By 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 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.
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
- Project Website (Placeholder based on typical project structure)