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.
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
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:
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.
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:
Smart Iteration Benefits:
Output: Versioned images plus a detailed review log with quality scores, critiques, and early-stop information.
Set your OpenRouter API key:
export OPENROUTER_API_KEY='your_api_key_here'
Get an API key at: https://openrouter.ai/keys
Effective Prompts for Scientific Diagrams:
✓ Good prompts (specific, detailed):
✗ Avoid vague prompts:
Key elements to include:
Scientific Quality Guidelines (automatically applied):
This skill should be used when:
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:
Works for all diagram types:
No coding, no templates, no manual drawing required.
The AI generation system uses smart iteration - it only regenerates if quality is below the threshold for your document type:
┌─────────────────────────────────────────────────────┐
│ 1. Generate image with Nano Banana 2 │
│ ↓ │
│ 2. Review quality with Gemini 3.1 Pro Preview │
│ ↓ │
│ 3. Score >= threshold? │
│ YES → DONE! (early stop) │
│ NO → Improve prompt, go to step 1 │
│ ↓ │
│ 4. Repeat until quality met OR max iterations │
└─────────────────────────────────────────────────────┘
Prompt Construction:
Scientific diagram guidelines + User request
Output: diagram_v1.png
Gemini 3.1 Pro Preview evaluates the diagram on:
Example Review Output:
SCORE: 8.0
STRENGTHS:
- Clear flow from top to bottom
- All phases properly labeled
- Professional typography
ISSUES:
- Participant counts slightly small
- Minor overlap on exclusion box
VERDICT: ACCEPTABLE (for poster, threshold 7.0)
| If Score... | Action |
|---|---|
| >= threshold | STOP - Quality is good enough for this document type |
| < threshold | Continue to next iteration with improved prompt |
Example:
If quality is below threshold, the system:
All iterations are saved with a JSON review log that includes early-stop information:
{
"user_prompt": "CONSORT participant flow diagram...",
"doc_type": "poster",
"quality_threshold": 7.0,
"iterations": [
{
"iteration": 1,
"image_path": "figures/consort_v1.png",
"score": 7.5,
"needs_improvement": false,
"critique": "SCORE: 7.5\nSTRENGTHS:..."
}
],
"final_score": 7.5,
"early_stop": true,
"early_stop_reason": "Quality score 7.5 meets threshold 7.0 for poster"
}
Note: With smart iteration, you may see only 1 iteration instead of the full 2 if quality is achieved early!
from scripts.generate_schematic_ai import ScientificSchematicGenerator
# Initialize generator
generator = ScientificSchematicGenerator(
api_key="your_openrouter_key",
verbose=True
)
# Generate with iterative refinement (max 2 iterations)
results = generator.generate_iterative(
user_prompt="Transformer architecture diagram",
output_path="figures/transformer.png",
iterations=2
)
# Access results
print(f"Final score: {results['final_score']}/10")
print(f"Final image: {results['final_image']}")
# Review individual iterations
for iteration in results['iterations']:
print(f"Iteration {iteration['iteration']}: {iteration['score']}/10")
print(f"Critique: {iteration['critique']}")
# Basic usage (default threshold 7.5/10)
python scripts/generate_schematic.py "diagram description" -o output.png
# Specify document type for appropriate quality threshold
python scripts/generate_schematic.py "diagram" -o out.png --doc-type journal # 8.5/10
python scripts/generate_schematic.py "diagram" -o out.png --doc-type conference # 8.0/10
python scripts/generate_schematic.py "diagram" -o out.png --doc-type poster # 7.0/10
python scripts/generate_schematic.py "diagram" -o out.png --doc-type presentation # 6.5/10
# Custom max iterations (1-2)
python scripts/generate_schematic.py "complex diagram" -o diagram.png --iterations 2
# Verbose output (see all API calls and reviews)
python scripts/generate_schematic.py "flowchart" -o flow.png -v
# Provide API key via flag
python scripts/generate_schematic.py "diagram" -o out.png --api-key "sk-or-v1-..."
# Combine options
python scripts/generate_schematic.py "neural network" -o nn.png --doc-type journal --iterations 2 -v
1. Be Specific About Layout:
✓ "Flowchart with vertical flow, top to bottom"
✓ "Architecture diagram with encoder on left, decoder on right"
✓ "Circular pathway diagram with clockwise flow"
2. Include Quantitative Details:
✓ "Neural network with input layer (784 nodes), hidden layer (128 nodes), output (10 nodes)"
✓ "Flowchart showing n=500 screened, n=150 excluded, n=350 randomized"
✓ "Circuit with 1kΩ resistor, 10µF capacitor, 5V source"
3. Specify Visual Style:
✓ "Minimalist block diagram with clean lines"
✓ "Detailed biological pathway with protein structures"
✓ "Technical schematic with engineering notation"
4. Request Specific Labels:
✓ "Label all arrows with activation/inhibition"
✓ "Include layer dimensions in each box"
✓ "Show time progression with timestamps"
5. Mention Color Requirements:
✓ "Use colorblind-friendly colors"
✓ "Grayscale-compatible design"
✓ "Color-code by function: blue for input, green for processing, red for output"
python scripts/generate_schematic.py \
"CONSORT participant flow diagram for randomized controlled trial. \
Start with 'Assessed for eligibility (n=500)' at top. \
Show 'Excluded (n=150)' with reasons: age<18 (n=80), declined (n=50), other (n=20). \
Then 'Randomized (n=350)' splits into two arms: \
'Treatment group (n=175)' and 'Control group (n=175)'. \
Each arm shows 'Lost to follow-up' (n=15 and n=10). \
End with 'Analyzed' (n=160 and n=165). \
Use blue boxes for process steps, orange for exclusion, green for final analysis." \
-o figures/consort.png
python scripts/generate_schematic.py \
"Transformer encoder-decoder architecture diagram. \
Left side: Encoder stack with input embedding, positional encoding, \
multi-head self-attention, add & norm, feed-forward, add & norm. \
Right side: Decoder stack with output embedding, positional encoding, \
masked self-attention, add & norm, cross-attention (receiving from encoder), \
add & norm, feed-forward, add & norm, linear & softmax. \
Show cross-attention connection from encoder to decoder with dashed line. \
Use light blue for encoder, light red for decoder. \
Label all components clearly." \
-o figures/transformer.png --iterations 2
python scripts/generate_schematic.py \
"MAPK signaling pathway diagram. \
Start with EGFR receptor at cell membrane (top). \
Arrow down to RAS (with GTP label). \
Arrow to RAF kinase. \
Arrow to MEK kinase. \
Arrow to ERK kinase. \
Final arrow to nucleus showing gene transcription. \
Label each arrow with 'phosphorylation' or 'activation'. \
Use rounded rectangles for proteins, different colors for each. \
Include membrane boundary line at top." \
-o figures/mapk_pathway.png
python scripts/generate_schematic.py \
"IoT system architecture block diagram. \
Bottom layer: Sensors (temperature, humidity, motion) in green boxes. \
Middle layer: Microcontroller (ESP32) in blue box. \
Connections to WiFi module (orange box) and Display (purple box). \
Top layer: Cloud server (gray box) connected to mobile app (light blue box). \
Show data flow arrows between all components. \
Label connections with protocols: I2C, UART, WiFi, HTTPS." \
-o figures/iot_architecture.png
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.
\includegraphics{} for generated imagesProblem: Overlapping text or elements
--iterations 2 for better refinementProblem: Elements not connecting properly
Problem: Export quality poor
--iterations 2Problem: Elements overlap after generation
--iterations 2 for better refinementProblem: False positive overlap detection
detect_overlaps(image_path, threshold=0.98)Problem: Generated image quality is low
--iterations 2Problem: Colorblind simulation shows poor contrast
Problem: High-severity overlaps detected
Problem: Visual report generation fails
Image.open(path).verify()Problem: Colors indistinguishable in grayscale
verify_accessibility(image_path)Problem: Text too small when printed
validate_resolution(image_path)Problem: Accessibility checks consistently fail
Load these files for comprehensive information on specific topics:
references/best_practices.md - Publication standards and accessibility guidelinesPython Libraries
Publication Standards
This skill works synergistically with:
Before submitting diagrams, verify:
run_quality_checks() and achieved PASS statusquality_reports/ directory\ref{} points to correct figure)# Required
export OPENROUTER_API_KEY='your_api_key_here'
# Get key at: https://openrouter.ai/keys
Simplest possible usage:
python scripts/generate_schematic.py "your diagram description" -o output.png
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.
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概要と使いどころ
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日本語の概要は準備中です。原文の説明を表示しています。