Rigorous - AI-Powered Scientific Manuscript Analysis

Cloud Version Available: A cloud version of the AI Peer Reviewer is now available at https://www.rigorous.company/. Simply upload your manuscript, provide context on target journal and review focus, and receive a comprehensive PDF report via email within 1-2 working days. The cloud version is currently free for testing purposes.

This repository contains tools for making scientific publishing more transparent, cheaper, faster, and ensuring rigorous peer and AI review.

Project Structure

• Agent1_Peer_Review: Advanced peer review system with specialized agents for comprehensive manuscript analysis, detailed feedback, and professional PDF report generation.
• Agent2_Outlet_Fit: (In Development) Tool for evaluating manuscript fit with target journals/conferences.

Current Status

Active Tools

• Agent1_Peer_Review: ✅ Ready for use
  • Comprehensive manuscript analysis with specialized agents
  • Detailed feedback on sections, scientific rigor, and writing quality
  • JSON output with actionable recommendations
  • PDF report generation (see below)

In Development

• Agent2_Outlet_Fit: 🚧 In Development
  • Core functionality being implemented
  • Integration with Agent1_Peer_Review in progress
  • Testing and validation ongoing

PDF Report Generation

This project includes a PDF report generator that creates a professional peer review report based on the outputs of the review agents.

How to Generate the PDF Report

1. Ensure you have the required dependencies installed:

   • reportlab
   • pillow
   • (Other dependencies as listed in requirements.txt)

2. Make sure the following files are present and up to date:

   • executive_summary.json (executive summary and overall scores)
   • quality_control_results.json (detailed section, rigor, and writing results)
   • logo.png (logo for the report header)

3. Run the PDF generator script:

   python Agent1_Peer_Review/pdf_generator.py

4. The generated PDF will be saved to:

   • Agent1_Peer_Review/results/review_report.pdf

Features

• Cover page with logo, manuscript title, and overall scores
• Executive summary and detailed analysis pages for all assessment items (S1–S10, R1–R7, W1–W7)
• Visually appealing tables for scores and suggestions
• Professional layout, color coding, and consistent formatting

For more details, see the comments in Agent1_Peer_Review/pdf_generator.py.

Requirements

• Python 3.7+
• OpenAI API key
• PDF manuscripts to analyze
• Dependencies listed in each tool's requirements.txt

License

This project is licensed under the MIT License.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.