Nano Banana 2 Infographic

Create sleek, rich, non-noisy infographic prompts and review sets for Gemini image generation.

This skill uses Nano Banana 2 only. For API calls, use the live callable model ID rather than assuming the public marketing name is the exact endpoint name.

Decision Tree

What do you need to do?

• The brief is incomplete or fuzzy Ask only for the missing essentials: topic, audience/context, must-include facts, and brand or style constraints.

• The user wants an infographic now Prepare four review variants by default at 16:9 unless the user specified another ratio. Read references/patterns.md.

• The user wants live Gemini renders or proof that the prompt works Read references/configuration.md, then run scripts/probe_gemini_image_api.py.

• The user wants exact API syntax, model IDs, or request fields Read references/api.md.

• The result looks noisy, text-heavy, or poster-like Read references/gotchas.md, simplify the composition, and regenerate.

Default Operating Mode

• Offer four distinct variants by default unless the user explicitly asks for fewer.
• Default aspect ratio to 16:9.
• Use Nano Banana 2 only. Do not fall back to older image models unless the user explicitly asks.
• Render the default pack concurrently when you need live outputs fast.
• Use separate render passes for the variants instead of trusting one request to return the exact number of images requested.
• Keep visible text short: title up to 5 words, labels 1-3 words, no paragraphs in the image.
• Prefer editorial restraint over maximal detail. If a choice would add noise, cut it.

Intake Questions

Ask these only when they are not already answered:

Missing	Ask
Topic or claim	"What is the infographic about, in one sentence?"
Audience or channel	"Where will this live: blog post, deck, report, keynote, or something else?"
Facts or sections	"Which numbers, claims, or sections must appear?"
Style boundaries	"Any brand colours, must-avoid looks, or reference tone?"

If the user already gave the essentials, do not re-interview them. Build the variant pack immediately.

Quick Reference

Need	Do	Output
Fast prompt pack	Run scripts/build_variant_pack.py with a brief JSON	four prompt variants plus a markdown review sheet
Fast parallel render	Run scripts/render_variant_pack.py on variant-pack.json	all variants rendered concurrently plus a batch manifest
Live render proof	Run scripts/probe_gemini_image_api.py on one prompt	saved response JSON and local image files
Default professional set	Use Executive Snapshot, Editorial Column, Decision Board, and Insight Ribbon	four reviewable directions
Noise reduction	Remove extra panels, colors, and prose before re-rendering	cleaner second pass

Default Variant Quartet

Variant	Best For	Direction
Executive Snapshot	C-suite slides, board pre-reads, strategic summaries	one dominant claim or number with 3-4 disciplined support blocks
Editorial Column	Blog posts, reports, explainers	tall stacked panels with generous whitespace and thin dividers
Decision Board	trade-offs, frameworks, comparisons	modular grid or side-by-side layout with equal visual weight
Insight Ribbon	keynote hero slides, opener visuals, and wide summaries	one horizontal narrative band with evenly spaced support modules

Read references/patterns.md for the exact prompt shape and regeneration ladder.

Rendering Rules

• Say 16:9 explicitly unless the user asked for another ratio.
• Ask for a white or near-white base, restrained accents, and flat editorial graphics.
• Keep to 2-3 accent colours plus gray or white.
• Use one visual idea per image. Do not combine process, comparison, glossary, and hero illustration in the same render.
• Put the long explanation outside the image. Generate the copy first, then render only the short text that must appear.

Gotchas

1. Asking for a "detailed infographic" usually increases clutter rather than clarity. Ask for hierarchy, whitespace, and restraint instead.
2. Google documents that the model might not create the exact number of images requested. Treat the four default variants as four deliberate passes.
3. Google also documents that text generation works best when the text is decided first and then rendered into the image. Do not improvise long copy inside the image prompt.
4. If the image looks like a poster, reduce the number of panels, colors, and icon families before changing everything else.
5. When the user needs dense quantitative fidelity, hand-built charts or vector layouts may be a better fit than Gemini image generation.

Reading Guide

Task	Read
Model IDs, request fields, aspect ratios, response shape	references/api.md
Variant design, question flow, prompt formula, iteration ladder	references/patterns.md
Environment setup, scripts, and live verification commands	references/configuration.md
Noise, text, language, and retry pitfalls	references/gotchas.md