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Use when converting academic papers into dissemination assets such as Paper2Web websites, Paper2Video video abstracts, or Paper2Poster conference posters from LaTeX or PDF sources.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/foryourhealth111-pixel/Vibe-Skills --skill paper-2-web命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Vibe Code Orchestrator (VCO) is a governed runtime entry that freezes requirements, bounds execution, and enforces verification and phase cleanup.
Full-stack software development agent for design, implementation, testing, and deployment. Use when the user explicitly asks for end-to-end project creation, feature development, bug fixing, or code refactoring.
Git提交与调试反思报告生成技能。用于分析开发过程中的错误、调试步骤和解决方案,生成结构化的中文反思报告,并创建包含报告引用的Git提交。显式请求词:反思提交、智能提交、生成调试报告、commit with reflection。
正在显示 SKILL.md
基于 SOC 职业分类
| name | paper-2-web |
| description | Use when converting academic papers into dissemination assets such as Paper2Web websites, Paper2Video video abstracts, or Paper2Poster conference posters from LaTeX or PDF sources. |
| allowed-tools | Read Write Edit Bash |
| license | Unknown |
| metadata | {"skill-author":"K-Dense Inc."} |
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:
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.
Use this skill when:
Trigger phrases:
This skill owns transformation of an existing paper into dissemination assets. It can reuse provided figures and paper assets, but it does not generate new scientific diagrams, run data analysis, write the original manuscript, or require a separate visual specialist as part of normal transformation.
Converts papers into layout-aware, interactive academic homepages that go beyond simple HTML conversion.
Key Features:
Best For: Post-publication promotion, preprint enhancement, lab websites, permanent research showcases
→ See references/paper2web.md for detailed documentation
Generates professional presentation videos with slides, narration, cursor movements, and optional talking-head video.
Key Features:
Best For: Video abstracts, conference presentations, online talks, course materials, YouTube promotion
→ See references/paper2video.md for detailed documentation
Creates print-ready academic posters with professional layouts and visual design.
Key Features:
Best For: Conference poster sessions, symposiums, academic exhibitions, virtual conferences
→ See references/paper2poster.md for detailed documentation
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:
→ See references/installation.md for complete installation guide
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"
→ See references/usage_examples.md for comprehensive workflow examples
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
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:
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
--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)--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)--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 discoveryGenerated 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/
For tight deadlines, generate in this order:
Before finalizing outputs:
LaTeX parsing errors:
pdflatex main.texPoor figure quality:
Video generation failures:
Poster layout issues:
API errors:
.env fileThe 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
Specify conference requirements:
Deploy generated websites to:
Print-ready files work with:
Share videos on:
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
Apply institution or lab branding:
Generate content in different languages:
This skill includes comprehensive reference documentation:
references/installation.md: Complete installation and configuration guidereferences/paper2web.md: Detailed Paper2Web documentation with all featuresreferences/paper2video.md: Comprehensive Paper2Video guide including talking-head setupreferences/paper2poster.md: Complete Paper2Poster documentation with design templatesreferences/usage_examples.md: Real-world examples and workflow patternsExternal Resources:
The Paper2All system includes built-in quality assessment:
All outputs undergo automated quality checks before generation completes.