- 📄 index.json
- 📄 LICENSE
- 📄 never_add.csv
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Activate when the user needs to generate, refine, or evaluate academic figures, diagrams, or statistical plots. Uses PaperBanana (paperbanana MCP server) to transform text descriptions or data files into publication-quality illustrations. Supports methodology diagrams, statistical plots, and comparative evaluation. --- > **Orchestration Log**: When this skill is activated, append a log entry to `outputs/orchestration_log.md`: > ``` > ### Skill Activation: Figure Engine > **Timestamp:** [current date/time] > **Actor:** AI Agent (figure-engine) > **Input:** [brief description of the figure request] > **Output:** [brief description of what was produced — e.g., "Generated methodology diagram (fig03_methodology.png), 3 iterations"] > ``` # Figure Engine ## Core Principle Academic papers need professional figures. This skill eliminates manual design work by using PaperBanana to generate publication-quality diagrams and plots from text descriptions or data files. Claude should produce ACTUAL FIGURES, not describe what to draw. > **Based on:** Zhu, D., Meng, R., Song, Y., Wei, X., Li, S., Pfister, T., & Yoon, J. (2026). > *PaperBanana: Automating Academic Illustration for AI Scientists.* [arXiv:2601.23265](https://arxiv.org/abs/2601.23265). > The pipeline uses a 5-agent, 2-phase architecture: Retriever → Planner → Stylist (Phase 1: planning), > then Visualizer ↔ Critic iterative refinement (Phase 2: generation) with VLM-as-Judge evaluation. > MCP integration via [`llmsresearch/paperbanana`](https://github.com/llmsresearch/paperbanana). > Official research repo: [`dwzhu-pku/PaperBanana`](https://github.com/dwzhu-pku/PaperBanana). ## Prerequisites The **paperbanana MCP server** must be running. It requires: - `pip install paperbanana[mcp,google]` - A `GOOGLE_API_KEY` in a `.env` file in the project root (get a free key at https://aistudio.google.com/apikey) The MCP server is configured in `plugin.json` and starts automatically. It loads the Google API key from `.env` in the p
