gh-k-dense-ai-claude-scientific-writer/skills/scientific-schematics/QUICK_REFERENCE.md

Scientific Schematics - Quick Reference

How it works: Describe your diagram → Nano Banana Pro generates it automatically

Setup (One-Time)

# Get API key from https://openrouter.ai/keys
export OPENROUTER_API_KEY='sk-or-v1-your_key_here'

# Add to shell profile for persistence
echo 'export OPENROUTER_API_KEY="sk-or-v1-your_key"' >> ~/.bashrc  # or ~/.zshrc

Basic Usage

# Describe your diagram, Nano Banana Pro creates it
python scripts/generate_schematic.py "your diagram description" -o output.png

# That's it! Automatic:
# - Iterative refinement (3 rounds)
# - Quality review and improvement
# - Publication-ready output

Common Examples

CONSORT Flowchart

python scripts/generate_schematic.py \
  "CONSORT flow: screened n=500, excluded n=150, randomized n=350" \
  -o consort.png

Neural Network

python scripts/generate_schematic.py \
  "Transformer architecture with encoder and decoder stacks" \
  -o transformer.png

Biological Pathway

python scripts/generate_schematic.py \
  "MAPK pathway: EGFR → RAS → RAF → MEK → ERK" \
  -o mapk.png

Circuit Diagram

python scripts/generate_schematic.py \
  "Op-amp circuit with 1kΩ resistor and 10µF capacitor" \
  -o circuit.png

Command Options

Option	Description	Example
-o, --output	Output file path	-o figures/diagram.png
--iterations N	Number of refinements (1-10)	--iterations 5
-v, --verbose	Show detailed output	-v
--api-key KEY	Provide API key	--api-key sk-or-v1-...

Prompt Tips

✓ Good Prompts (Specific)

• "CONSORT flowchart with screening (n=500), exclusion (n=150), randomization (n=350)"
• "Transformer architecture: encoder on left with 6 layers, decoder on right, cross-attention connections"
• "MAPK signaling: receptor → RAS → RAF → MEK → ERK → nucleus, label each phosphorylation"

✗ Avoid (Too Vague)

• "Make a flowchart"
• "Neural network"
• "Pathway diagram"

Output Files

For input diagram.png, you get:

• diagram_v1.png - First iteration
• diagram_v2.png - Second iteration
• diagram_v3.png - Final iteration
• diagram.png - Copy of final
• diagram_review_log.json - Quality scores and critiques

Review Log

{
  "iterations": [
    {
      "iteration": 1,
      "score": 7.0,
      "critique": "Good start. Font too small..."
    },
    {
      "iteration": 2,
      "score": 8.5,
      "critique": "Much improved. Minor spacing issues..."
    },
    {
      "iteration": 3,
      "score": 9.5,
      "critique": "Excellent. Publication ready."
    }
  ],
  "final_score": 9.5
}

Python API

from scripts.generate_schematic_ai import ScientificSchematicGenerator

# Initialize
gen = ScientificSchematicGenerator(api_key="your_key")

# Generate
results = gen.generate_iterative(
    user_prompt="diagram description",
    output_path="output.png",
    iterations=3
)

# Check quality
print(f"Score: {results['final_score']}/10")

Troubleshooting

API Key Not Found

# Check if set
echo $OPENROUTER_API_KEY

# Set it
export OPENROUTER_API_KEY='your_key'

Import Error

# Install requests
pip install requests

Low Quality Score

• Make prompt more specific
• Include layout details (left-to-right, top-to-bottom)
• Specify label requirements
• Increase iterations: --iterations 5

Testing

# Verify installation
python test_ai_generation.py

# Should show: "6/6 tests passed"

Cost

Typical cost per diagram (3 iterations):

• Simple: $0.10-0.30
• Complex: $0.30-0.50

How Nano Banana Pro Works

Simply describe your diagram in natural language:

• ✓ No coding required
• ✓ No templates needed
• ✓ No manual drawing
• ✓ Automatic quality review
• ✓ Publication-ready output
• ✓ Works for any diagram type

Just describe what you want, and it's generated automatically.

Getting Help

# Show help
python scripts/generate_schematic.py --help

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

Quick Start Checklist

• Set OPENROUTER_API_KEY environment variable
• Run python test_ai_generation.py (should pass 6/6)
• Try: python scripts/generate_schematic.py "test diagram" -o test.png
• Review output files (test_v1.png, v2, v3, review_log.json)
• Read SKILL.md for detailed documentation
• Check README.md for examples

Resources

• Full documentation: SKILL.md
• Detailed guide: README.md
• Implementation details: IMPLEMENTATION_SUMMARY.md
• Example script: example_usage.sh
• Get API key: https://openrouter.ai/keys