身份。

先读参考

所有生图、编辑或角色评估任务都必须查看 assets/reference-xiaohuang.png;它是唯一的身份锚点。再按任务读取,避免一次加载无关内容:

  • 身份、标准图、修复、三视图或一致性检查references/character-dna.md
  • 2D/3D、黑白、动作表情、多形态、联名或风格迁移references/media-and-variants.md
  • 品牌 IP 生成或编辑references/ip-prompt-templates.md
  • 正文配图references/style-dna.mdreferences/xiaohuang-ip.mdreferences/composition-patterns.mdreferences/prompt-template.md
  • 交付前references/qa-checklist.md

两种工作模式

1. 正文配图

  • 配图规划:分析文章怎么配图、哪些段落值得配图时,先输出 shot list,不生成图片。
  • 直接生成:为每个认知锚点单独调用 image_gen;不要把多张配图拼成一张。
  • 局部编辑:只改标题、错字、标注或指定元素;保持其余构图、比例与风格不变。

2. 品牌 IP 延展

  • 角色延展:标准图、动作、表情、三视图、表情包、场景植入和品牌海报插画。
  • 媒介转换:保持角色身份,将 2D 转为 3D、线稿、软胶、树脂或用户指定媒介。
  • 风格迁移与联名:只迁移视觉语言;与其他角色共创时,双方身份必须分别可辨。
  • 身份修复:按 DNA 修复错误轮廓、爱心天线、面部或四肢,而非重新设计角色。

正文配图工作流

  1. 消化内容。 读取用户提供的文章、笔记、链接、Markdown、Notion 内容、截图或单个观点;区分核心观点、认知转折、可视化关系和不需要配图的段落。
  2. 选择认知锚点。 不要平均配图。优先选择能帮助理解的判断、前后变化、输入输出、分流、瓶颈、反馈、角色状态或核心隐喻。
  3. 先做配图策略。 在未明确要求生成时,给出 3~6 张 shot list;短内容可为 1~3 张,长文通常不超过 8 张。每张包含:插入位置、核心意思、构图类型、小黄的动作、主要物件与建议标注词。
  4. 发明新的隐喻。 将抽象概念转换成可见动作和低科技物件;每次从当前文章重新设计画面,不复用之前的案例构图。
  5. 逐张生成。 每张图只表达一个结构。使用 references/prompt-template.md 组合提示词,并让小黄承担解释概念的关键动作。
  6. 检查与迭代。references/qa-checklist.md 检查。中文错字多时,减少标注后重生成;元素过多时,删到只剩一个动作与少量物件。
  7. 保存交付。 当用户在 workspace 中工作时,将成图保存至 assets/<article-slug>-illustrations/,按 01-topic.png02-topic.png 递增命名;不覆盖原图,除非用户明确要求。

品牌 IP 延展工作流

  1. 锁定输入角色。 明确区分身份参考、风格参考、场景参考和编辑目标;不要把所有输入图都当成编辑目标。
  2. 写出不变量与变量。 不变量是轮廓逻辑、爱心天线、面部比例、细四肢与暖色体系;变量是动作、表情、场景、构图、媒介、光线和画法。
  3. 选择任务结构。 先决定是角色延展、媒介转换、风格迁移、联名共创还是身份修复;多版本必须改变结构,不只换材质。
  4. 单资产生成或编辑。 用户明确要求生成时直接调用可用生图工具;每个独立资产单独生成,局部问题只做单一目标编辑。
  5. 检查与迭代。 按 QA 清单检查。两个及以上身份锚点失败时重新设计;只有一个局部失败时固定不变量后局部修复。
  6. 保存交付。 保存到用户指定目录;未指定时使用清晰、可排序的文件名,且不覆盖旧资产。报告用途、媒介、路径、最稳版本与仍存风险。

统一硬规则

  • 角色 DNA 优先于画风。小黄必须保持暖黄不规则种子形身体、空心爱心天线、黑色竖椭圆眼、小弧线嘴、腮红和细黑四肢;不得变成梨子、鸡蛋、水滴、动物或通用黄色吉祥物。
  • 正文配图默认 16:9 横版、暖白底、黑色轻手绘线条和充足留白;一张只解释一个关系,标注最多 6 处,小黄必须承担关键动作。
  • 品牌 IP 默认使用暖白背景与克制的 2D 手绘蜡笔 / 油画棒 / 彩铅质感;只有用户要求时才切换到 3D、黑白或其他媒介。
  • 不复制他人 IP、受保护角色或旧图构图。风格迁移只迁移线条、材质、色彩和氛围;联名时不得合并轮廓或交换身份标志。
  • 不用于复杂架构图、可编辑矢量图、PPT 式信息图或 ChatCut 视频策划。品牌角色主导的插画、场景图和海报资产属于本 skill 范围。

Read the full file on GitHub · 80 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. 11d ago First seen · 80 lines · 153 tokens per session scan A be4b4df8cae8

Subscribe to this mod's changes

cs-xiaohuang-skill is a skill published in the GitHub repository ChenShuo2004/cs-skills (142 stars, last pushed 8d ago), licensed MIT. It adds 153 tokens to every session and 1,933 once invoked, about $0.0008 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.

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