OpenSkillIndex← back to search
claude-code

paperbanana

Generate publication-quality academic diagrams from paper methodology text

install
source · Clone the upstream repo
git clone https://github.com/dwzhu-pku/PaperBanana
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/dwzhu-pku/PaperBanana "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skill" ~/.claude/skills/dwzhu-pku-paperbanana-paperbanana && rm -rf "$T"
manifest: skill/SKILL.md
source content

PaperBanana

Generate publication-quality academic diagrams and pipeline figures from a paper's methodology section and figure caption. PaperBanana orchestrates a multi-agent pipeline (Retriever, Planner, Stylist, Visualizer, Critic) to produce camera-ready figures suitable for venues like NeurIPS, ICML, and ACL.

Environment Setup

cd <repo-root>
uv pip install -r requirements.txt

Set your API key via environment variable or in 

configs/model_config.yaml
.

Option 1 (Recommended): OpenRouter API key — one key for both text reasoning and image generation:

export OPENROUTER_API_KEY="sk-or-v1-..."

Option 2: Google API key — direct access to Gemini API:

export GOOGLE_API_KEY="your-key-here"

If both keys are configured, OpenRouter is used by default.

Usage

python skill/run.py \
  --content "METHOD_TEXT" \
  --caption "FIGURE_CAPTION" \
  --task diagram \
  --output output.png

Parameters

ParameterRequiredDefaultDescription
--content
Yes*Method section text to visualize
--content-file
Yes*Path to a file containing the method text (alternative to 
--content
)
--caption
YesFigure caption or visual intent
--task
No
diagram
Task type: 
diagram
--output
No
output.png
Output image file path
--aspect-ratio
No
21:9
Aspect ratio: 
21:9
, 
16:9
, or 
3:2
--max-critic-rounds
No
3
Maximum critic refinement iterations
--num-candidates
No
10
Number of parallel candidates to generate
--retrieval-setting
No
auto
Retrieval mode: 
auto
, 
manual
, 
random
, or 
none
--main-model-name
No
gemini-3.1-pro-preview
Main model for VLM agents. Provider auto-detected from configured API key
--image-gen-model-name
No
gemini-3.1-flash-image-preview
Model for image generation. Also supports 
gemini-3-pro-image-preview
--exp-mode
No
demo_full
Pipeline: 
demo_full
(with Stylist) or 
demo_planner_critic
(without Stylist)

*One of 

--content
or 
--content-file
is required.

When 

--num-candidates
> 1, output files are named 
<stem>_0.png
, 
<stem>_1.png
, etc.

Output

The absolute path of each saved image is printed to stdout, one per line.

Examples

Diagram

python skill/run.py \
  --content "We propose a transformer-based encoder-decoder architecture. The encoder consists of 12 self-attention layers with residual connections. The decoder uses cross-attention to attend to encoder outputs and generates the target sequence autoregressively." \
  --caption "Figure 1: Overview of the proposed transformer architecture" \
  --task diagram \
  --output architecture.png

Important Notes

  • Runtime: A single candidate typically takes 3-10 minutes depending on model and network conditions. With the default 10 candidates running in parallel, expect ~10-30 minutes total. Plan accordingly.
  • API calls: Each candidate involves multiple LLM calls (Retriever + Planner + Stylist + Visualizer + up to 3 Critic rounds). Candidates run in parallel for efficiency.
  • Image generation: The Visualizer agent calls an image generation model (Gemini Image) to render diagrams.

About

PaperBanana is based on the PaperVizAgent framework, a reference-driven multi-agent system for automated academic illustration. It was developed as part of the research paper:

PaperBanana: Automating Academic Illustration for AI Scientists Dawei Zhu, Rui Meng, Yale Song, Xiyu Wei, Sujian Li, Tomas Pfister, Jinsung Yoon arXiv:2601.23265

The framework introduces a collaborative team of five specialized agents — Retriever, Planner, Stylist, Visualizer, and Critic — to transform raw scientific content into publication-quality diagrams. Evaluation is conducted on the PaperBananaBench benchmark.