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 3.7 Flash quality review.

How it works:

• Describe your diagram in natural language
• Nano Banana 2 generates a PNG draft from the specified components and relationships
• Gemini 3.7 Flash reviews quality against document-type thresholds
• Smart iteration: Refines when the score or verdict requests improvement
• Saved drafts and a review log for manual verification
• No coding, templates, or manual drawing required

Local review thresholds by document type (heuristics chosen by this helper, not publisher acceptance criteria):

Save diagrams under figures/ using -o figures/name.png, then inspect every label and relationship before including them in a paper or poster. The output location is the path you choose.

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

Run from this skill directory with requests installed and OPENROUTER_API_KEY set. The following paid-generation examples are illustrative; the request contracts are tested offline. Supply source-backed labels and relationships rather than asking the image model to invent them:

# 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 2 creates initial image following scientific diagram best practices
2. Review 1: Gemini 3.7 Flash evaluates quality against document-type threshold
3. Decision: If quality >= threshold and no improvement verdict → DONE (no more iterations needed!)
4. If below threshold or improvement requested: 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 (name_v1.png, name_v2.png), a copy of the latest successful version at the path you asked for, and name_review_log.json with the score, critique, and any early-stop reason.

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 with the reason in "review_error". "reviewed": false means the reviewer did not return a usable response; a prose answer with no parsable score can have "reviewed": true. "final_reviewed" remains false without a numeric score, and the run prints Review unavailable - image kept, quality not verified. Treat that image as unchecked and

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