alterlab-scientific-schematics

Generate publication-ready scientific schematics from natural-language prompts with iterative refinement.

58|9|Updated Mar 16, 2026

Search Tags:#iteration #diagram-generation #publication-quality #scientific-visualization #ai-assistance #okabe-ito

Ready to run

npx skills add https://github.com/AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-scientific-schematics

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: alterlab-scientific-schematics
Source: https://github.com/AlterLab-IEU/AlterLab-Academic-Skills/tree/main/skills/visualization/alterlab-scientific-schematics
Command: npx skills add https://github.com/AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-scientific-schematics

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, and includes references (resource) components.

What problem does it solve?

Generating clear, publication-ready scientific diagrams from complex ideas is time-consuming and error-prone; this skill uses AI to translate natural-language descriptions into professional schematics with automated quality checks.

Core Features & Use Cases

AI-driven generation of diagrams (neural networks, pathways, circuits, system architectures) with iterative refinement.
Built-in quality review using Gemini 3.1 Pro Preview to ensure clarity, accuracy, and publication readiness.
Output includes versioned images and a detailed review log, suitable for inclusion in publications or presentations.

Quick Start

Describe your diagram in plain language to generate a publication-quality schematic with automatic refinement.

Frequently Asked Questions about alterlab-scientific-schematics

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I generate publication-quality scientific diagrams from text descriptions?▼

To generate publication-quality scientific schematics, describe your neural network, biological pathway, or system architecture in plain natural language. The AI-driven iterative refinement process translates your text into professional diagrams with automatic quality reviews.

What types of scientific schematics can I create using AI-assisted generation?▼

AI-assisted generation supports creating neural networks, biological pathways, system architectures, and circuits. It applies document-type thresholds for journals, posters, and presentations to ensure the output meets specific publication standards.

Can I use natural language prompts to create scientific visualizations for journals and posters?▼

Yes, you can use natural language prompts to create scientific visualizations tailored for journals, posters, or presentations. The system applies specific document-type thresholds to ensure the generated diagrams meet the required publication standards.

How does iterative refinement improve publication-ready schematics?▼

Iterative refinement improves publication-ready schematics by applying automatic quality reviews after each generation cycle. Using a two-iteration cap, the system evaluates clarity and accuracy, logging the detailed reviews via a structured JSON artifact.

Are there limitations to the AI-driven diagram generation process?▼

The main limitation of the AI-driven diagram generation process is a strict two-iteration cap for refinement. Additionally, outputs are automatically reviewed against specific document-type thresholds, which may restrict highly unconventional schematic formats.

Do I need any specific Python dependencies to extract images from the generated schematics?▼

You need the requests Python dependency to support the schematic generation workflow. The system handles base64 extraction of images internally, outputting versioned images alongside a detailed review log in a structured JSON artifact.