Imported from akillness/jeo-skills (.agent-skills/paperbanana/SKILL.md). Install upstream with npx skills add akillness/jeo-skills --skill paperbanana. Copyright stays with the author (MIT).

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
• 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):

1. Data → chart? Use plot / plot-batch (VLM-generated matplotlib, no image-gen key)
2. One diagram from text/PDF? Use generate
3. Have a figure already? Use evaluate (judge) or polish (guided edit), not a fresh run
4. Many diagrams/plots from a manifest? Use batch / plot-batch; sweep params with sweep
5. A whole paper's figure set? Use orchestrate
6. Generative figures are the wrong tool (exact vector art, trivial chart, brand-locked design)? Route out

Detailed packet examples and route-outs live in references/intake-and-route-outs.md.

Step 2: Install only the profile you need

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.

Full agent-by-agent detail lives in references/pipeline-and-agents.md.

Step 4: Run the lightest mode that fits

bash

Cheapest: statistical plot from data (no image-gen key)

bash scripts/run.sh plot --data results.csv --intent "Bar chart of accuracy across benchmarks"

Single methodology diagram from text

bash scripts/run.sh generate --input method.txt --caption "Overview of our framework"

Add optimization + auto-refine ONLY when a plain run underdelivers

bash scripts/run.sh generate --input method.txt --caption "Overview" --optimize --auto

The full command surface (generate, plot, batch, plot-batch, sweep, orchestrate, composite, evaluate, polish, venues, studio, setup, data) with flags lives in references/modes-and-cli.md.

Step 5: Fix before you regenerate

When a figure already exists, do not rerun the whole pipeline:

bash

Judge an existing figure against a human reference (VLM-as-Judge)

bash scripts/run.sh evaluate --generated out.png --reference human.png
--context method.txt --caption "Overview of our framework"

Guided edit against a venue style guide (image-edit capable provider)

bash scripts/run.sh polish --input figure.png --venue icml --iterations 2

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

1. Start with the smallest mode (plot < generate < batch/sweep/orchestrate) and escalate only on evidence.
2. Prefer plot for data — it skips the image-gen provider entirely and costs less.
3. Fix existing figures with polish/evaluate before paying for a full regeneration.
4. Treat --optimize, --auto, --iterations, and --num-candidates as cost dials — raise them only when a plain run falls short.
5. Pick the cheapest provider that meets quality (Gemini/Atlas VLMs are low cost); the pipeline is provider-agnostic.
6. Use venue style packs (neurips, icml, acl, ieee, or a custom pack) instead of hand-tuning aesthetics per run.
7. Judge with the VLM-as-Judge evaluate command before claiming a figure is publication-ready.
8. Route out honestly to matplotlib/TikZ/vector editors or a human designer when generative figures are the wrong tool.

References

• references/intake-and-route-outs.md
• references/pipeline-and-agents.md
• references/modes-and-cli.md
• references/providers-and-config.md
• references/evaluation-and-venues.md
• scripts/install.sh
• scripts/run.sh
• scripts/run-mcp.sh
• PaperBanana GitHub Repository
• PaperBanana paper (arXiv:2601.23265)