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zjp1997720
scholar-stickers

生成"人物贴纸"式知识介绍交互页面:适用于介绍思想家/科学家/文学家、学科原理、发展历史的课堂教学场景。当用户要"介绍几位人物""做人物贴纸页面""原理科普演示页""学科发展历史互动页"时触发。输入人物姓名、历史朝代、原理知识等,自动查资料并按固定结构生成 16:9 无滚动幻灯片页面(总览 + 每人一页,点击展开交互)。

Overview

Publisherzjp1997720
Repositoryzhijian-skills
Skill namescholar-stickers
Stars
780
Forks
99
Bundled files
7
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 7 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by zjp1997720 on GitHub. Read the source before you install it.

Installation

Install the Scholar Stickers AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/zjp1997720/zhijian-skills/tree/main/skills/scholar-stickers
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/zjp1997720/zhijian-skills.git /tmp/zhijian-skills
mkdir -p .claude/skills
cp -r /tmp/zhijian-skills/skills/scholar-stickers .claude/skills/scholar-stickers
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Scholar Stickers in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Scholar Stickers on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Scholar Stickers is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Scholar Stickers · 知识人物贴纸幻灯片生成器

把一组人物(思想家 / 科学家 / 文学家 / 历史人物)或一组概念,做成单文件 16:9 交互幻灯片:总览页贴纸墙 → 点击贴纸 → 人物详情页(生平可点击展开)。面向教师课堂投影使用,零滚动、键盘翻页、后排可读。


1. 输入参数

参数必填说明示例
figures✅3–6 位人物名单苏格拉底、孔子、卢梭、杜威、皮亚杰
domain✅学科领域(决定查资料的方向)教育哲学 / 经典物理学 / 唐宋文学
title✅总览页大标题哲学,从惊讶与疑惑开始
subtitle⬜总览页引言(默认空)我们为何教育……
chapter⬜顶部章节编号(默认 CHAPTER · 01)CHAPTER · 03
theme⬜配色主题(默认 warm-classic,见 §5.2)ink-blue / forest
expand_mode⬜生平是否点击展开(默认开)on / off

如果用户只给人物名单没给 title/domain,先按领域拟 2–3 个标题让用户选,不要自作主张定稿。


2. 工作流程(五步)

Step 1 · 资料整理(每人五段式)

用 WebSearch / 本地知识库为每位人物查证并整理以下字段。所有字段有字数硬限制(一屏放不下就失败):

js
{
  id: 'socrates',            // 拼音 id,用于图片文件名
  name: '苏格拉底',           // 中文姓名
  nameEn: 'Socrates',        // 外文名(中国人物用拼音或字)
  years: '470 BC – 399 BC',  // 生卒年
  keywords: '古希腊 · 街头追问 · 产婆术',   // ≤4 个关键词,· 分隔
  bio: '……约 110 字……',     // 生平:4-5 行以内,3-5 个关键事件
  contribs: [                // 贡献:恰好 3 条,每条 ≤40 字,<b>加粗术语</b>
    '<b>术语</b>:一句话解释。',
    '<b>术语</b>:一句话解释。',
    '<b>术语</b>:一句话解释。'
  ],
  quotes: ['……', '……'],     // 金句:恰好 2 条,每条 ≤30 字
  books: ['《A》', '《B》'],   // 经典文本:恰好 2 本
  inspiration: '……≤22 字,一行说完。'   // 当代启示:单句
}

内容红线:

  • 拿不准的史实标 [待核实],不要编造生卒年、著作名、语录
  • 金句必须是有出处的原文或公认转述,查不到就换成著作名句
  • 中国人物 quotes 优先用原典(文言可保留,加一句白话点题可选)

Step 2 · 肖像生成(铅笔素描贴纸风)

用当前平台的文生图能力(WorkBuddy 为「多模态内容生成」skill,其他平台用自带生图工具)为每位人物生成肖像,统一风格。

Prompt 模板(替换 {描述}):

Pencil sketch portrait of {人物身份描述:国籍/时代/外貌特征/服装},
graphite pencil drawing on white paper, monochromatic black and white,
hand-drawn white sticker border around the portrait with slightly torn paper edges,
bust composition, classical engraving style

外貌描述要写可识别特征(胡须/眼镜/帽子/发型/服装),例如:

  • 苏格拉底 → ancient Greek philosopher Socrates, bald head with curly white side hair, long curly white beard, classical Greek toga draped over one shoulder, intense thoughtful gaze
  • 杜威 → American philosopher John Dewey, round wire-rimmed glasses, small mustache, neat combed gray hair, formal suit with tie, dignified expression

调用要点(平台通用 + WorkBuddy 实测):

  • 通用原则:用当前平台/工具自带的文生图能力即可,Prompt 统一走上方模板,输出 768×960(或接近竖版比例),PNG 落到交付目录的 portraits/ 子目录
  • WorkBuddy(2026-08 实测):
    1. 先调 connect_cloud_service 拿 tempToken(每次任务重新获取)
    2. 脚本:定位本机 WorkBuddy 安装目录下的 resources/app.asar.unpacked/resources/builtin-skills/buddy-multimodal-generation/scripts/buddy-cloud.py(用 where buddy-cloud.py 或按安装路径查找,不要写死用户名)
    3. Python:优先 python3,找不到时用 py -3 或 where python 探测(不要写死绝对路径)
    4. 参数:image "<prompt>" --resolution 768:960 --token-stdin
    5. Token 管道传入:echo -n "<token>" | <python> -u <script> image "..." --token-stdin
    6. 生成完 curl -sS -L -o portraits/{id}.png "<result_url>" 落到交付目录的 portraits/ 子目录
    7. 若报"250 个任务上限",等 30 秒重试一次;最多重试 1 次
  • 生成后必须逐张 Read 检查:人物是否可识别、有没有水印角标(有水印就裁掉或重生成)

Step 3 · 页面组装

复制 template.html(本 skill 目录下)到交付目录,替换:

  1. THEME 对象 → 配色(见 §5.2)
  2. META 对象 → title / subtitle / chapter
  3. FIGURES 数组 → Step 1 的数据
  4. 确认 portraits/ 相对路径图片齐全

Step 4 · 自检清单

  • 总览页贴纸墙 3×2 排布不错位、hover 回正上浮
  • 每个详情页 4 行齐全:贴纸行 / 生平(默认收起)/ 贡献 / 文本+启示
  • 生平点击展开动画正常、翻页后自动复位收起
  • 键盘 ←→ 翻页、Esc 回总览、底部圆点可点
  • 缩放窗口,stage 始终 16:9 居中无滚动
  • 金句 28px、正文 22px——投影可读
  • 内容无溢出(bio ≤5 行、contribs 每条单行或两行内)

Step 5 · 交付

preview_url 打开 HTML 预览 → deliver_attachments 交付 HTML + 全部肖像 PNG + 内容文档 MD(可选)。


3. 页面结构(固定,不可擅改)

SLIDE 0  总览页
├── 顶部条:CHAPTER · NN | BEAT NN(期刊式编号)
├── 左:eyebrow + 大标题(em 斜体朱红强调词)+ 引言 + 点击提示
└── 右:贴纸墙(3 列 × 2 行,各自轻微旋转,hover 回正)

SLIDE 1..N  人物页(每人一页,纵向 4 行)
├── 第 1 行「贴纸行」:肖像(240px 白边贴纸) + 名字块(中64/EN26/年代21/关键词16.5) + 金句块(28px 大引号)
├── 第 2 行「生平」:标题条(点击展开 ▸) + 正文(22px,默认收起)
├── 第 3 行「贡献」:标题 + 3 条 ◆ 列表(22px)
└── 第 4 行「文本+启示」:书签(18px × 2) | ✦ 启示条(20px 单行)

底部条(每页):导航圆点 | 键盘提示 | 页码 NN / MM

交互:点贴纸跳详情 / ←→·空格·PgUpPgDn 翻页 / Esc·Home 回总览 / End 到末页 / 生平点击展开(翻页自动复位)。


4. 设计规范(课堂可读性优先)

4.1 版式

  • 16:9 固定舞台:设计稿 1600×900,JS fitStage() 计算 scale = min(vw/1600, vh/900),transform: scale() 居中;外层深色底。任何屏幕都是标准 16:9 满屏、零滚动。
  • 纵向单列流详情页:"左=人右=事"都不用,用从上到下一条龙——老师讲课视线沿一条主轴。

4.2 字号基线(1600×900 设计稿 px,后排可读底线)

层级字号字重
总览大标题80900
人名(中文)64900
金句28600
生平 / 贡献正文22400
启示20400
section 标题19700
书签18400
英文名 / 年代26 / 21—
关键词16.5400

字号上调时 line-height 反向收一点(22px 配 1.7)保持密度;section 标题必须跟正文同步放大,它是视线锚点。

4.3 字体

css
'Cormorant Garamond'(拉丁/斜体/数字) + 'Noto Serif SC'(中文标题/正文) + 'Noto Sans SC'(中文辅助)

Google Fonts CDN 引入;中英双语标签是固定风格(BIOGRAPHY · 生平事迹)。

4.4 配色主题

默认 warm-classic(本 skill 视觉签名):

令牌值用途
--bg#f3ebdb页面暖米底
--paper#fbf6e8金句块/纸面
--ink#1a1612主文字
--vermillion#b8392a朱红强调(标题词/引号/箭头/圆点)
--ink-mute#6a5e4a辅助文字

备选主题(改 :root 变量即可):

  • ink-blue:vermillion→#2c5a7a,bg→#e9edf2(冷调学术)
  • forest:vermillion→#3a6b4f,bg→#eaf0e6(自然学科)
  • 用户指定品牌色时,只替换 --vermillion 和 --bg 两个令牌,其余不动。

4.5 贴纸样式

白底 padding 7px + 三层投影(近/中/远)+ 旋转 -2.5°~2.5° 错落;hover:rotate(0) translateY(-8px) scale(1.05)。


5. 模板与资产

  • template.html —— 参数化完整页面(THEME / META / FIGURES 三个替换点在文件头部有注释标记)
  • 肖像统一存 portraits/{id}.png(768×960)

6. 常见变体

需求做法
加人物FIGURES 加条目 + 生成肖像;总览墙自动 3×N 排
概念/原理页(非人物)肖像换成"原理示意贴纸"(生成简笔示意素描图),金句位放原理一句话表述
发展历史时间线在总览页底部加 timeline ribbon;或详情页 years 行展开时期背景
不要点击展开expand_mode: off:删 .d-bio-content 折叠 CSS,p 直接显示
全英文版name/n ↔ nameEn 互换,字号各减 2px

7. 红线

  • 不虚构:生卒年、著作、语录必须可查证;查不到换内容,不编
  • 不超字数:bio ≤120 字、inspiration ≤22 字、quotes ≤30 字/条——超了就删减,不是缩小字号
  • 不滚动:任何情况下页面不允许出现滚动条(课堂投影死罪)
  • AI 肖像带水印:裁掉或重生成,交付前逐张检查

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Scholar Stickers AI skill do?

生成"人物贴纸"式知识介绍交互页面:适用于介绍思想家/科学家/文学家、学科原理、发展历史的课堂教学场景。当用户要"介绍几位人物""做人物贴纸页面""原理科普演示页""学科发展历史互动页"时触发。输入人物姓名、历史朝代、原理知识等,自动查资料并按固定结构生成 16:9 无滚动幻灯片页面(总览 + 每人一页,点击展开交互)。

Why use Scholar Stickers on TypingMind?

Because you install it once and use it with any model. Scholar Stickers is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Scholar Stickers in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zjp1997720/zhijian-skills/tree/main/skills/scholar-stickers. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Scholar Stickers?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Scholar Stickers?

As many as you like. As long as a model supports skills, you can use Scholar Stickers with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Scholar Stickers AI skill free?

Yes. It is published on GitHub by zjp1997720 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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