An automated AI tool that converts complex academic research PDFs into presentation-ready PowerPoint posters and HTML layouts.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install newpaper-skill
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install newpaper-skill using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Paper2Poster?
Paper2Poster is a sophisticated automation tool designed to bridge the gap between academic research and visual presentation. By utilizing advanced LLM parsing, it analyzes the structure of research papers and reformats the content into high-quality PowerPoint (PPTX) and HTML posters. This skill is a standout example of how Openclaw Skills can enhance research productivity by automating the tedious task of poster design while maintaining technical accuracy.
The tool is compatible with a wide range of OpenAI-standard APIs, including UniAPI and MiniMax, providing flexibility in model selection for both text and vision processing. It ensures that the transition from a dense PDF to a visual summary is seamless, professional, and customizable for institutional requirements.
Paper2Poster Use Cases
- Creating academic posters for international conferences directly from research PDFs.
- Generating web-ready HTML summaries of technical papers for lab websites.
- Automating the design of presentation materials for internal research reviews.
- Quickly incorporating university or laboratory branding into research posters.
How Paper2Poster Works
- The skill clones the source repository into the current directory and verifies the structure.
- It initializes a dedicated Python 3.10 Conda environment to ensure dependency isolation.
- The agent installs core libraries and specific versions of the docling parser for high-fidelity PDF extraction.
- It configures API credentials and environment variables for the selected LLM provider.
- The system processes the source PDF and optional institutional logos through a specialized generation pipeline.
- Final assets are produced in the generated_posters directory in both editable PPTX and viewable HTML formats.
Paper2Poster Setup
First, ensure you have Conda installed. The setup process involves cloning the repository and preparing the environment:
git clone https://github.com/caoxinran102-sys/Paper2Poster.git .
conda create -n poster_env python=3.10 -y
conda activate poster_env
pip install -r requirements.txt
Create a .env file with your API credentials:
OPENAI_API_KEY=your_key_here
OPENAI_BASE_URL=your_api_base_url
Paper2Poster Data Schema & Taxonomy
The skill organizes its outputs and configurations as follows:
| Path | Description |
|---|---|
/generated_posters/ |
Primary output directory for all generated materials. |
*.pptx |
The final academic poster file, ready for PowerPoint editing. |
*.html |
A responsive HTML version of the academic poster. |
.env |
Configuration file storing API keys and workspace settings. |
PosterAgent/ |
Core logic directory for the generation pipeline. |
Paper2Poster Advanced Features
- Intelligent retry logic with exponential backoff for handling rate limits (429) and server errors (529).
- Automatic model switching capabilities when encountering format errors (400) during generation.
- Support for institutional branding through custom logo path integration.
- Multi-engine support for OpenAI-compatible APIs, allowing for cost and performance optimization within Openclaw Skills.
SKILL.md
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