| name | paper-banana |
| description | Agentic framework for automating the generation of publication-ready academic illustrations and statistical plots. |
| license | CC-BY-SA-4.0 |
| metadata | {"author":"Peking University & Google Cloud AI Research","version":"1.0.0"} |
| compatibility | [{"system":"Python 3.9+"}] |
| allowed-tools | ["run_shell_command","read_file"] |
PaperBanana
PaperBanana is an advanced agentic framework designed to automate the creation of high-quality, publication-ready academic illustrations. It employs a multi-agent architecture to retrieve data, plan visualizations, style figures, and critique the output, ensuring adherence to strict academic standards.
When to Use This Skill
- You need to generate methodology diagrams from text descriptions.
- You want to create statistical plots (e.g., bar charts, line graphs, scatter plots) that meet academic publication standards.
- You need to refine existing figures for better clarity, aesthetics, or faithfulness to the data.
Core Capabilities
- Multi-Agent Orchestration: Coordinates specialized agents (Retriever, Planner, Stylist, Visualizer, Critic) to handle complex illustration tasks.
- Methodology Diagrams: Generates flowcharts and system architecture diagrams.
- Statistical Plots: Produces high-quality plots for data visualization.
- Iterative Refinement: Uses a critic agent to review and improve figures based on academic criteria.
Workflow
- Input: Provide a description of the figure or data to be visualized.
- Planning: The Planner agent breaks down the request into actionable steps.
- Generation: The Visualizer and Stylist agents create the initial draft.
- Critique & Refine: The Critic agent reviews the output, and the system iteratively improves it.
- Output: A high-resolution image file ready for inclusion in a manuscript.
References
