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.
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
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Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
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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.
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
Budget/keys: which provider key is available (VLM-only vs image-gen), and any USD cap
Quality need: quick draft vs auto-refine to critic satisfaction vs multi-candidate fan-out
Use this escalation ladder (smallest first):
Data → chart? Use plot / plot-batch (VLM-generated matplotlib, no image-gen key)
One diagram from text/PDF? Use generate
Have a figure already? Use evaluate (judge) or polish (guided edit), not a fresh run
Many diagrams/plots from a manifest? Use batch / plot-batch; sweep params with sweep
A whole paper's figure set? Use orchestrate
Generative figures are the wrong tool (exact vector art, trivial chart, brand-locked design)? Route out
Use a virtual environment unless the user explicitly wants a system install.
bash
bash scripts/install.sh --profile plot # VLM-only: statistical plots
bash scripts/install.sh --profile diagram # image-gen provider for diagrams
bash scripts/install.sh --profile pdf # + PyMuPDF for PDF inputs
bash scripts/install.sh --profile studio # + Gradio local web UI
bash scripts/install.sh --profile mcp # MCP server surface
bash scripts/install.sh --profile all # only when the full surface is truly needed
Then set exactly one provider key (OPENAI_API_KEY, GOOGLE_API_KEY, ATLASCLOUD_API_KEY, …).
Provider selection and config live in references/providers-and-config.md.
Step 3: Understand the pipeline before turning knobs
Every diagram run is the same plan-then-refine loop, so tune stages instead of guessing flags:
Phase 0 — Input Optimization (--optimize, optional): Context Enricher structures raw method text; Caption Sharpener tightens the caption. Two parallel VLM calls.
Phase 1 — Linear Planning: Retriever picks reference examples → Planner writes a target description via in-context learning → Stylist applies venue aesthetics.
Phase 2 — Iterative Refinement: Visualizer renders an image → Critic revises the description; repeat for --iterations (default 3) or until satisfied with --auto.
Evaluation dimensions (Faithfulness, Readability, Conciseness, Aesthetics) and venue style packs
live in references/evaluation-and-venues.md.
Step 6: Escalate to batch, sweep, or orchestrate only when scale is the real problem
batch / plot-batch — many items from one manifest; optional composite stitches panels into one labeled figure
sweep — the same figure across a parameter grid, ranked by a quality proxy score
orchestrate — parse a full paper, plan multiple method figures + data plots, emit a figure_package.json + figures.tex bundle
For one or two figures, single-shot generate/plot stays simpler and cheaper.
Step 7: Expose it to agents only when a script is not the point
Use the MCP server (11 tools: generate_diagram, generate_plot, continue_run, evaluate_diagram,
orchestrate_figures, batch_diagrams, batch_plots, download_references, …) or the bundled Claude
Code skills (/generate-diagram, /generate-plot, /evaluate-diagram) when the goal is agent/IDE
access rather than a bespoke run.
bash
bash scripts/run-mcp.sh # stdio MCP server via uvx --from paperbanana[mcp]
Examples
Example 1: The user has a CSV and wants a chart
Use plot (or plot-batch) — it renders matplotlib from a VLM and needs no image-gen key, so it is the cheapest path.
Example 2: The user has one method paragraph and wants a diagram
Use generate with a clear --caption; add --optimize/--auto only if the first pass underdelivers.
Example 3: The user already has a figure that is "almost right"
Use polish (guided edit) or evaluate (judge), not a fresh generate run.
Example 4: The user wants every figure in a paper regenerated
Use orchestrate on the paper source; inspect orchestration_plan.json with --dry-run before spending credits.
Example 5: The user wants an agent to make figures on demand
Expose the MCP server or the Claude Code skills instead of hand-writing a one-off script.
Example 6: The figure is a trivial two-bar chart or exact vector art
Route out to plain matplotlib, TikZ, or a vector editor — a multi-agent generative run is overkill or the wrong tool.
Best practices
Start with the smallest mode (plot < generate < batch/sweep/orchestrate) and escalate only on evidence.
Prefer plot for data — it skips the image-gen provider entirely and costs less.
Fix existing figures with polish/evaluate before paying for a full regeneration.
Treat --optimize, --auto, --iterations, and --num-candidates as cost dials — raise them only when a plain run falls short.
Pick the cheapest provider that meets quality (Gemini/Atlas VLMs are low cost); the pipeline is provider-agnostic.
Use venue style packs (neurips, icml, acl, ieee, or a custom pack) instead of hand-tuning aesthetics per run.
Judge with the VLM-as-Judge evaluate command before claiming a figure is publication-ready.
Route out honestly to matplotlib/TikZ/vector editors or a human designer when generative figures are the wrong tool.
