Paper2All学术论文转化工具：一键生成网站、视频、海报，提升研究影响力

paper-2-web by davila7/claude-code-templates

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安装命令

npx skills add https://github.com/davila7/claude-code-templates --skill paper-2-web

内容创作自动化科研工具

🇨🇳中文介绍

Paper2All：学术论文转化流水线

概述

此技能使用 Paper2All 自动化流水线，将学术论文转化为多种宣传和演示格式。该系统将研究论文（LaTeX 或 PDF）转化为三种主要输出：

Paper2Web：具有布局感知设计的交互式、可探索的学术主页
Paper2Video：带有旁白、幻灯片和可选人像画面的专业演示视频
Paper2Poster：具有专业版式的可打印会议海报

该流水线利用 LLM 驱动的内容提取、设计生成和迭代优化，创建适合会议、期刊、预印本库和学术推广的高质量输出。

使用场景

在以下情况下使用此技能：

创建会议材料：为学术会议制作海报、演示视频和配套网站
推广研究：将已发表论文或预印本转化为易于访问、引人入胜的网络格式
准备演示文稿：根据论文内容生成视频摘要或完整演示视频
传播研究成果：为社交媒体、实验室网站或机构展示创建宣传材料
增强预印本：为 bioRxiv、arXiv 或其他预印本提交添加交互式主页
批量处理：同时为多篇论文生成宣传材料

触发短语：

"将这篇论文转化为网站"
"根据我的 LaTeX 论文生成会议海报"
"根据这项研究创建视频演示"
"为我的论文制作交互式主页"
"将我的论文转化为宣传材料"
"为我的会议演讲生成海报和视频"

利用科学示意图增强视觉效果

使用此技能创建文档时，请始终考虑添加科学图表和示意图以增强视觉传达。

如果您的文档尚未包含示意图或图表：

广告位招租

在这里展示您的产品或服务

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1. Paper2Web：交互式网站生成

将论文转化为具有布局感知的交互式学术主页，超越简单的 HTML 转换。

响应式、多版块布局，适应论文内容
交互式图表、表格和引用
移动端友好的导航设计
自动徽标发现（使用 Google Search API）
美学优化和质量评估

最适合：发表后推广、预印本增强、实验室网站、永久性研究展示

→ 详细文档请参阅 references/paper2web.md

2. Paper2Video：演示视频生成

生成带有幻灯片、旁白、光标移动和可选人像视频的专业演示视频。

根据论文结构自动生成幻灯片
自然语音合成
同步的光标移动和高亮显示
可选的人像视频（使用 Hallo2，需要 GPU）
多语言支持

最适合：视频摘要、会议演示、在线讲座、课程材料、YouTube 推广

→ 详细文档请参阅 references/paper2video.md

3. Paper2Poster：会议海报生成

创建具有专业版式和视觉设计的可打印学术海报。

自定义海报尺寸（任意大小）
专业设计模板
机构品牌支持
链接二维码生成
高分辨率输出（300+ DPI）

最适合：会议海报展、研讨会、学术展览、虚拟会议

→ 详细文档请参阅 references/paper2poster.md

安装 Paper2All：

git clone https://github.com/YuhangChen1/Paper2All.git
cd Paper2All
conda create -n paper2all python=3.11
conda activate paper2all
pip install -r requirements.txt

配置 API 密钥（创建 .env 文件）：

OPENAI_API_KEY=your_openai_api_key_here
# 可选：用于徽标搜索的 GOOGLE_API_KEY 和 GOOGLE_CSE_ID

安装系统依赖项：
- LibreOffice（文档转换）
- Poppler 工具（PDF 处理）
- NVIDIA GPU 48GB（可选，用于人像视频）

→ 完整安装指南请参阅 references/installation.md

生成所有组件（网站 + 海报 + 视频）：

python pipeline_all.py \
  --input-dir "path/to/paper" \
  --output-dir "path/to/output" \
  --model-choice 1

仅生成网站：

python pipeline_all.py \
  --input-dir "path/to/paper" \
  --output-dir "path/to/output" \
  --model-choice 1 \
  --generate-website

使用自定义尺寸生成海报：

python pipeline_all.py \
  --input-dir "path/to/paper" \
  --output-dir "path/to/output" \
  --model-choice 1 \
  --generate-poster \
  --poster-width-inches 60 \
  --poster-height-inches 40

生成视频（轻量级流水线）：

python pipeline_light.py \
  --model_name_t gpt-4.1 \
  --model_name_v gpt-4.1 \
  --result_dir "path/to/output" \
  --paper_latex_root "path/to/paper"

→ 完整工作流程示例请参阅 references/usage_examples.md

工作流程决策树

使用此决策树来确定生成哪些组件：

User needs promotional materials for paper?
│
├─ Need permanent online presence?
│  └─→ Generate Paper2Web (interactive website)
│
├─ Need physical conference materials?
│  ├─→ Poster session? → Generate Paper2Poster
│  └─→ Oral presentation? → Generate Paper2Video
│
├─ Need video content?
│  ├─→ Journal video abstract? → Generate Paper2Video (5-10 min)
│  ├─→ Conference talk? → Generate Paper2Video (15-20 min)
│  └─→ Social media? → Generate Paper2Video (1-3 min)
│
└─ Need complete package?
   └─→ Generate all three components

支持的输入格式

1. LaTeX 源代码（推荐）：

paper_directory/
├── main.tex              # 主论文文件
├── sections/             # 可选：分章节文件
├── figures/              # 所有图形文件
├── tables/               # 表格文件
└── bibliography.bib      # 参考文献

具有嵌入字体的高质量 PDF
可选择的文本（非扫描图像）
高分辨率图形（推荐 300+ DPI）

input/
└── paper_name/
    ├── main.tex (或 paper.pdf)
    ├── figures/
    └── bibliography.bib

多篇论文（批量处理）：

input/
├── paper1/
│   └── main.tex
├── paper2/
│   └── main.tex
└── paper3/
    └── main.tex

--model-choice 1：GPT-4（质量与成本的最佳平衡）
--model-choice 2：GPT-4.1（最新功能，成本更高）
--model_name_t gpt-3.5-turbo：更快，成本更低（质量可接受）

--generate-website：启用网站生成
--generate-poster：启用海报生成
--generate-video：启用视频生成
--enable-talking-head：为视频添加人像（需要 GPU）

--poster-width-inches [width]：自定义海报宽度
--poster-height-inches [height]：自定义海报高度
--video-duration [seconds]：目标视频长度
--enable-logo-search：自动发现机构徽标

生成的输出按论文和组件组织：

output/
└── paper_name/
    ├── website/
    │   ├── index.html
    │   ├── styles.css
    │   └── assets/
    ├── poster/
    │   ├── poster_final.pdf
    │   ├── poster_final.png
    │   └── poster_source/
    └── video/
        ├── final_video.mp4
        ├── slides/
        ├── audio/
        └── subtitles/

尽可能使用 LaTeX：提供最佳的内容提取和结构
正确组织文件：将所有资源（图形、表格、参考文献）保存在论文目录中
高质量图形：使用矢量格式（PDF、SVG）或高分辨率位图（300+ DPI）
干净的 LaTeX：移除编译产物，确保源代码编译成功

GPT-4：最适合生产质量输出、会议、出版物
GPT-4.1：当您需要最新功能或最佳质量时使用
GPT-3.5-turbo：用于快速草稿、测试或简单论文

对于紧迫的截止日期，按此顺序生成：

网站（最快，最通用，约 15-30 分钟）
海报（中等速度，用于打印截止日期，约 10-20 分钟）
视频（最慢，可以稍后生成，约 20-60 分钟）

在最终确定输出之前：

网站：在多个设备上测试，验证所有链接有效，检查图形质量
海报：打印测试页，验证在 3-6 英尺距离的文本可读性，检查颜色
视频：观看整个视频，验证音频同步，在不同设备上测试

网站：每篇论文 15-30 分钟
海报：每篇论文 10-20 分钟
视频（无人像）：每篇论文 20-60 分钟
视频（带人像）：每篇论文 60-120 分钟

CPU：用于并行处理的多核处理器
RAM：最低 16GB，大型论文推荐 32GB
GPU：标准输出可选，人像视频需要（NVIDIA A6000 48GB）
存储：每篇论文 1-5GB，取决于组件和质量设置

API 成本（近似）

网站：每篇论文 $0.50-2.00（GPT-4）
海报：每篇论文 $0.30-1.00（GPT-4）
视频：每篇论文 $1.00-3.00（GPT-4）
完整套餐：每篇论文 $2.00-6.00（GPT-4）

LaTeX 解析错误：

确保 LaTeX 源代码编译成功：pdflatex main.tex
检查所有引用的文件都存在
验证没有自定义包阻止解析

图形质量差：

使用矢量格式（PDF、SVG、EPS）代替位图
确保位图图像为 300+ DPI
检查图形在编译的 PDF 中正确渲染

视频生成失败：

验证有足够的磁盘空间（推荐 5GB+）
检查所有依赖项已安装（LibreOffice、Poppler）
查看输出目录中的错误日志

海报布局问题：

验证海报尺寸合理（24"-72" 范围）
检查内容长度（非常长的论文可能需要手动整理）
确保图形具有适合海报尺寸的分辨率

验证 .env 文件中的 API 密钥
检查 API 信用余额
确保没有速率限制（等待并重试）

系统自动检测目标平台：

Twitter/X（英文，数字文件夹名称）：

mkdir -p input/001_twitter/
# 生成英文宣传内容

Xiaohongshu/小红书（中文，字母数字文件夹名称）：

mkdir -p input/xhs_paper/
# 生成中文宣传内容

指定会议要求：

标准海报尺寸（4'×3'、5'×4'、A0、A1）
视频摘要长度限制（通常 3-5 分钟）
机构品牌要求
配色方案偏好

将生成的网站部署到：

GitHub Pages：免费托管，支持自定义域名
学术托管：大学网络服务器
个人服务器：AWS、DigitalOcean 等
Netlify/Vercel：具有 CI/CD 的现代托管

打印就绪的文件适用于：

专业海报打印服务
大学打印店
在线服务（例如 Spoonflower、VistaPrint）
大幅面打印机（如果可用）

在以下平台分享视频：

YouTube：公开或未列出以获得最大覆盖范围
机构存储库：大学视频平台
会议平台：虚拟会议系统
社交媒体：Twitter、LinkedIn、ResearchGate

高效处理多篇论文：

# 在批处理目录中组织论文
for paper in paper1 paper2 paper3; do
    python pipeline_all.py \
      --input-dir input/$paper \
      --output-dir output/$paper \
      --model-choice 1 &
done
wait

应用机构或实验室品牌：

在论文目录中提供徽标文件
在配置中指定配色方案
使用自定义模板（高级）
匹配会议主题要求

以不同语言生成内容：

在配置中指定目标语言
系统适当地翻译内容
为视频旁白选择合适的声音
使设计约定适应文化

参考资料与资源

此技能包含全面的参考文档：

references/installation.md：完整的安装和配置指南
references/paper2web.md：包含所有功能的详细 Paper2Web 文档
references/paper2video.md：包含人像设置的综合 Paper2Video 指南
references/paper2poster.md：包含设计模板的完整 Paper2Poster 文档
references/usage_examples.md：真实世界示例和工作流程模式

GitHub 仓库：https://github.com/YuhangChen1/Paper2All
精选数据集：可在 Hugging Face 上获取（13 个研究类别）
基准测试套件：参考网站和评估指标

评估与质量指标

Paper2All 系统包含内置的质量评估：

完整性：论文内容的覆盖范围
准确性：对研究结果的忠实呈现
清晰度：可访问性和可理解性
信息量：关键信息的突出程度

美学：视觉吸引力和专业性
布局：平衡、层次结构和组织
可读性：文本易读性和图形清晰度
一致性：统一的样式和品牌

性能：加载时间、响应性
兼容性：跨浏览器、跨设备支持
可访问性：WCAG 合规性、屏幕阅读器支持
标准：有效的 HTML/CSS、打印就绪的 PDF

所有输出在生成完成前都经过自动质量检查。

🇺🇸English

Paper2All: Academic Paper Transformation Pipeline

Overview

This skill enables the transformation of academic papers into multiple promotional and presentation formats using the Paper2All autonomous pipeline. The system converts research papers (LaTeX or PDF) into three primary outputs:

Paper2Web : Interactive, explorable academic homepages with layout-aware design
Paper2Video : Professional presentation videos with narration, slides, and optional talking-head
Paper2Poster : Print-ready conference posters with professional layouts

The pipeline uses LLM-powered content extraction, design generation, and iterative refinement to create high-quality outputs suitable for conferences, journals, preprint repositories, and academic promotion.

When to Use This Skill

Use this skill when:

Creating conference materials : Posters, presentation videos, and companion websites for academic conferences
Promoting research : Converting published papers or preprints into accessible, engaging web formats
Preparing presentations : Generating video abstracts or full presentation videos from paper content
Disseminating findings : Creating promotional materials for social media, lab websites, or institutional showcases
Enhancing preprints : Adding interactive homepages to bioRxiv, arXiv, or other preprint submissions
Batch processing : Generating promotional materials for multiple papers simultaneously

Trigger phrases :

"Convert this paper to a website"
"Generate a conference poster from my LaTeX paper"
"Create a video presentation from this research"
"Make an interactive homepage for my paper"
"Transform my paper into promotional materials"
"Generate a poster and video for my conference talk"

Visual Enhancement with Scientific Schematics

When creating documents with this skill, always consider adding scientific diagrams and schematics to enhance visual communication.

If your document does not already contain schematics or diagrams:

Use the scientific-schematics skill to generate AI-powered publication-quality diagrams
Simply describe your desired diagram in natural language
Nano Banana Pro will automatically generate, review, and refine the schematic

For new documents: Scientific schematics should be generated by default to visually represent key concepts, workflows, architectures, or relationships described in the text.

How to generate schematics:

python scripts/generate_schematic.py "your diagram description" -o figures/output.png

The AI will automatically:

Create publication-quality images with proper formatting
Review and refine through multiple iterations
Ensure accessibility (colorblind-friendly, high contrast)
Save outputs in the figures/ directory

When to add schematics:

Paper transformation pipeline diagrams
Website layout architecture diagrams
Video production workflow illustrations
Poster design process flowcharts
Content extraction diagrams
System architecture visualizations
Any complex concept that benefits from visualization

For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.

Core Capabilities

1. Paper2Web: Interactive Website Generation

Converts papers into layout-aware, interactive academic homepages that go beyond simple HTML conversion.

Key Features :

Responsive, multi-section layouts adapted to paper content
Interactive figures, tables, and citations
Mobile-friendly design with navigation
Automatic logo discovery (with Google Search API)
Aesthetic refinement and quality assessment

Best For : Post-publication promotion, preprint enhancement, lab websites, permanent research showcases

→ Seereferences/paper2web.md for detailed documentation

2. Paper2Video: Presentation Video Generation

Generates professional presentation videos with slides, narration, cursor movements, and optional talking-head video.

Key Features :

Automated slide generation from paper structure
Natural-sounding speech synthesis
Synchronized cursor movements and highlights
Optional talking-head video using Hallo2 (requires GPU)
Multi-language support

Best For : Video abstracts, conference presentations, online talks, course materials, YouTube promotion

→ Seereferences/paper2video.md for detailed documentation

3. Paper2Poster: Conference Poster Generation

Creates print-ready academic posters with professional layouts and visual design.

Key Features :

Custom poster dimensions (any size)
Professional design templates
Institution branding support
QR code generation for links
High-resolution output (300+ DPI)

Best For : Conference poster sessions, symposiums, academic exhibitions, virtual conferences

→ Seereferences/paper2poster.md for detailed documentation

Quick Start

Prerequisites

Install Paper2All :

git clone https://github.com/YuhangChen1/Paper2All.git
cd Paper2All
conda create -n paper2all python=3.11
conda activate paper2all
pip install -r requirements.txt

Configure API Keys (create .env file):

OPENAI_API_KEY=your_openai_api_key_here
# Optional: GOOGLE_API_KEY and GOOGLE_CSE_ID for logo search

Install System Dependencies :
- LibreOffice (document conversion)
- Poppler utilities (PDF processing)
- NVIDIA GPU with 48GB (optional, for talking-head videos)

→ Seereferences/installation.md for complete installation guide

Basic Usage

Generate All Components (website + poster + video):

python pipeline_all.py \
  --input-dir "path/to/paper" \
  --output-dir "path/to/output" \
  --model-choice 1

Generate Website Only :

python pipeline_all.py \
  --input-dir "path/to/paper" \
  --output-dir "path/to/output" \
  --model-choice 1 \
  --generate-website

Generate Poster with Custom Size :

python pipeline_all.py \
  --input-dir "path/to/paper" \
  --output-dir "path/to/output" \
  --model-choice 1 \
  --generate-poster \
  --poster-width-inches 60 \
  --poster-height-inches 40

Generate Video (lightweight pipeline):

python pipeline_light.py \
  --model_name_t gpt-4.1 \
  --model_name_v gpt-4.1 \
  --result_dir "path/to/output" \
  --paper_latex_root "path/to/paper"

→ Seereferences/usage_examples.md for comprehensive workflow examples

Workflow Decision Tree

Use this decision tree to determine which components to generate:

User needs promotional materials for paper?
│
├─ Need permanent online presence?
│  └─→ Generate Paper2Web (interactive website)
│
├─ Need physical conference materials?
│  ├─→ Poster session? → Generate Paper2Poster
│  └─→ Oral presentation? → Generate Paper2Video
│
├─ Need video content?
│  ├─→ Journal video abstract? → Generate Paper2Video (5-10 min)
│  ├─→ Conference talk? → Generate Paper2Video (15-20 min)
│  └─→ Social media? → Generate Paper2Video (1-3 min)
│
└─ Need complete package?
   └─→ Generate all three components

Input Requirements

Supported Input Formats

1. LaTeX Source (Recommended):

paper_directory/
├── main.tex              # Main paper file
├── sections/             # Optional: split sections
├── figures/              # All figure files
├── tables/               # Table files
└── bibliography.bib      # References

2. PDF :

High-quality PDF with embedded fonts
Selectable text (not scanned images)
High-resolution figures (300+ DPI preferred)

Input Organization

Single Paper :

input/
└── paper_name/
    ├── main.tex (or paper.pdf)
    ├── figures/
    └── bibliography.bib

Multiple Papers (batch processing):

input/
├── paper1/
│   └── main.tex
├── paper2/
│   └── main.tex
└── paper3/
    └── main.tex

Common Parameters

Model Selection

--model-choice 1: GPT-4 (best balance of quality and cost)
--model-choice 2: GPT-4.1 (latest features, higher cost)
--model_name_t gpt-3.5-turbo: Faster, lower cost (acceptable quality)

Component Selection

--generate-website: Enable website generation
--generate-poster: Enable poster generation
--generate-video: Enable video generation
--enable-talking-head: Add talking-head to video (requires GPU)

Customization

--poster-width-inches [width]: Custom poster width
--poster-height-inches [height]: Custom poster height
--video-duration [seconds]: Target video length
--enable-logo-search: Automatic institution logo discovery

Output Structure

Generated outputs are organized by paper and component:

output/
└── paper_name/
    ├── website/
    │   ├── index.html
    │   ├── styles.css
    │   └── assets/
    ├── poster/
    │   ├── poster_final.pdf
    │   ├── poster_final.png
    │   └── poster_source/
    └── video/
        ├── final_video.mp4
        ├── slides/
        ├── audio/
        └── subtitles/

Best Practices

Input Preparation

Use LaTeX when possible : Provides best content extraction and structure
Organize files properly : Keep all assets (figures, tables, bibliography) in paper directory
High-quality figures : Use vector formats (PDF, SVG) or high-resolution rasters (300+ DPI)
Clean LaTeX : Remove compilation artifacts, ensure source compiles successfully

Model Selection Strategy

GPT-4 : Best for production-quality outputs, conferences, publications
GPT-4.1 : Use when you need latest features or best possible quality
GPT-3.5-turbo : Use for quick drafts, testing, or simple papers

Component Priority

For tight deadlines, generate in this order:

Website (fastest, most versatile, ~15-30 min)
Poster (moderate speed, for print deadlines, ~10-20 min)
Video (slowest, can be generated later, ~20-60 min)

Quality Assurance

Before finalizing outputs:

Website : Test on multiple devices, verify all links work, check figure quality
Poster : Print test page, verify text readability from 3-6 feet, check colors
Video : Watch entire video, verify audio synchronization, test on different devices

Resource Requirements

Processing Time

Website : 15-30 minutes per paper
Poster : 10-20 minutes per paper
Video (no talking-head) : 20-60 minutes per paper
Video (with talking-head) : 60-120 minutes per paper

Computational Requirements

CPU : Multi-core processor for parallel processing
RAM : 16GB minimum, 32GB recommended for large papers
GPU : Optional for standard outputs, required for talking-head (NVIDIA A6000 48GB)
Storage : 1-5GB per paper depending on components and quality settings

API Costs (Approximate)

Website : $0.50-2.00 per paper (GPT-4)
Poster : $0.30-1.00 per paper (GPT-4)
Video : $1.00-3.00 per paper (GPT-4)
Complete package : $2.00-6.00 per paper (GPT-4)

Troubleshooting

Common Issues

LaTeX parsing errors :

Ensure LaTeX source compiles successfully: pdflatex main.tex
Check all referenced files are present
Verify no custom packages prevent parsing

Poor figure quality :

Use vector formats (PDF, SVG, EPS) instead of rasters
Ensure raster images are 300+ DPI
Check figures render correctly in compiled PDF

Video generation failures :

Verify sufficient disk space (5GB+ recommended)
Check all dependencies installed (LibreOffice, Poppler)
Review error logs in output directory

Poster layout issues :

Verify poster dimensions are reasonable (24"-72" range)
Check content length (very long papers may need manual curation)
Ensure figures have appropriate resolution for poster size

API errors :

Verify API keys in .env file
Check API credit balance
Ensure no rate limiting (wait and retry)

Platform-Specific Features

Social Media Optimization

The system auto-detects target platforms:

Twitter/X (English, numeric folder names):

mkdir -p input/001_twitter/
# Generates English promotional content

Xiaohongshu/小红书 (Chinese, alphanumeric folder names):

mkdir -p input/xhs_paper/
# Generates Chinese promotional content

Conference-Specific Formatting

Specify conference requirements:

Standard poster sizes (4'×3', 5'×4', A0, A1)
Video abstract length limits (typically 3-5 minutes)
Institution branding requirements
Color scheme preferences

Integration and Deployment

Website Deployment

Deploy generated websites to:

GitHub Pages : Free hosting with custom domain
Academic hosting : University web servers
Personal servers : AWS, DigitalOcean, etc.
Netlify/Vercel : Modern hosting with CI/CD

Poster Printing

Print-ready files work with:

Professional poster printing services
University print shops
Online services (e.g., Spoonflower, VistaPrint)
Large format printers (if available)

Video Distribution

Share videos on:

YouTube : Public or unlisted for maximum reach
Institutional repositories : University video platforms
Conference platforms : Virtual conference systems
Social media : Twitter, LinkedIn, ResearchGate

Advanced Usage

Batch Processing

Process multiple papers efficiently:

# Organize papers in batch directory
for paper in paper1 paper2 paper3; do
    python pipeline_all.py \
      --input-dir input/$paper \
      --output-dir output/$paper \
      --model-choice 1 &
done
wait

Custom Branding

Apply institution or lab branding:

Provide logo files in paper directory
Specify color schemes in configuration
Use custom templates (advanced)
Match conference theme requirements

Multi-Language Support

Generate content in different languages:

Specify target language in configuration
System translates content appropriately
Selects appropriate voice for video narration
Adapts design conventions to culture

References and Resources

This skill includes comprehensive reference documentation:

references/installation.md : Complete installation and configuration guide
references/paper2web.md : Detailed Paper2Web documentation with all features
references/paper2video.md : Comprehensive Paper2Video guide including talking-head setup
references/paper2poster.md : Complete Paper2Poster documentation with design templates
references/usage_examples.md : Real-world examples and workflow patterns

External Resources :

GitHub Repository: https://github.com/YuhangChen1/Paper2All
Curated Dataset: Available on Hugging Face (13 research categories)
Benchmark Suite: Reference websites and evaluation metrics

Evaluation and Quality Metrics

The Paper2All system includes built-in quality assessment:

Content Quality

Completeness : Coverage of paper content
Accuracy : Faithful representation of findings
Clarity : Accessibility and understandability
Informativeness : Key information prominence

Design Quality

Aesthetics : Visual appeal and professionalism
Layout : Balance, hierarchy, and organization
Readability : Text legibility and figure clarity
Consistency : Uniform styling and branding

Technical Quality

Performance : Load times, responsiveness
Compatibility : Cross-browser, cross-device support
Accessibility : WCAG compliance, screen reader support
Standards : Valid HTML/CSS, print-ready PDFs

All outputs undergo automated quality checks before generation completes.

Weekly Installs

174

Repository

davila7/claude-…emplates

GitHub Stars

22.6K

First Seen

Jan 21, 2026

Security Audits

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Installed on

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GitHub Actions 官方文档查询助手 - 精准解答 CI/CD 工作流问题

30,000 周安装

Paper2All学术论文转化工具：一键生成网站、视频、海报，提升研究影响力

🇨🇳中文介绍

Paper2All：学术论文转化流水线

概述

使用场景

利用科学示意图增强视觉效果

相关 Skills

核心功能

1. Paper2Web：交互式网站生成

2. Paper2Video：演示视频生成

3. Paper2Poster：会议海报生成

快速开始

先决条件

基本用法

工作流程决策树

输入要求

支持的输入格式

输入组织

常用参数

模型选择

组件选择

自定义

输出结构

最佳实践

输入准备

模型选择策略

组件优先级

质量保证

资源要求

处理时间

计算要求

API 成本（近似）

故障排除

常见问题

平台特定功能

社交媒体优化

会议特定格式

集成与部署

网站部署

海报打印

视频分发

高级用法

批量处理

自定义品牌

多语言支持

参考资料与资源

评估与质量指标

内容质量

设计质量

技术质量

🇺🇸English

Paper2All: Academic Paper Transformation Pipeline

Overview

When to Use This Skill

Visual Enhancement with Scientific Schematics

Core Capabilities

1. Paper2Web: Interactive Website Generation

2. Paper2Video: Presentation Video Generation

3. Paper2Poster: Conference Poster Generation

Quick Start

Prerequisites

Basic Usage

Workflow Decision Tree

Input Requirements

Supported Input Formats

Input Organization

Common Parameters

Model Selection

Component Selection

Customization

Output Structure

Best Practices

Input Preparation

Model Selection Strategy

Component Priority

Quality Assurance

Resource Requirements

Processing Time

Computational Requirements

API Costs (Approximate)