Scientific SchematicsSAFE
CLI tool for configuring and monitoring Claude Code
Overview
From the repository's own README, as read at the audited commit. Badges and raw HTML are left out.
Generate any scientific diagram by describing it in natural language.
Nano Banana Pro creates publication-quality diagrams automatically - no coding, no templates, no manual drawing required.
Quick Start
Generate Any Diagram
# Set your OpenRouter API key export OPENROUTER_API_KEY='your_api_key_here' # Generate any scientific diagram python scripts/generate_schematic.py "CONSORT participant flow diagram" -o figures/consort.png # Neural network architecture python scripts/generate_schematic.py "Transformer encoder-decoder architecture" -o figures/transformer.png # Biological pathway python scripts/generate_schematic.py "MAPK signaling pathway" -o figures/pathway.png
What You Get
- Up to two iterations (v1, v2) with progressive refinement
- Automatic quality review after each iteration
- Detailed review log with scores and critiques (JSON format)
- Publication-ready images following scientific standards
Features
Iterative Refinement Process
- Generation 1: Create initial diagram from your description
- Review 1: AI evaluates clarity, labels, accuracy, accessibility
- Generation 2: Improve based on critique
- Review 2: Second evaluation with specific feedback
- Generation 3: Final polished version
Automatic Quality Standards
All diagrams automatically follow:
- Clean white/light background
- High contrast for readability
- Clear labels (minimum 10pt font)
- Professional typography
- Colorblind-friendly colors
- Proper spacing between elements
- Scale bars, legends, axes where appropriate
Installation
For AI Generation
# Get OpenRouter API key # Visit: https://openrouter.ai/keys # Set environment variable export OPENROUTER_API_KEY='sk-or-v1-...' # Or add to .env file echo "OPENROUTER_API_KEY=sk-or-v1-..." >> .env # Install Python dependencies (if not already installed) pip install requests
Usage Examples
###
0e2296a54d3bOBSERVED · 2026-10-06Install
Commands as the repository documents them. They are shown, not run.
pip install requests
pip install requests
pip install requests
pip install -r requirements.txt
What it tells the agent
The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.
--- name: scientific-schematics description: "Create publication-quality scientific diagrams using Nano Banana Pro AI with smart iterative refinement. Uses Gemini 3 Pro 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." allowed-tools: [Read, Write, Edit, Bash] --- # Scientific Schematics and Diagrams ## Overview Scientific schematics and diagrams transform complex concepts into clear visual representations for publication. **This skill uses Nano Banana Pro AI for diagram generation with Gemini 3 Pro quality review.** **How it works:** - Describe your diagram in natural language - Nano Banana Pro generates publication-quality images automatically - **Gemini 3 Pro 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 Pro 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 Pro handles everything automatically with **smart iteration**: ```bash # 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 Pro creates initial image following scientific diagram best practices 2. **Review 1**: **Gemini 3 Pro** 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: ```bash export OPENROUTER_API_KEY='your_api_key_here' ``` Get an API key at: https://openrouter.ai/keys ### 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 Pro generates it automatically: ```bash python scripts/generate_schematic.py "your diagram description" -o output.png ``` **That's it!** The AI handles: - ✓ Layout and com
Trust audit
SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | PASS |
| L1 | Static analysis of the code | PASS |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- declared (1 observation(s))
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (4)
return base64.b64decode(base64_str)
return base64.b64decode(base64_str)
return base64.b64decode(base64_str)
# Set permanently (add to ~/.bashrc or ~/.zshrc)
Gates applied: no_behavioural_pass.
0e2296a54d3bfull audit observations/trust-audit/skill/davila7__scientific-schematics.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-10-06 | 0e2296a54d3b | SAFE | B | 89 | first audit |
Questions
What does the Scientific Schematics skill do?
CLI tool for configuring and monitoring Claude Code
Is Scientific Schematics safe to install?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.
What can Scientific Schematics access on my machine?
The audit observed that it reaches the network. Each of those is consistent with what it says it does. Secrets in the source: none found.
How current is this page?
The grade is for one exact copy of the source (0e2296a54d3b), read on 2026-10-06. The repository is watched, and a new audit runs when it changes — this is the first audit.