paperbanana

name	paperbanana
description	Route academic-figure work into the lightest workable PaperBanana mode instead of jumping straight to a full multi-agent generation run. Use when the user needs publication-quality methodology diagrams, statistical plots, figure evaluation, polishing an existing figure, batch/sweep generation, or a full-paper figure package from text or PDF. Even if the user does not say "paperbanana" — also triggers on: academic figure, methodology diagram, publication figure, generate diagram from paper, statistical plot from CSV, figure evaluation, polish figure, NeurIPS/ICML figure, arxiv illustration, plan-then-refine diagram pipeline.
allowed-tools	Bash Read Write Edit Glob Grep WebFetch
compatibility	Requires Python 3.10+ and works best in a virtual environment. Diagram generation needs an image-gen provider key (OpenAI/Azure/Gemini/Atlas/OpenRouter); statistical plots need only a VLM key. Studio, PDF, and MCP surfaces require extras.
license	MIT
metadata	{"tags":"paperbanana, academic-figures, diagram-generation, statistical-plots, multi-agent, vlm-as-judge, mcp, cli, publication, python","version":"1.0","source":"https://github.com/llmsresearch/paperbanana"}

paperbanana — routing-first academic illustration

Keyword: paperbanana · methodology diagram · statistical plot · figure evaluation · plan-then-refine

This is an unofficial community implementation of the PaperBanana paper (arXiv:2601.23265). Respect provider terms, API budgets, and each venue's figure/reuse policies.

PaperBanana turns text or a paper into publication-quality figures with a two-phase, multi-agent, plan-then-refine pipeline. It is most useful when you choose the smallest workable mode instead of always running the full generation loop:

• render data-driven charts with plot (matplotlib code, no image-gen key) before spending image credits
• fix an existing figure with polish or evaluate before regenerating from scratch
• generate a single diagram with generate before reaching for batch/sweep/orchestrate
• escalate iterations, candidates, and --optimize only on evidence, not by default
• route out to a vector editor, TikZ, or a human designer when generative figures are the wrong tool

When to use this skill

• The user wants a methodology/architecture diagram from a .txt, .md, or .pdf and you must pick the lightest generation path
• The user has tabular results (CSV/JSON) and wants a chart — cheaper than a diagram because no image-gen provider is needed
• The user already has a figure and wants it judged (evaluate) or improved (polish) rather than re-created
• The user needs many figures (a manifest batch, a parameter sweep, or a whole-paper figure package)
• The user wants PaperBanana exposed to an agent/IDE via the MCP server or Claude Code skills
• The user keeps drifting toward hand-drawing every figure, hard-coding one provider, or maxing iterations/candidates and needs an honest route-out

Instructions

Step 1: Capture one intake packet before choosing a mode

Collect the minimum packet:

• Artifact class: a methodology diagram, a statistical plot, an existing figure to fix/judge, many figures, or a full-paper package
• Source: raw method text, a paper (.pdf/.md), a data file (CSV/JSON), or an existing image