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:
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionpaper-2-webExecute the skills CLI command in your project's root directory to begin installation:
Fetches paper-2-web from davila7/claude-code-templates and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate paper-2-web. Access via /paper-2-web in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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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:
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:
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:
When to add schematics:
For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.
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 templatesrefereMake data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
davila7/claude-code-templates
supercent-io/skills-template
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
Useful defaults in paper-2-web — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
We added paper-2-web from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend paper-2-web for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: paper-2-web is the kind of skill you can hand to a new teammate without a long onboarding doc.
We added paper-2-web from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
paper-2-web has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: paper-2-web is the kind of skill you can hand to a new teammate without a long onboarding doc.
Solid pick for teams standardizing on skills: paper-2-web is focused, and the summary matches what you get after install.
Solid pick for teams standardizing on skills: paper-2-web is focused, and the summary matches what you get after install.
paper-2-web is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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