PaperBanana CC
A Claude Code Skill/Plugin for generating publication-quality academic diagrams and plots.
Built entirely upon PaperBanana — this project re-implements the full pipeline from the original research as a Claude Code-native skill, where Claude Code itself acts as the orchestrating VLM (Vision-Language Model) for every stage: paper analysis, reference retrieval, structured planning, venue-specific styling, and iterative vision-based critique.
Based On
This project is a Claude Code adaptation of the following works:
All prompts, reference datasets, style guides, and pipeline design are derived from the above. PaperBanana CC restructures them into a Claude Code Skill so that Claude Code subscribers can generate academic figures through natural conversation — no separate VLM server or complex setup required.
Why Claude Code?
Claude Code already includes a state-of-the-art VLM with tool use, vision, and long-context capabilities. Instead of running a separate inference server, PaperBanana CC leverages Claude Code as both the reasoning engine and the orchestrator:
- Zero infrastructure — no GPU server, no model downloads, no Docker
- Full context across phases — the Critic can compare the generated image against the original methodology, not just the final prompt
- Interactive refinement — the user confirms direction at each stage via natural conversation
- Plugin distribution — install once, use in any project
Image Generation Options
| Method | Requirement | Best For |
|---|
| OpenAI API (gpt-image-1) | OPENAI_API_KEY | Highest quality diagrams |
| Gemini API (Imagen 3) | GOOGLE_API_KEY | Fast iteration, flexible ratios |
| Manual generation | Any image AI subscription | No API key needed — use ChatGPT, Gemini, or any web-based image generator |
The manual generation mode is designed for users who have a subscription to Google AI Pro, ChatGPT Plus, or similar services but don't want to set up API keys. PaperBanana CC generates the optimized prompt, you paste it into your preferred web UI, and provide the resulting image back for critique.
Pipeline
[Phase 0] Input Enrichment
Methodology text + caption → 7-axis structuring + 6-spec caption enhancement
← User confirms direction
[Phase 1] Reference Retrieval
538 reference examples → 2-axis semantic matching → top-10 with images
← User selects references
[Phase 2] Plan → Style → Generate
Venue (NeurIPS/ICML/ACL/IEEE) + generation method selection
→ 7-item structured plan → venue-specific styling → image generation
Prompt always displayed and saved
[Critic Loop] Iterative Refinement (default 3 rounds)
Claude Code vision critique → revision or completion
← User can provide additional feedback at any point
All phases except image generation are performed directly by Claude Code. Prompts and style guides from the original PaperBanana research are used to instruct Claude Code at each stage.
Installation
Requirements
- Claude Code subscription (Claude Pro / Max / Team)
- uv (for
uvx — Python dependencies are installed automatically)
- Optional: OpenAI API key and/or Google API key (not required for manual generation)
Install via Plugin Marketplace
# 0. Install uv (if not installed) — required for image generation
curl -LsSf https://astral.sh/uv/install.sh | sh
# 1. Add marketplace (one-time)
/plugin marketplace add eunsanMountain/paperbanana-cc
# 2. Install plugin
/plugin install paperbanana-cc@paperbanana-marketplace
# 3. (Optional) Set API keys — not required for manual generation mode
echo "OPENAI_API_KEY=sk-..." >> .env
echo "GOOGLE_API_KEY=AI..." >> .env
That's it. No git clone, no uv sync — the plugin loads skills directly, and Python dependencies are fetched on-demand via uvx.
Reference data (~300MB) is downloaded automatically on first run.
Alternative: Local Development
git clone https://github.com/eunsanMountain/paperbanana-cc.git
cd paperbanana-cc && uv sync
claude --plugin-dir ./
Usage
Since Claude Code runs inside your project, PaperBanana CC can read your codebase directly — no copy-pasting needed. Just point it at the relevant source files:
> /paperbanana
Analyze src/model/transformer.py and generate an architecture diagram
showing the data flow from input embedding through the attention layers.
Caption: "Overview of the proposed multi-modal transformer architecture."