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 3.6 Flash quality review.

How it works:

• Describe your diagram in natural language
• Nano Banana 2 generates publication-quality images automatically
• Gemini 3.6 Flash 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:

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

What the output is: a raster PNG at whatever resolution the image model returns. This skill has no vector path and no DPI control — if a journal demands PDF, EPS, or 300 dpi TIFF, convert the PNG downstream and check the result at final print size.

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 3.6 Flash 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 (name_v1.png, name_v2.png), a copy of the winner at the path you asked for, and name_review_log.json with the score, critique, and early-stop reason per iteration.

When the review cannot run — a rate limit, a content filter, a reviewer that answers in some unexpected shape — the image is still generated and saved, but no score is invented for it. The log records "score": null and "reviewed": false with the reason in "review_error", and the run prints Review unavailable — image kept, quality not verified. Treat that image as unchecked and look at it yourself; re-running is worth a try, since the failure is usually transient.

Configuration

Set your OpenRouter API key:

export OPENROUTER_API_KEY='your_api_key_here'

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

Data leaves the machine. Your prompt is sent to OpenRouter to generate the image, and the generated image is sent back to OpenRouter for the quality review. Both are subject to OpenRouter's data policies and those of the underlying model providers. Do not describe unpublished data, patient information, or anything under embargo in the prompt.

AI Generation Best Practices

Effective Prompts for Scientific Diagrams:

✓ Good prompts (specific, detailed):

• "CONSORT flowchart showing participant flow from screening (n=500) through randomization to final analysis"
• "Transformer neural network architecture with encoder stack on left, decoder stack on right, showing multi-head attention and cross-attention connections"
• "Biological signaling cascade: EGFR receptor → RAS → RAF → MEK → ERK → nucleus, with phosphorylation steps labeled"
• "Block diagram of IoT system: sensors → microcontroller → WiFi module → cloud server → mobile app"

✗ Avoid vague prompts:

• "Make a flowchart" (too generic)
• "Neural network" (which type? what components?)
• "Pathway diagram" (which pathway? what molecules?)

Key elements to include:

• Type: Flowchart, architecture diagram, pathway, circuit, etc.
• Components: Specific elements to include
• Flow/Direction: How elements connect (left-to-right, top-to-bottom)
• Labels: Key annotations or text to include
• Style: Any specific visual requirements

Scientific Quality Guidelines (automatically applied):

• Clean white/light background
• High contrast for readability
• Clear, readable labels (minimum 10pt)
• Professional typography (sans-serif fonts)
• Colorblind-friendly colors (Okabe-Ito palette)
• Proper spacing to prevent crowding
• Scale bars, legends, axes where appropriate

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.

AI Generation Mode (Nano Banana 2 + Gemini 3.6 Flash Review)

Smart Iterative Refinement, Advanced Usage, and Examples

The generate-review-refine loop, the Python API and command-line options, prompt engineering guidance, and four worked examples (CONSORT flowchart, neural network architecture, biological pathway, system architecture) are in references/iterative_refinement.md.

The loop stops as soon as the review passes, so a simple diagram usually costs one iteration; only complex figures use the full budget.

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 principles — ask for these in the prompt

1. Clarity over complexity - Simplify, remove unnecessary elements
2. Consistent styling - Describe the same visual conventions across a paper's figures
3. Colorblind accessibility - Ask for the Okabe-Ito palette and redundant encoding
4. Appropriate typography - Sans-serif fonts, generously sized labels
5. Logical flow - State the direction (left-to-right, top-to-bottom) explicitly

The generator applies all of these by default, but naming them in your own words for the specific diagram works better than relying on the built-in guidelines alone.

What the pipeline cannot do

1. Vector output - PNG only; no PDF, SVG, or EPS is produced
2. Resolution control - the image model chooses; there is no DPI flag
3. Color space - RGB only; convert for CMYK print workflows downstream
4. Exact line weights or text sizes - describe them in the prompt, then verify by eye

For a journal that requires vector art or 300+ dpi TIFF, convert the PNG after generation and check the result at the size it will actually be printed.

Integration Guidelines

1. Include in LaTeX - Use \includegraphics{} for generated images
2. Caption thoroughly - Describe all elements and abbreviations
3. Reference in text - Explain diagram in narrative flow
4. Maintain consistency - Same style across all figures in paper
5. Version control - Keep prompts and generated images in repository

Troubleshooting Common Issues

Generation is stochastic and iteration is capped at 2, so the levers that actually change the outcome are the prompt, the document type, and re-running. There is no post-processing step and no quality-checking library in this skill: everything you can inspect lives in the generated PNG and in <name>_review_log.json.