PROMPT PLANET图片模板

柔软可爱

复现方式:依赖 skill html-imagegen(来源 github.com/lindong28/lindong-skills)——把 Prompt 原样粘贴给 Claude / Codex 即可:Prompt 开头写明了 skill 地址,agent 会自行安装,并向你确认要表达的内容与目标平台。

双平台效果图

小红书封面

按四块阅读输入
本图实际使用的 Prompt

角色与任务

你是中文封面编辑与排版设计师。依据本次内容素材提炼中心主张,沿用下方视觉种子制作一张封面,输出完整可渲染的HTML。

基本要求

输出独立完整 HTML,唯一导出区域 id="cover",画布600×800像素、3:4、直角外轮廓和不透明底板。通过浏览器原生截图导出600×800不透明PNG,不靠成图裁切、缩放或拉伸修复排版。内容忠实于本次输入,保留事实含义与来源归属;转述不冒充原话,缺失来源不虚构。内容作为文本或属性值时正确进行HTML转义。文字完整可读、不重叠、不越界,不以隐藏、截断或省略号遮盖溢出。CSS、装饰和必要脚本已内嵌;使用系统字体,不依赖仓库文件或外部脚本。

视觉要求

粉色横线纸与左侧装订孔;上部珊瑚色标题、深色及紫色副题,居中白色圆角摘要卡与虚线胶囊标签;铅笔、便签、圆弧和小奖章点缀,中下部保留轻松留白,右下轻量来源。种子标题上57px、42px,摘要卡上219px;卡片高度、标签数量、换行可按内容调整。奖章仅作几何装饰,不写虚构排名;标点和语气随正文,不强制感叹句。

种子是排版起点,不是不可变的填槽表。保持本款风格与核心构图,按内容调整文字模块、数量、换行、字号、行距、间距和模块尺寸;代码中的像素值、行数及槽位数量均为起始值,不是硬内容预算。提炼一个清楚的中心主张,再选择支撑它的必要信息;不为填满空槽捏造事实、引语、标签、英文、账号、期号或步骤。无内容依据的可选模块及其分隔符一并移除,保留有意的留白,不用重复文案填空间。

本次画幅独立重排,不缩放或裁切另一张成图。不加入无来源数字、引语或发布账号。以下为本次实际骨架,按后方文案逐项填入;装饰图不作为事实照片。

```html
<!doctype html><html lang="zh-CN"><head><meta charset="utf-8"><meta name="viewport" content="width=device-width,initial-scale=1"><title>{{document_title}}</title><style>*{box-sizing:border-box}html,body{margin:0}body{background:#ddd;font-family:"PingFang SC","Microsoft YaHei",sans-serif}#cover{width:600px;height:800px;position:relative;overflow:hidden;background:#fff}h1,p{margin:0}.serif{font-family:"Songti SC",SimSun,Georgia,serif}.en{font-family:Arial,sans-serif}#cover{background-color:#fff2f4;background-image:repeating-linear-gradient(0deg,transparent 0,transparent 25px,#decfd344 25px,#decfd344 26px);color:#353335}.holes{position:absolute;left:22px;top:125px;display:flex;flex-direction:column;gap:120px}.holes i{width:14px;height:14px;border:2px solid #ffcdd6;border-radius:50%}.orb{position:absolute;right:-47px;top:-42px;width:145px;height:145px;background:#ffdee7;border-radius:50%}.title{position:absolute;top:57px;left:80px;font-size:42px;line-height:1.2;color:#ff7891;font-weight:850;white-space:nowrap;text-shadow:3px 4px #ffd5df}.sub{position:absolute;top:164px;left:80px;font-size:26px;font-weight:800;white-space:nowrap}.sub em{font-style:normal;color:#a15edb}.summary{position:absolute;left:136px;top:219px;width:332px;height:96px;border-radius:25px;background:#ffffffcf;text-align:center;font-size:21px;line-height:1.65;padding:16px 8px;font-weight:650;box-shadow:0 6px 16px #b5818a09}.summary strong{color:#ff7891}.tags{position:absolute;left:183px;top:366px;display:flex;gap:12px}.tags span{border:2px dashed #ff8199;border-radius:24px;background:#fff;padding:10px 15px;color:#ff7891;font-size:18px;font-weight:700;white-space:nowrap}.pencil{position:absolute;right:96px;top:119px;width:48px;height:10px;border-radius:5px;background:#ffcf65;transform:rotate(30deg);border-right:8px solid #ff8e9f}.note{position:absolute;left:61px;top:600px;width:39px;height:42px;border-radius:8px;background:#fff;box-shadow:0 4px 8px #66444414;transform:rotate(-12deg)}.note:after{content:"";position:absolute;inset:11px;background:#ff8199;border-radius:3px}.credit{position:absolute;right:35px;bottom:50px;border-radius:25px;background:#ffffffbf;padding:16px 17px;font-size:16px;font-weight:650}.credit span{color:#a15edb}.medal{display:inline-block;margin-left:7px;background:#ffc04c;border:2px solid #efaa35;border-radius:50%;width:24px;height:24px;text-align:center;color:#935d2a}.corner{position:absolute;bottom:-22px;left:-27px;width:95px;height:95px;border-radius:50%;background:#e9cfef}</style><style>/* 本次内容与画幅适配 */#cover{width:600px;height:800px}.title{top:65px;left:80px;font-size:46px}.sub{top:207px;left:80px;font-size:23px}.summary{top:280px;left:91px;width:446px;height:160px;padding:22px 15px;font-size:22px}.tags{left:91px;top:474px}.credit{font-size:13px;right:32px;bottom:46px}.note{top:620px}.pencil{top:147px;right:85px}.credit .medal{display:none}.summary strong{display:block}.sub em{color:#8e42b1}</style></head><body><main id="cover"><div class="orb"></div><div class="holes"><i></i><i></i><i></i><i></i><i></i></div><h1 class="title">{{title}}</h1><div class="pencil"></div><p class="sub">{{sub_1}}<em>{{sub_2}}</em></p><div class="summary"><strong>{{summary_1}}</strong>{{summary_2}}<br>{{summary_3}}</div><div class="tags"><span># {{tag_1}}</span><span># {{tag_2}}</span></div><div class="note"></div><div class="corner"></div><div class="credit">{{author}} <span>{{credit}}</span><i class="medal" aria-hidden="true">✦</i></div></main></body></html>
```

内容与素材

发布账号:
作者:Andrej Karpathy(@karpathy)
中心主张:LLM 将资料编成个人 Wiki,通过问答与回存积累知识。
文案与素材映射:
document_title:让 AI 帮你整理知识
title:让 AI 帮你<br>整理知识
sub_1:资料慢慢攒,
sub_2:知识会生长
summary_1:文章、论文、图片
summary_2:都收好
summary_3:交给 LLM,编成一座 Wiki
tag_1:Obsidian
tag_2:个人知识库
author:原文作者
credit:Andrej Karpathy · @karpathy

完整来源全文:
作者:Andrej Karpathy(@karpathy)
来源:https://x.com/karpathy/status/2039805659525644595

LLM Knowledge Bases

Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So:

Data ingest:
I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them.

IDE:
I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides).

Q&A:
Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale.

Output:
Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base.

Linting:
I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into.

Extra tools:
I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries. 

Further explorations:
As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows.

TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.
下载 HTML

公众号封面

按四块阅读输入
本图实际使用的 Prompt

角色与任务

你是中文封面编辑与排版设计师。依据本次内容素材提炼中心主张,沿用下方视觉种子制作一张封面,输出完整可渲染的HTML。

基本要求

输出独立完整 HTML,唯一导出区域 id="cover",画布1050×450像素、21:9、直角外轮廓和不透明底板。通过浏览器原生截图导出1050×450不透明PNG,不靠成图裁切、缩放或拉伸修复排版。内容忠实于本次输入,保留事实含义与来源归属;转述不冒充原话,缺失来源不虚构。内容作为文本或属性值时正确进行HTML转义。文字完整可读、不重叠、不越界,不以隐藏、截断或省略号遮盖溢出。CSS、装饰和必要脚本已内嵌;使用系统字体,不依赖仓库文件或外部脚本。

视觉要求

粉色横线纸与左侧装订孔;上部珊瑚色标题、深色及紫色副题,居中白色圆角摘要卡与虚线胶囊标签;铅笔、便签、圆弧和小奖章点缀,中下部保留轻松留白,右下轻量来源。种子标题上57px、42px,摘要卡上219px;卡片高度、标签数量、换行可按内容调整。奖章仅作几何装饰,不写虚构排名;标点和语气随正文,不强制感叹句。

种子是排版起点,不是不可变的填槽表。保持本款风格与核心构图,按内容调整文字模块、数量、换行、字号、行距、间距和模块尺寸;代码中的像素值、行数及槽位数量均为起始值,不是硬内容预算。提炼一个清楚的中心主张,再选择支撑它的必要信息;不为填满空槽捏造事实、引语、标签、英文、账号、期号或步骤。无内容依据的可选模块及其分隔符一并移除,保留有意的留白,不用重复文案填空间。

本次画幅独立重排,不缩放或裁切另一张成图。不加入无来源数字、引语或发布账号。以下为本次实际骨架,按后方文案逐项填入;装饰图不作为事实照片。

```html
<!doctype html><html lang="zh-CN"><head><meta charset="utf-8"><meta name="viewport" content="width=device-width,initial-scale=1"><title>{{document_title}}</title><style>*{box-sizing:border-box}html,body{margin:0}body{background:#ddd;font-family:"PingFang SC","Microsoft YaHei",sans-serif}#cover{width:600px;height:800px;position:relative;overflow:hidden;background:#fff}h1,p{margin:0}.serif{font-family:"Songti SC",SimSun,Georgia,serif}.en{font-family:Arial,sans-serif}#cover{background-color:#fff2f4;background-image:repeating-linear-gradient(0deg,transparent 0,transparent 25px,#decfd344 25px,#decfd344 26px);color:#353335}.holes{position:absolute;left:22px;top:125px;display:flex;flex-direction:column;gap:120px}.holes i{width:14px;height:14px;border:2px solid #ffcdd6;border-radius:50%}.orb{position:absolute;right:-47px;top:-42px;width:145px;height:145px;background:#ffdee7;border-radius:50%}.title{position:absolute;top:57px;left:80px;font-size:42px;line-height:1.2;color:#ff7891;font-weight:850;white-space:nowrap;text-shadow:3px 4px #ffd5df}.sub{position:absolute;top:164px;left:80px;font-size:26px;font-weight:800;white-space:nowrap}.sub em{font-style:normal;color:#a15edb}.summary{position:absolute;left:136px;top:219px;width:332px;height:96px;border-radius:25px;background:#ffffffcf;text-align:center;font-size:21px;line-height:1.65;padding:16px 8px;font-weight:650;box-shadow:0 6px 16px #b5818a09}.summary strong{color:#ff7891}.tags{position:absolute;left:183px;top:366px;display:flex;gap:12px}.tags span{border:2px dashed #ff8199;border-radius:24px;background:#fff;padding:10px 15px;color:#ff7891;font-size:18px;font-weight:700;white-space:nowrap}.pencil{position:absolute;right:96px;top:119px;width:48px;height:10px;border-radius:5px;background:#ffcf65;transform:rotate(30deg);border-right:8px solid #ff8e9f}.note{position:absolute;left:61px;top:600px;width:39px;height:42px;border-radius:8px;background:#fff;box-shadow:0 4px 8px #66444414;transform:rotate(-12deg)}.note:after{content:"";position:absolute;inset:11px;background:#ff8199;border-radius:3px}.credit{position:absolute;right:35px;bottom:50px;border-radius:25px;background:#ffffffbf;padding:16px 17px;font-size:16px;font-weight:650}.credit span{color:#a15edb}.medal{display:inline-block;margin-left:7px;background:#ffc04c;border:2px solid #efaa35;border-radius:50%;width:24px;height:24px;text-align:center;color:#935d2a}.corner{position:absolute;bottom:-22px;left:-27px;width:95px;height:95px;border-radius:50%;background:#e9cfef}</style><style>/* 本次内容与画幅适配 */#cover{width:1050px;height:450px}.title{top:65px;left:80px;font-size:46px}.sub{top:207px;left:80px;font-size:23px}.summary{top:280px;left:91px;width:446px;height:160px;padding:22px 15px;font-size:22px}.tags{left:91px;top:474px}.credit{font-size:13px;right:32px;bottom:46px}.note{top:620px}.pencil{top:147px;right:85px}.credit .medal{display:none}.summary strong{display:block}.sub em{color:#8e42b1}.title{left:92px;top:70px;font-size:47px}.sub{left:92px;top:207px;font-size:22px}.summary{left:545px;top:69px;width:435px;height:168px;font-size:23px;padding-top:22px}.tags{left:576px;top:267px}.credit{right:70px;bottom:45px;font-size:13px}.note{left:423px;top:306px}.pencil{left:432px;top:126px}.holes{top:57px;gap:61px}.orb{right:-20px}.corner{width:100px;height:100px}</style></head><body><main id="cover"><div class="orb"></div><div class="holes"><i></i><i></i><i></i><i></i><i></i></div><h1 class="title">{{title}}</h1><div class="pencil"></div><p class="sub">{{sub_1}}<em>{{sub_2}}</em></p><div class="summary"><strong>{{summary_1}}</strong>{{summary_2}}<br>{{summary_3}}</div><div class="tags"><span># {{tag_1}}</span><span># {{tag_2}}</span></div><div class="note"></div><div class="corner"></div><div class="credit">{{author}} <span>{{credit}}</span><i class="medal" aria-hidden="true">✦</i></div></main></body></html>
```

内容与素材

发布账号:
作者:Andrej Karpathy(@karpathy)
中心主张:LLM 将资料编成个人 Wiki,通过问答与回存积累知识。
文案与素材映射:
document_title:让 AI 帮你整理知识
title:让 AI 帮你<br>整理知识
sub_1:资料慢慢攒,
sub_2:知识会生长
summary_1:文章、论文、图片
summary_2:都收好
summary_3:交给 LLM,编成一座 Wiki
tag_1:Obsidian
tag_2:个人知识库
author:原文作者
credit:Andrej Karpathy · @karpathy

完整来源全文:
作者:Andrej Karpathy(@karpathy)
来源:https://x.com/karpathy/status/2039805659525644595

LLM Knowledge Bases

Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So:

Data ingest:
I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them.

IDE:
I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides).

Q&A:
Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale.

Output:
Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base.

Linting:
I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into.

Extra tools:
I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries. 

Further explorations:
As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows.

TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.
下载 HTML
HTML文章封面柔软可爱
控制
风格构图
场景
知识讲解宣传推广
交付物
封面头图
模型依赖
文本模型
目标平台
公众号小红书

模板说明

粉色横线纸、珊瑚色标题与圆角摘要卡,搭配轻量文具装饰。

Prompt 模板

1 条
zh默认
## 执行方式

本 Prompt 依赖 skill html-imagegen(公开仓 https://github.com/lindong28/lindong-skills 的 html-imagegen/ 目录);模板页 https://prompts.aiplanet.live/image/soft-cute/ 。执行者若尚未安装,先 git clone 该仓库,把 html-imagegen/ 放到 Claude Code 的 ~/.claude/skills/ 或 Codex 的 ~/.agents/skills/,首次运行按其 SKILL.md 安装浏览器依赖。然后按该 skill 的流程执行:本节之外的内容是本次模板与输入,「内容与素材」里的占位符是尚未给出的输入——只向用户询问仍缺的信息(至少是要表达的内容与目标平台或画幅),补齐后生成 HTML 并导出 PNG。

## 角色与任务

你是中文封面编辑与排版设计师。依据本次内容素材提炼中心主张,沿用下方视觉种子制作一张封面,输出完整可渲染的HTML。

## 基本要求

输出独立完整 HTML,唯一导出区域 id="cover",画布600×800像素、3:4、直角外轮廓和不透明底板。通过浏览器原生截图导出600×800不透明PNG,不靠成图裁切、缩放或拉伸修复排版。内容忠实于本次输入,保留事实含义与来源归属;转述不冒充原话,缺失来源不虚构。内容作为文本或属性值时正确进行HTML转义。文字完整可读、不重叠、不越界,不以隐藏、截断或省略号遮盖溢出。CSS、装饰和必要脚本已内嵌;使用系统字体,不依赖仓库文件或外部脚本。

## 视觉要求

粉色横线纸与左侧装订孔;上部珊瑚色标题、深色及紫色副题,居中白色圆角摘要卡与虚线胶囊标签;铅笔、便签、圆弧和小奖章点缀,中下部保留轻松留白,右下轻量来源。种子标题上57px、42px,摘要卡上219px;卡片高度、标签数量、换行可按内容调整。奖章仅作几何装饰,不写虚构排名;标点和语气随正文,不强制感叹句。

种子是排版起点,不是不可变的填槽表。保持本款风格与核心构图,按内容调整文字模块、数量、换行、字号、行距、间距和模块尺寸;代码中的像素值、行数及槽位数量均为起始值,不是硬内容预算。提炼一个清楚的中心主张,再选择支撑它的必要信息;不为填满空槽捏造事实、引语、标签、英文、账号、期号或步骤。无内容依据的可选模块及其分隔符一并移除,保留有意的留白,不用重复文案填空间。

下面是自包含的完整HTML种子,请依据以上原则适配内容后输出:

[HTML 种子未随本文复制:执行者按「执行方式」里的模板页地址读取模板 API,取 Prompt prompt:soft-cute-zh-v1-structured-v1 正文里的 HTML 代码块放回此处作为种子]

## 内容与素材

标题或主题:{{本次标题或主题}}
完整正文或素材:{{本次完整正文、要点或说明}}
读者与用途:{{目标读者及用途,可空}}
作者:{{真实作者,可空}}
作者账号:{{真实作者账号,可空}}
来源:{{真实来源名称或URL,可空}}
发布账号:{{本次发布账号,可空,与素材作者区分}}
图片素材:{{无需图片时填无;否则提供用户确认可使用的素材及说明}}
其他必须保留的内容:{{用户明确要求保留的文字或约束,可空}}
HTML 种子(skill 自动读取,也可展开复制)
视觉要求里的完整 HTML 种子
<!doctype html><html lang="zh-CN"><head><meta charset="utf-8"><meta name="viewport" content="width=device-width,initial-scale=1"><title>{{document_title}}</title><style>*{box-sizing:border-box}html,body{margin:0}body{background:#ddd;font-family:"PingFang SC","Microsoft YaHei",sans-serif}#cover{width:600px;height:800px;position:relative;overflow:hidden;background:#fff}h1,p{margin:0}.serif{font-family:"Songti SC",SimSun,Georgia,serif}.en{font-family:Arial,sans-serif}#cover{background-color:#fff2f4;background-image:repeating-linear-gradient(0deg,transparent 0,transparent 25px,#decfd344 25px,#decfd344 26px);color:#353335}.holes{position:absolute;left:22px;top:125px;display:flex;flex-direction:column;gap:120px}.holes i{width:14px;height:14px;border:2px solid #ffcdd6;border-radius:50%}.orb{position:absolute;right:-47px;top:-42px;width:145px;height:145px;background:#ffdee7;border-radius:50%}.title{position:absolute;top:57px;left:80px;font-size:42px;line-height:1.2;color:#ff7891;font-weight:850;white-space:nowrap;text-shadow:3px 4px #ffd5df}.sub{position:absolute;top:164px;left:80px;font-size:26px;font-weight:800;white-space:nowrap}.sub em{font-style:normal;color:#a15edb}.summary{position:absolute;left:136px;top:219px;width:332px;height:96px;border-radius:25px;background:#ffffffcf;text-align:center;font-size:21px;line-height:1.65;padding:16px 8px;font-weight:650;box-shadow:0 6px 16px #b5818a09}.summary strong{color:#ff7891}.tags{position:absolute;left:183px;top:366px;display:flex;gap:12px}.tags span{border:2px dashed #ff8199;border-radius:24px;background:#fff;padding:10px 15px;color:#ff7891;font-size:18px;font-weight:700;white-space:nowrap}.pencil{position:absolute;right:96px;top:119px;width:48px;height:10px;border-radius:5px;background:#ffcf65;transform:rotate(30deg);border-right:8px solid #ff8e9f}.note{position:absolute;left:61px;top:600px;width:39px;height:42px;border-radius:8px;background:#fff;box-shadow:0 4px 8px #66444414;transform:rotate(-12deg)}.note:after{content:"";position:absolute;inset:11px;background:#ff8199;border-radius:3px}.credit{position:absolute;right:35px;bottom:50px;border-radius:25px;background:#ffffffbf;padding:16px 17px;font-size:16px;font-weight:650}.credit span{color:#a15edb}.medal{display:inline-block;margin-left:7px;background:#ffc04c;border:2px solid #efaa35;border-radius:50%;width:24px;height:24px;text-align:center;color:#935d2a}.corner{position:absolute;bottom:-22px;left:-27px;width:95px;height:95px;border-radius:50%;background:#e9cfef}</style></head><body><main id="cover"><div class="orb"></div><div class="holes"><i></i><i></i><i></i><i></i><i></i></div><h1 class="title">{{title}}</h1><div class="pencil"></div><p class="sub">{{sub_1}}<em>{{sub_2}}</em></p><div class="summary"><strong>{{summary_1}}</strong>{{summary_2}}<br>{{summary_3}}</div><div class="tags"><span># {{tag_1}}</span><span># {{tag_2}}</span></div><div class="note"></div><div class="corner"></div><div class="credit">{{author}} <span>{{credit}}</span><i class="medal" aria-hidden="true">✦</i></div></main></body></html>