What it does
Generates publication-quality academic diagrams and statistical plots from text descriptions through a two-phase multi-agent pipeline. Supports OpenAI (GPT-5.2 and GPT-Image-1.5), Azure OpenAI, Google Gemini, and Atlas Cloud as image generation backends. Offers batch diagram generation from YAML/JSON manifests, batch plot generation from data descriptions, optional PDF context loading, and a local Gradio web UI called Studio. Integrates with Claude Code via MCP with skills for /generate-diagram, /generate-plot, and /evaluate-diagram.
Who it's for
Academic researchers and scientists authoring papers or presentations who need to generate high-quality figures programmatically. Suited for AI researchers working in Claude Code or other LLM-integrated environments, particularly those who lack design expertise or prefer code-driven illustration.
Common use cases
- Generate methodology diagrams and architecture visualizations from text descriptions
- Create statistical plots from CSV or JSON data specifications
- Iteratively refine diagrams based on feedback via auto-refine mode
- Batch-generate multiple diagrams and plots from a single manifest file
- Include PDF methodology pages as context to improve diagram fidelity
Setup pitfalls
- Requires Python 3.10 or higher
- Requires an API key for image generation: OpenAI, Google Gemini (free tier available), Azure OpenAI, or Atlas Cloud
- Classified high-risk due to filesystem read/write access and outbound network calls—ensure working directories are sandboxed when processing untrusted input
- Keep API keys in environment variables, not configuration files