Supports neural network architectures, system diagrams, flowcharts, biological pathways, circuit diagrams, and complex scientific visualizations through natural language descriptions
Uses Nano Banana Pro for generation and Gemini 3 Pro for quality review, with smart iteration that stops early if quality meets the threshold for your document type (journal: 8.5/10, poster: 7.0/10, presentation: 6.5/1
Fetches scientific-schematics from davila7/claude-code-templates and configures it for Cursor.
3
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4
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Confirm successful installation by checking the skill directory location:
.cursor/skills/scientific-schematics
Restart Cursor to activate scientific-schematics. Access via /scientific-schematics in your agent's command palette.
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Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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:
# 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 --iterations2 --doc-type journal
What happens behind the scenes:
Generation 1: Nano Banana Pro creates initial image following scientific diagram best practices
Review 1: Gemini 3 Pro 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 plus a detailed review log with quality scores, critiques, and early-stop information.
"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"
Layout and Composition (0-2 points) - Logical flow, balanced, no overlaps
Professional Appearance (0-2 points) - Publication-ready quality
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)
Decision Point: Continue or Stop?
If Score...
Action
>= threshold
STOP - Quality is good enough for this document type
< threshold
Continue to next iteration with improved prompt
Example:
For a poster (threshold 7.0): Score of 7.5 → DONE after 1 iteration!
For a journal (threshold 8.5): Score of 7.5 → Continue improving
Subsequent Iterations (Only If Needed)
If quality is below threshold, the system:
Extracts specific issues from Gemini 3 Pro's review
Enhances the prompt with improvement instructions
Regenerates with Nano Banana Pro
Reviews again with Gemini 3 Pro
Repeats until threshold met or max iterations reached
Review Log
All iterations are saved with a JSON review log that includes early-stop information:
›Access to product documentation and roadmap tools (Jira, Notion, etc.)
›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
1Install product management skill
2Start with user story generation for known feature
3Progress to competitive analysis: research 2-3 competitors
4Use for roadmap prioritization: apply RICE/ICE scoring
5Draft stakeholder communications and refine based on feedback
6Build template library for recurring PM tasks
7Share effective prompts with product team
Common Pitfalls
⚠Not validating competitive research—verify facts before sharing
⚠Accepting user stories without involving engineering team
⚠Over-relying on frameworks without qualitative judgment
⚠Not customizing outputs to company culture and communication style
⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
+Validate research and competitive analysis with real data
+Collaborate with engineering when generating technical requirements
+Customize frameworks and templates to your company context
+Use skill for first drafts, refine with stakeholder input
+Document successful prompt patterns for PM tasks
+Combine AI efficiency with human judgment and intuition
✗ Don't
−Don't publish competitive analysis without fact-checking
−Don't finalize user stories without engineering review
−Don't make prioritization decisions solely on AI scoring
−Don't skip customer validation of generated requirements
−Don't ignore company-specific context and culture
💡 Pro Tips
★Provide context: company goals, constraints, customer feedback
★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
1Basic: user stories, feature specs, status updates
