Create publication-quality scientific diagrams using Nano Banana 2 AI with smart iterative refinement. Uses Gemini 3.1 Pro Preview for quality review. Only regenerates if quality is below threshold for your document type. Specialized in neural network architectures, system diagrams, flowcharts, biological pathways, and complex scientific visualizations.
SKILL.md
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.
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:
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.
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:
Generation 1: Nano Banana 2 creates initial image following scientific diagram best practices
Review 1: Gemini 3.6 Flash evaluates quality against document-type threshold
Decision: If quality >= threshold → DONE (no more iterations needed!)
If below threshold: Improved prompt based on critique, regenerate
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.
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"
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:
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
Clarity over complexity - Simplify, remove unnecessary elements
Consistent styling - Describe the same visual conventions across a paper's figures
Colorblind accessibility - Ask for the Okabe-Ito palette and redundant encoding
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
Vector output - PNG only; no PDF, SVG, or EPS is produced
Resolution control - the image model chooses; there is no DPI flag
Color space - RGB only; convert for CMYK print workflows downstream
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
Include in LaTeX - Use \includegraphics{} for generated images
Caption thoroughly - Describe all elements and abbreviations
Reference in text - Explain diagram in narrative flow
Maintain consistency - Same style across all figures in paper
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.
The diagram is wrong
Overlapping text, crowded elements, or arrows that miss their targets
Name the layout in the prompt: "vertical flow, one box per row, generous spacing between stages"
Name the connections: "arrow from RAF to MEK labelled phosphorylation", not "show the cascade"
Re-run. Two runs of the same prompt differ, and a bad layout is often just an unlucky draw
Content is scientifically wrong or a component is missing
List the components explicitly, with counts and labels — the model will not infer them
Read the critique field in the review log: the reviewer usually names what it saw missing
Wrong text in labels, or figure numbering baked into the image
The prompt already forbids "Figure 1:" captions; if one appears anyway, re-run
Misspelled labels are the most common failure of image models. Read every label before using it
The score seems wrong
Score is lower than the diagram deserves
Read the critique before re-running; the reviewer's complaint is often legitimate and specific
The threshold, not the score, decides whether it iterates — --doc-type journal demands 8.5
A run stops at a score below the threshold
That is the iteration cap. --iterations 2 is the maximum; the last image is kept and reported
with its real score
"score": null and "reviewed": false in the log
The review call failed or answered in an unusable shape. The image is fine and was kept; only its
quality was never measured. Check "review_error", look at the image yourself, and re-run
Setup
Error: OPENROUTER_API_KEY not found
export OPENROUTER_API_KEY='sk-or-v1-...', or add it to a .env file, or pass --api-key
Error: requests library not found
uv pip install requests
Any API error — run with -v to see the request, the model slug, and the full error body
Resources and References
Detailed References
Load these files for comprehensive information on specific topics:
references/iterative_refinement.md - The generate-review-refine loop, the Python API, every
command-line option, prompt engineering guidance, and four worked examples
references/best_practices.md - Publication standards and accessibility guidelines to draw
on when writing prompts and when judging the result
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.
