Alterlab scientific schematics

Scientific Schematics and Diagrams

Overview

Scientific schematics and diagrams transform complex concepts into clear visual representations for publication. This skill uses Nano Banana 2 AI for diagram generation with Gemini 3.1 Pro Preview quality review.

How it works:

• Describe your diagram in natural language
• Nano Banana 2 generates publication-quality images automatically
• Gemini 3.1 Pro Preview reviews quality against document-type thresholds
• Smart iteration: Only regenerates if quality is below threshold
• Publication-ready output in minutes
• No coding, templates, or manual drawing required

Quality Thresholds by Document Type:

Document Type	Threshold	Description
journal	8.5/10	Nature, Science, peer-reviewed journals
conference	8.0/10	Conference papers
thesis	8.0/10	Dissertations, theses
grant	8.0/10	Grant proposals
preprint	7.5/10	arXiv, bioRxiv, etc.
report	7.5/10	Technical reports
poster	7.0/10	Academic posters
presentation	6.5/10	Slides, talks
default	7.5/10	General purpose

Simply describe what you want, and Nano Banana 2 creates it. All diagrams are stored in the figures/ subfolder and referenced in papers/posters.

Quick Start: Generate Any Diagram

Create any scientific diagram by simply describing it. Nano Banana 2 handles everything automatically with smart iteration:

# Generate for journal paper (highest quality threshold: 8.5/10)
python scripts/generate_schematic.py "CONSORT participant flow diagram with 500 screened, 150 excluded, 350 randomized" -o figures/consort.png --doc-type journal

# Generate for presentation (lower threshold: 6.5/10 - faster)
python scripts/generate_schematic.py "Transformer encoder-decoder architecture showing multi-head attention" -o figures/transformer.png --doc-type presentation

# Generate for poster (moderate threshold: 7.0/10)
python scripts/generate_schematic.py "MAPK signaling pathway from EGFR to gene transcription" -o figures/mapk_pathway.png --doc-type poster

# Custom max iterations (max 2)
python scripts/generate_schematic.py "Complex circuit diagram with op-amp, resistors, and capacitors" -o figures/circuit.png --iterations 2 --doc-type journal

What happens behind the scenes:

1. Generation 1: Nano Banana 2 creates initial image following scientific diagram best practices
2. Review 1: Gemini 3.1 Pro Preview evaluates quality against document-type threshold
3. Decision: If quality >= threshold → DONE (no more iterations needed!)
4. If below threshold: Improved prompt based on critique, regenerate
5. Repeat: Until quality meets threshold OR max iterations reached

Smart Iteration Benefits:

• ✅ Saves API calls if first generation is good enough
• ✅ Higher quality standards for journal papers
• ✅ Faster turnaround for presentations/posters
• ✅ Appropriate quality for each use case

Output: Versioned images plus a detailed review log with quality scores, critiques, and early-stop information.

Configuration

Set your OpenRouter API key:

export OPENROUTER_API_KEY='your_api_key_here'

Get an API key at: https://openrouter.ai/keys

Data & privacy

This skill's generation scripts (scripts/generate_schematic_ai.py) send your diagram description / prompt to a third-party API (OpenRouter) over the network for image generation and quality review. Your text prompts — and any details you include in them — leave your machine and are processed by an external provider. Do not include confidential, clinical, patient-identifying, or unpublished proprietary content in figure descriptions. Describe figures generically and add sensitive labels locally afterward if needed.

AI Generation Best Practices

Good prompts are specific: name the diagram type, components, flow/direction, labels, and style. Vague prompts ("make a flowchart", "neural network") underperform. Scientific quality guidelines (clean background, ≥10pt labels, sans-serif, Okabe-Ito palette, proper spacing) are applied automatically. Good/bad prompt examples and the full guideline list are in references/ai_generation_guide.md.

When to Use This Skill

This skill should be used when:

• Creating neural network architecture diagrams (Transformers, CNNs, RNNs, etc.)
• Illustrating system architectures and data flow diagrams
• Drawing methodology flowcharts for study design (CONSORT, PRISMA)
• Visualizing algorithm workflows and processing pipelines
• Creating circuit diagrams and electrical schematics
• Depicting biological pathways and molecular interactions
• Generating network topologies and hierarchical structures
• Illustrating conceptual frameworks and theoretical models
• Designing block diagrams for technical papers

How to Use This Skill

Simply describe your diagram in natural language. Nano Banana 2 generates it automatically:

python scripts/generate_schematic.py "your diagram description" -o output.png

That's it! The AI handles:

• ✓ Layout and composition
• ✓ Labels and annotations
• ✓ Colors and styling
• ✓ Quality review and refinement
• ✓ Publication-ready output

Works for all diagram types:

• Flowcharts (CONSORT, PRISMA, etc.)
• Neural network architectures
• Biological pathways
• Circuit diagrams
• System architectures
• Block diagrams
• Any scientific visualization

No coding, no templates, no manual drawing required.

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AI Generation Mode (Nano Banana 2 + Gemini 3.1 Pro Preview Review)

The AI generation system uses smart iteration: Nano Banana 2 generates an image, Gemini 3.1 Pro Preview scores it (0-10 across scientific accuracy, clarity, label quality, layout, and professional appearance), and the system stops early once the score meets the document-type threshold — otherwise it improves the prompt from the critique and regenerates (max 2 iterations). Every run writes a JSON review log with per-iteration scores, critiques, and early-stop info.

The deep dive — iteration flowchart, review rubric, example review output, decision table, JSON log schema, the ScientificSchematicGenerator Python API, all command-line options, and prompt engineering tips — is in references/ai_generation_guide.md.

Copy-and-adapt worked invocations for CONSORT flowcharts, transformer architectures, biological pathways, and system block diagrams are in references/generation_examples.md.

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Command-Line Usage

The main entry point for generating scientific schematics:

# Basic usage
python scripts/generate_schematic.py "diagram description" -o output.png

# Custom iterations (max 2)
python scripts/generate_schematic.py "complex diagram" -o diagram.png --iterations 2

# Verbose mode
python scripts/generate_schematic.py "diagram" -o out.png -v

Note: The Nano Banana 2 AI generation system includes automatic quality review in its iterative refinement process. Each iteration is evaluated for scientific accuracy, clarity, and accessibility.

Best Practices Summary

Design for clarity over complexity, consistent styling, colorblind accessibility (Okabe-Ito, redundant encoding), ≥7-8 pt sans-serif type, and vector output (PDF/SVG; 300+ DPI for raster). Integrate with LaTeX via \includegraphics{}, caption thoroughly, reference in text, and keep prompts + outputs under version control. The full design/technical/integration checklist and the pre-submission verification checklist are in references/submission_checklist.md.

Troubleshooting Common Issues

For fixes to AI generation problems (overlaps, poor connections), image-quality issues, quality-check failures, and accessibility problems (grayscale contrast, small text), see references/troubleshooting.md. Most issues resolve by making the prompt more specific or raising --iterations 2.

Resources and References

Detailed References

Load these files for comprehensive information on specific topics:

• references/ai_generation_guide.md - Smart-iteration workflow, review rubric, Python API, CLI options, prompt engineering
• references/generation_examples.md - Worked CLI invocations (CONSORT, transformer, pathway, system diagrams)
• references/troubleshooting.md - Fixes for generation, quality-check, and accessibility issues
• references/submission_checklist.md - Best-practices summary and pre-submission verification checklist
• references/diagram_types.md - Catalog of scientific diagram types with examples
• references/best_practices.md - Publication standards and accessibility guidelines

External Resources

Python Libraries

• Schemdraw Documentation: https://schemdraw.readthedocs.io/
• NetworkX Documentation: https://networkx.org/documentation/
• Matplotlib Documentation: https://matplotlib.org/

Publication Standards

• Nature Figure Guidelines: https://www.nature.com/nature/for-authors/final-submission
• Science Figure Guidelines: https://www.science.org/content/page/instructions-preparing-initial-manuscript
• CONSORT Diagram: https://www.consort-spirit.org/

Integration with Other Skills

This skill works synergistically with:

• Scientific Writing - Diagrams follow figure best practices
• Scientific Visualization - Shares color palettes and styling
• LaTeX Posters - Generate diagrams for poster presentations
• Research Grants - Methodology diagrams for proposals
• Peer Review - Evaluate diagram clarity and accessibility

Quick Reference Checklist

Before submitting diagrams, run through the full checklist (visual quality, accessibility, typography, publication standards, required quality verification, documentation/version control, and final integration) in references/submission_checklist.md.

Environment Setup

# Required
export OPENROUTER_API_KEY='your_api_key_here'

# Get key at: https://openrouter.ai/keys

Getting Started

Simplest possible usage:

python scripts/generate_schematic.py "your diagram description" -o output.png

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Use this skill to create clear, accessible, publication-quality diagrams that effectively communicate complex scientific concepts. The AI-powered workflow with iterative refinement ensures diagrams meet professional standards.