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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.

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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

1. Multi-Agent Orchestration: Coordinates specialized agents (Retriever, Planner, Stylist, Visualizer, Critic) to handle complex illustration tasks.
2. Methodology Diagrams: Generates flowcharts and system architecture diagrams.
3. Statistical Plots: Produces high-quality plots for data visualization.
4. Iterative Refinement: Uses a critic agent to review and improve figures based on academic criteria.

Workflow

1. Input: Provide a description of the figure or data to be visualized.
2. Planning: The Planner agent breaks down the request into actionable steps.
3. Generation: The Visualizer and Stylist agents create the initial draft.
4. Critique & Refine: The Critic agent reviews the output, and the system iteratively improves it.
5. Output: A high-resolution image file ready for inclusion in a manuscript.

References

• Project Website (Placeholder based on typical project structure)

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