figure-engine

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. 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. Official research repo: dwzhu-pku/PaperBanana.

Prerequisites

PaperBanana must be installed: pip install paperbanana[mcp,google]

A GOOGLE_API_KEY must be available via one of:

• Environment variable GOOGLE_API_KEY
• .env file in project root
• ~/.paperbanana.env

Get a free key at https://aistudio.google.com/apikey

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Method Priority

Priority Order:

1. PRIMARY — Direct Python API (via Bash → python3) — ALWAYS use this
2. FALLBACK — matplotlib/seaborn — If PaperBanana is not installed at all

Note: The PaperBanana MCP server is NOT used. The MCP stdio transport is unreliable (timeouts, hangs, silent failures). Always use the direct Python API.

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Method 1: Direct Python API (PRIMARY — Always Use This)

The plugin ships a helper script scripts/paperbanana_direct.py that calls the PaperBanana Python API directly via asyncio.run(), completely bypassing the MCP stdio transport. It outputs JSON to stdout.