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paperbanana-skill

by PlutoLei · PlutoLei/paperbanana-skill

Generate publication-quality academic diagrams — architecture figures, training curves, system schematics — from prompts, PaperBanana-style.

A Claude Code skill wrapping the PaperBanana workflow for academic figures. Instead of fighting TikZ or Inkscape, describe the figure; get publishable SVG/PDF output with the visual grammar papers expect (clean typography, consistent arrows, no rainbow colors).

Why use it

Key features

Live Demo

What it looks like in practice

paperbanana-skill.replay ▶ ready
0/0

Install

Author repository · Claude Skills documentation

Use Cases

Real-world ways to use paperbanana-skill

Draw your model architecture for a conference paper

👤 ML researchers, grad students mid-paper ⏱ ~30 min intermediate

When to use: Deadline in 5 days, you still don't have a clean architecture figure.

Flow
  1. Describe the model
    Use paperbanana-skill. Architecture figure: input → tokenizer → transformer stack (6 layers) → classifier head. Use the skill's default paper style.✓ Copied
    → SVG with clean boxes, arrows, consistent typography
  2. Tweak
    Label the transformer block internals — attention + FFN.✓ Copied
    → Updated figure with sub-block detail
  3. Export for LaTeX
    Export as PDF sized for a two-column paper.✓ Copied
    → PDF with correct aspect ratio

Outcome: A reviewer-worthy figure without a week of Inkscape.

Pitfalls
  • Figure over-decorated — Prefer restraint; one accent color, black arrows, lots of whitespace
Combine with: filesystem

Turn training logs into a publication-ready plot

👤 ML researchers with wandb/tensorboard exports ⏱ ~20 min beginner

When to use: You have CSVs of training metrics and need a figure for the paper.

Flow
  1. Feed CSV
    Use paperbanana-skill. Plot loss and accuracy curves from training.csv in two-panel layout.✓ Copied
    → Two-panel figure with clean axes
  2. Add baselines
    Overlay the baseline model's curves (baseline.csv) as dashed.✓ Copied
    → Overlay added with clear legend

Outcome: A plot that fits the paper's aesthetic.

Keep all figures in a paper visually consistent

👤 Authors with 6+ figures drifting in style ⏱ ~45 min intermediate

When to use: You look at the paper and realize each figure has a different font.

Flow
  1. Define the style
    Use paperbanana-skill. Create a shared style: 10pt sans-serif, #1f77b4 primary, black stroke 0.8pt.✓ Copied
    → Style file saved
  2. Apply to all figures
    Regenerate figures 1-6 using this style.✓ Copied
    → Consistent batch output

Outcome: Figures that look like they belong in the same paper.

Combine with: filesystem

Combinations

Pair with other MCPs for X10 leverage

paperbanana-skill + filesystem

Organize figures in the paper's /figures directory

Save all figures under paper/figures/ with source.svg + out.pdf per figure.✓ Copied
paperbanana-skill + mermaid-skill

Use Mermaid for casual diagrams, PaperBanana for publication quality

Sketch in Mermaid for discussion; produce with PaperBanana for camera-ready.✓ Copied

Tools

What this MCP exposes

ToolInputsWhen to callCost
architecture_diagram description System figures 0
plot_curves data file, metrics Training/eval metrics 0
apply_style style file, figures Cross-figure consistency 0

Cost & Limits

What this costs to run

API quota
None
Tokens per call
Moderate
Monetary
Free
Tip
Lock the style once; apply across figures to avoid re-deciding.

Security

Permissions, secrets, blast radius

Credential storage: None
Data egress: None from skill

Troubleshooting

Common errors and fixes

Exported PDF has wrong font in LaTeX

Embed fonts or use standard PDF font families; check paperbanana's export settings

SVG renders differently in browser vs LaTeX

Stick to PDF for LaTeX; SVG for web/slides

Alternatives

paperbanana-skill vs others

AlternativeWhen to use it insteadTradeoff
TikZ / PGFPlotsYou want full LaTeX integration and are comfortable with TikZSteep learning curve
mermaid-skillCasual docs, not publicationNot publication-quality aesthetic

More

Resources

📖 Read the official README on GitHub

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