Automated summary
Generates academic figures (framework diagrams and statistical charts) from paper method text using the PaperBanana multi-agent framework, and creates portable draw.io diagrams with embedded logo watermarks.
Strengths
- Automates figure generation from method text
- Supports both framework diagrams and statistical charts
- Embeds branding watermark automatically
- Produces portable draw.io files with embedded images
Limitations
- Requires setup of PaperBanana repository and model configuration
- Watermark is mandatory and cannot be removed
- Quality depends on underlying model and critic iterations
When preparing academic papers that need consistent, automatically generated figures from method descriptions.
When you need fine-grained manual control over every element of the figure layout.
Security analysis
CautionThe skill uses Bash to clone an external GitHub repository and install its requirements, which is a moderate supply-chain risk. It runs Python scripts that generate figures via network API calls, but does not exfiltrate data or perform destructive actions. No obfuscated or harmful commands detected.
- •Clones external repository (PaperBanana) and installs dependencies, introducing supply-chain risk.
- •Executes Python scripts that call external AI models, which could incur costs or unintended usage.
Examples
Generate a framework diagram from the following method text: 'We propose a novel architecture consisting of an encoder, a feature extractor, and a classifier. The encoder takes raw input and transforms it into latent features. These features are then passed to the extractor which refines them, and finally to the classifier for prediction.' with caption 'Figure 1: Overall architecture' and save as /path/to/output.pngGenerate a statistical chart showing performance comparison between our method (Ours), Baseline A, and Baseline B on accuracy metric. Data: Ours=92.5, A=88.3, B=85.1. Caption: 'Figure 2: Accuracy comparison'. Save as /path/to/chart.pngCreate a draw.io diagram for a pipeline with three components: Data Input, Processor, and Output. Use the logo from the imgs folder as a watermark. Save as pipeline.drawioname: paper-banana description: Academic Figure Generation - Generate framework diagrams and statistical charts from method text using PaperBanana multi-agent framework, and create draw.io diagrams with embedded logo images allowed-tools: Bash, Read, Write user-invocable: true
Paper Banana - Academic Figure Generation
MANDATORY WATERMARK RULE: Every image generated by this skill — whether raster (PNG/JPG) via
generate_figure.pyor draw.io XML viagenerate_drawio.py— MUST embed the Paper Banana logo (paper-banana/imgs/logo.png) in one corner of the final output as a watermark. This is a non-negotiable branding requirement. When usinggenerate_figure.py, composite the logo onto the output image before saving. When usinggenerate_drawio.py, include an additional logo node anchored to a corner of the canvas.
Automatically generate academic figures from paper method section text using the PaperBanana multi-agent framework (Planner → Visualizer → Critic loop).
Prerequisites
# 1. Clone PaperBanana
git clone https://github.com/paperbanana/PaperBanana.git ~/PaperBanana
# 2. Install dependencies
cd ~/PaperBanana
pip install -r requirements.txt
# 3. Configure model (edit configs/model_config.yaml)
# Set OpenAI-compatible API base URL, API key, and model name
Core Commands
SCRIPT=~/.claude/skills/yjyddq/paper-banana/scripts/generate_figure.py
# Basic usage
python3 $SCRIPT \
--content "Method text (Markdown format)" \
--caption "Figure 1: Framework diagram title" \
--output ./figure.png
# Read content from file
python3 $SCRIPT \
--content @method_section.md \
--caption "Figure 1: Pipeline overview" \
--output ./fig1.png
# High-quality mode (full pipeline + more iterations)
python3 $SCRIPT \
--content @method.md \
--caption "Figure 2: Architecture" \
--output ./fig2.png \
--exp-mode demo_full \
--critic-rounds 5
# Generate statistical charts
python3 $SCRIPT \
--content "Experimental results data..." \
--caption "Figure 3: Performance comparison" \
--output ./fig3.png \
--task plot
CLI Arguments
| Argument | Default | Description |
|----------|---------|-------------|
| --content | (required) | Method text, supports @filepath to read from file |
| --caption | (required) | Figure title / visual intent description |
| --output | (required) | Output image path (.png / .jpg) |
| --task | diagram | Task type: diagram (framework diagram) or plot (statistical chart) |
| --aspect-ratio | 16:9 | Aspect ratio |
| --exp-mode | demo_planner_critic | Pipeline mode |
| --retrieval-setting | none | Reference retrieval strategy |
| --critic-rounds | 3 | Maximum Critic iteration rounds |
| --image-model-name | (config file) | Override image generation model |
| --paperbanana-dir | ~/PaperBanana | PaperBanana project path |
Pipeline Mode Comparison
| Mode | Workflow | Use Case |
|------|----------|----------|
| demo_planner_critic | Planner → Visualizer → Critic × N | Fast generation, recommended default |
| demo_full | Retriever → Planner → Stylist → Visualizer → Critic × N | More polished, includes style optimization |
Output Format
The script outputs JSON to stdout:
{
"status": "success",
"output": "/absolute/path/to/figure.png",
"format": "PNG",
"size": "1920x1080",
"exp_mode": "demo_planner_critic",
"task": "diagram"
}
On failure:
{
"status": "error",
"message": "Error description"
}
Draw.io Diagrams with Embedded Logos
Generate draw.io (diagrams.net) XML files with logo images from imgs/ embedded as base64 data URIs. The resulting .drawio files are fully portable — no external image references needed.
Logo Directory
Place logo images (PNG/JPG/GIF/SVG/WebP) in imgs/ under the skill directory:
paper-banana/imgs/
├── logo.png
├── model_icon.png
└── database.svg
Draw.io Commands
DRAWIO_SCRIPT=~/.claude/skills/yjyddq/paper-banana/scripts/generate_drawio.py
# List available logos
python3 $DRAWIO_SCRIPT --list-logos
# Generate diagram from JSON config file
python3 $DRAWIO_SCRIPT \
--config @diagram_config.json \
--output ./architecture.drawio
# Inline JSON config
python3 $DRAWIO_SCRIPT \
--config '{"nodes":[{"id":"a","label":"Planner","logo":"logo.png","x":100,"y":100},{"id":"b","label":"Visualizer","x":300,"y":100}],"edges":[{"source":"a","target":"b","label":"plan"}]}' \
--output ./pipeline.drawio
# Custom logo directory
python3 $DRAWIO_SCRIPT \
--config @config.json \
--output ./diagram.drawio \
--logo-dir /path/to/custom/logos
Config JSON Format
{
"title": "System Architecture",
"nodes": [
{"id": "n1", "label": "Planner Agent", "logo": "logo.png", "x": 100, "y": 100, "width": 120, "height": 80, "logo_size": 40},
{"id": "n2", "label": "Visualizer", "x": 300, "y": 100},
{"id": "n3", "label": "Critic", "logo": "logo.png", "x": 500, "y": 100}
],
"edges": [
{"source": "n1", "target": "n2", "label": "plan"},
{"source": "n2", "target": "n3", "label": "image"}
]
}
Node fields:
| Field | Required | Default | Description |
|-------|----------|---------|-------------|
| id | yes | — | Unique node identifier |
| label | no | same as id | Display label |
| logo | no | — | Logo filename from imgs/ directory (embedded as base64) |
| x, y | no | auto-layout | Position in pixels |
| width | no | 120 | Node width |
| height | no | 80 | Node height |
| logo_size | no | 40 | Logo image size in pixels |
| style | no | — | Custom draw.io style string (overrides default) |
Edge fields:
| Field | Required | Default | Description |
|-------|----------|---------|-------------|
| source / from | yes | — | Source node id |
| target / to | yes | — | Target node id |
| label | no | "" | Edge label |
| style | no | — | Custom draw.io edge style |
Draw.io CLI Arguments
| Argument | Default | Description |
|----------|---------|-------------|
| --config | (required) | Diagram config: JSON string or @filepath |
| --output | (required) | Output .drawio file path |
| --logo-dir | imgs/ (skill dir) | Directory containing logo images |
| --list-logos | — | List available logos and exit |
Draw.io Output Format
{
"status": "success",
"output": "/absolute/path/to/diagram.drawio",
"format": "drawio",
"nodes": 3,
"edges": 2,
"logos_embedded": ["logo.png"]
}
Comparison with drawio
| Dimension | paper-banana | drawio | |-----------|-------------|--------| | Output type | Raster image (PNG/JPG), AI-generated | Vector graphic (XML), manual/rule-based | | Use case | Paper method diagrams, framework diagrams, schematic illustrations | Flowcharts, ER diagrams, architecture diagrams | | Editability | Non-editable (raster) | Fully editable (XML) | | Visual quality | High (AI-stylized) | Medium (engineering diagram style) | | Generation speed | Slow (multiple AI call rounds) | Fast (direct XML generation) |
From the file to a first result
The download contains exactly the content displayed on this page. This copy may differ from the latest version in the source repository.
View the source and supporting filesThis download contains SKILL.md only. Any scripts, references, templates and dependencies are not included. Get the required files from the source, preserving their directory structure, before continuing.
1. Download the file
Download SKILL.mdFrom the root of a test project, save the file as .skill-downloads/academic-figure-generation-ms2s7bn6/SKILL.md, or use the command below. This folder is for inspecting the skill before installation.
Command for macOS/Linux or WSL: requires a POSIX shell and curl. On Windows without WSL, use the download button and create the folder manually.
mkdir -p '.skill-downloads/academic-figure-generation-ms2s7bn6' && curl --fail --show-error --location 'https://skills-guides.com/api/skills/academic-figure-generation-ms2s7bn6/download' --output '.skill-downloads/academic-figure-generation-ms2s7bn6/SKILL.md.part' && mv '.skill-downloads/academic-figure-generation-ms2s7bn6/SKILL.md.part' '.skill-downloads/academic-figure-generation-ms2s7bn6/SKILL.md'2. Check prerequisites
Read SKILL.md before using it. If it references scripts or external services, inspect them and prepare the required access in your test environment.
Tools declared by the author (check availability in your agent): Bash, Read, Write
Original excerpt: Prerequisites
# 1. Clone PaperBanana
git clone https://github.com/paperbanana/PaperBanana.git ~/PaperBanana
# 2. Install dependencies
cd ~/PaperBanana
pip install -r requirements.txt
# 3. Configure model (edit configs/model_config.yaml)
# Set OpenAI-compatible API base URL, API key, and model name
The platforms on this page are catalog indications. The paths below follow tool documentation; they do not prove this skill has been run in those tools. No minimum tool version is confirmed for this listing.
3. Load the skill
After inspection, copy the complete folder into your project using one of the commands below, then open a new tool session. The command stops if a folder with the same name already exists: compare the files before replacing it.
Claude Code
test -f '.skill-downloads/academic-figure-generation-ms2s7bn6/SKILL.md' && test -s '.skill-downloads/academic-figure-generation-ms2s7bn6/SKILL.md' && mkdir -p '.claude/skills' && test ! -e '.claude/skills/paper-banana' && cp -R '.skill-downloads/academic-figure-generation-ms2s7bn6' '.claude/skills/paper-banana'OpenSkills
OpenSkills must already be installed. The local install/read procedure is checked with OpenSkills 1.5.0, which requires Node.js ≥ 20.6.0. Catalog slugs are not resolved automatically.
Official OpenSkills installationopenskills install './.skill-downloads/academic-figure-generation-ms2s7bn6'openskills read 'academic-figure-generation-ms2s7bn6'Reading should display the installed file content and its base directory. It does not run the skill or install its dependencies.
For an agent that reads AGENTS.md, then sync the index. This command changes AGENTS.md: review the diff before using it.
openskills syncManual method or another tool: attach SKILL.md to a conversation, or open it in an agent that can read files. Include any required supporting resources. No universal folder is recognized by every tool.
4. Try a first task
Use a short, synthetic input: an API endpoint, a small document or an interface brief. In a new conversation, attach the file or give the agent its path, then adapt this request:
Read .skill-downloads/academic-figure-generation-ms2s7bn6/SKILL.md and apply its instructions to this example: [describe a test input]. Identify any missing files, dependencies or access. Produce the result requested by the skill without inventing data or running unreviewed scripts.Expected result: output matching the skill instructions, or a precise list of missing prerequisites. A read confirmation alone does not validate a first use. If an external resource is unavailable, that path remains untested.
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