alterlab-paper-2-web — independently scanned and version-tracked by SaferSkills.
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First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
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:
If a diagram or figure would aid comprehension, invoke the alterlab-scientific-schematics skill (diagrams/schematics) or the alterlab-generate-image skill (images). Figures are optional — add them only where they improve clarity.
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
git clone https://github.com/YuhangChen1/Paper2All.git
cd Paper2All
conda create -n p2w python=3.11
conda activate p2w
pip install -r requirements.txtPaper2Video runs in its own environment (conda create -n p2v python=3.10), and the optional talking-head module in a third (conda create -n hallo python=3.10).
.env file): OPENAI_API_KEY=your_openai_api_key_here
OPENAI_API_BASE=https://api.openai.com/v1 # or https://openrouter.ai/api/v1 for OpenRouter
# Optional: GOOGLE_SEARCH_API_KEY and GOOGLE_SEARCH_ENGINE_ID for logo search→ See `references/installation.md` for complete installation guide
pipeline_all.py drives the website, poster, and PR-material modules. The --model-choice flag selects which component to run — it is NOT a model selector. Omit it to run all modules (with automatic PDF detection).
--model-choice | Component |
|---|---|
| (omitted) | All modules |
1 | Website (Paper2Web) only |
2 | Poster (Paper2Poster) only |
3 | PR materials (AutoPR) only |
Generate Everything (website + poster + PR materials):
python pipeline_all.py \
--input-dir "path/to/paper" \
--output-dir "path/to/output"Generate Website Only:
python pipeline_all.py \
--input-dir "path/to/paper" \
--output-dir "path/to/output" \
--model-choice 1Generate Poster with Custom Size:
python pipeline_all.py \
--input-dir "path/to/paper" \
--output-dir "path/to/output" \
--model-choice 2 \
--poster-width-inches 60 \
--poster-height-inches 40Generate Video (separate p2v environment, lightweight pipeline — no talking-head):
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"For the full video pipeline with a Hallo2 talking-head, use pipeline.py with --ref_img, --ref_audio, and --gpu_list (requires the hallo env and an NVIDIA A6000 48GB GPU).
→ 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 components1. LaTeX Source (Recommended):
paper_directory/
├── main.tex # Main paper file
├── sections/ # Optional: split sections
├── figures/ # All figure files
├── tables/ # Table files
└── bibliography.bib # References2. PDF:
Single Paper:
input/
└── paper_name/
├── main.tex (or paper.pdf)
├── figures/
└── bibliography.bibMultiple Papers (batch processing):
input/
├── paper1/
│ └── main.tex
├── paper2/
│ └── main.tex
└── paper3/
└── main.texpipeline_all.py)--model-choice 1: Website (Paper2Web) only--model-choice 2: Poster (Paper2Poster) only--model-choice 3: PR materials (AutoPR) only--model-choice): Run all modules--model_name_t: Model for text/script generation (e.g. gpt-4.1)--model_name_v: Model for visual/slide generation (e.g. gpt-4.1)pipeline_all.py)--poster-width-inches [width]: Custom poster width--poster-height-inches [height]: Custom poster heightGenerated 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/The LLM is configured via OPENAI_API_KEY / OPENAI_API_BASE in .env (point the base at OpenRouter to use Claude or other models). The video pipeline takes explicit --model_name_t / --model_name_v overrides:
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 contentXiaohongshu/小红书 (Chinese, alphanumeric folder names):
mkdir -p input/xhs_paper/
# Generates Chinese promotional contentSpecify 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 &
done
waitApply institution or lab branding:
Generate content in different languages:
This skill includes comprehensive reference documentation:
External Resources:
The Paper2All system includes built-in quality assessment:
All outputs undergo automated quality checks before generation completes.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.