amhuppert/my-ai-resources/nano-banana-prompt

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Create a high-quality image generation prompt for Google's Nano

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SKILL.md

Nano Banana 2 Prompt Generator

Generate optimized prompts for Google's Nano Banana 2 (Gemini 3.1 Flash Image) model. Transform user concepts into detailed, well-structured prompts that leverage NB2's strengths: natural language understanding, thinking mode, text rendering, character consistency, and image search grounding.

<user-request> Use the image concept and constraints supplied in the user's invocation. </user-request>

Step 1: Understand the User's Intent

Extract from the user's request:

  • Subject: The primary subject(s) of the image
  • Style: Desired visual style (photograph, illustration, painting, 3D render, etc.)
  • Mood/Atmosphere: Emotional tone or feeling
  • Use case: What the image is for (social media, print, product mockup, etc.)
  • Special requirements: Text rendering, character consistency, specific aspect ratio, etc.

If the request is too vague to produce a quality prompt (e.g., just "a cat"), ask the user to clarify intent. Ask about style preference, mood, and intended use — but only ask what's genuinely ambiguous. A request like "a cyberpunk cityscape at night" has enough to work with.

Step 2: Compose the Prompt

Read references/prompting-guide.md for style vocabulary, composition terms, lighting terms, and prompt formula templates. Then construct the prompt following these core principles:

Prompt Structure

Build prompts using natural language sentences, not comma-separated keyword tags. NB2 is a reasoning model — brief it like a creative director, not a search engine.

Assemble these elements in order:

  1. Style/Medium — Lead with the visual format: "A cinematic photograph of...", "A watercolor illustration of...", "A minimalist vector graphic of..."
  2. Subject — Describe who/what with specific physical details (age, clothing, material, color, texture)
  3. Action — What is happening in the scene
  4. Setting/Environment — Where the scene takes place, with atmospheric details
  5. Composition — Camera angle, framing, depth of field (see reference guide for terms)
  6. Lighting — Light source, quality, color temperature
  7. Mood/Atmosphere — Emotional tone reinforcement
Critical Rules
  • Describe what to include, never what to exclude ("empty street" not "no cars")
  • Use specific descriptors over generic ones ("navy blue tweed blazer" not "nice jacket")
  • Include materiality when relevant ("brushed steel", "crumpled linen", "weathered oak")
  • Wrap text in quotes when the image should contain readable text (e.g., a sign reading "Open 24 Hours")
  • Specify typography for text elements ("bold sans-serif", "hand-lettered script", "neon cursive")
Thinking Mode Recommendation

Append a thinking mode recommendation based on prompt complexity:

ComplexityThinking ModeWhen to Use
SimpleMinimal (default)Single subject, clear style, no text
ModerateHighMulti-element scenes, text rendering, specific composition
ComplexDynamicInfographics, multi-character consistency, data visualization

Step 3: Output the Result

Present the output in this format:

~~~markdown

Nano Banana 2 Prompt

Prompt:

[The full optimized prompt text]

Recommended Settings:

  • Thinking mode: [Minimal / High / Dynamic]
  • Aspect ratio: [ratio, e.g., 16:9, 1:1, 9:16]
  • Resolution: [1K / 2K / 4K based on use case]

Tips:

  • [1-3 specific tips for iterating on this particular prompt]

~~~

Output Guidelines
  • Produce exactly one prompt unless the user asks for variations
  • Keep prompts to 2-5 sentences — detailed but not bloated
  • If the user's concept would benefit from image search grounding (real landmarks, known products, public figures), note this capability
  • If the concept involves multiple images with consistent characters, note that NB2 supports up to 5-person consistency with reference images

Additional Resources

Reference Files
  • `references/prompting-guide.md` — Comprehensive Nano Banana 2 prompting reference including style vocabulary, composition terms, lighting terms, text rendering patterns, editing workflows, and example prompt formulas

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