nanobanana

name

nanobanana

description

Craft high-precision prompts and edit instructions for Nano Banana image workflows, especially when using the local nanobanana MCP tools for generation, editing, character consistency, or multi-image fusion. Use when the task needs structured prompts, reference-role assignment, layout-heavy image specs, typography-heavy images, iterative edit-first refinement, or reliable model/aspect/output-path choices.

Nanobanana

Use this skill to turn a vague image request into a prompt that Nano Banana can execute reliably, or to refine an existing image/edit request without restarting from scratch.

Workflow

1. Identify the operation first.

• generate: create a new image from text and optional references.
• edit: modify an existing image while preserving specified elements.
• character_consistency: keep one character stable across scenes.
• multi_image_fusion: combine several references into one coherent result.

1. Choose the lowest-complexity prompt shape that fits the ask.

• Use a short natural-language prompt for simple scenes.
• Use a structured block for multi-part layouts, infographics, or scene logic.
• Use JSON-like structure when the user needs many simultaneous constraints or multiple reference roles.

1. Build the prompt in this order.

• Subject: who or what must appear.
• Action/state: what is happening.
• Setting: where it is happening.
• Composition: framing, camera angle, spatial layout.
• Lighting/mood: time of day, light direction, contrast, atmosphere.
• Style/materiality: photoreal, editorial, infographic, matte acrylic, film grain, etc.
• Constraint layer: exact text, counts, positions, identity preservation, what must remain unchanged.

1. Prefer positive, explicit constraints.

• Say what should be present and how it should behave.
• For edits, explicitly state what must remain unchanged.
• For text rendering, quote exact text and specify placement plus typographic character.

1. Use edit-first iteration.

• If the first output is close, preserve the successful parts and request only the delta.
• Example: Keep composition, subject identity, and wardrobe identical; shift lighting to golden hour and replace background with a foggy bridge.

Local MCP Rules

• mcp__nanobanana__generate_image is for new images.
• mcp__nanobanana__edit_image is for targeted changes to an existing image.
• mcp__nanobanana__character_consistency is for repeated scenes with one character reference.
• mcp__nanobanana__multi_image_fusion is for combining several references.
• output_path must stay inside the tool's allowed repo-local output area. In practice, use a simple filename or relative path; do not pass an absolute path outside image_out.

Model Selection

• Use gemini-3-pro-image-preview for the highest-fidelity structured prompting, typography, technical diagrams, and dense layout work.
• Use gemini-3.1-flash-image-preview when speed matters or when extra-wide ratios like 4:1, 1:4, 8:1, or 1:8 are required.
• Use gemini-2.5-flash-image for fast, simpler iterations when top-end fidelity is not necessary.

Prompting Patterns

• For simple generation, use the formula in references/frameworks.md.
• For structured scene blueprints, multi-reference prompts, and edit instructions, use references/frameworks.md.
• For reusable prompt skeletons across posters, product shots, infographics, floor plans, storyboards, and character scenes, use references/examples.md.

Guardrails

• Do not overstuff the prompt with decorative synonyms when concrete constraints will do.
• For exact counts, rows, layouts, labels, or room sizes, state them numerically and spatially.
• For reference images, assign each image a job such as identity, pose, style, lighting, or environment.
• For text-heavy images, keep each required text string short unless the user explicitly needs a dense poster or infographic.
• When an example from the source material is unsafe, irrelevant, or too verbose for direct reuse, extract the pattern and rewrite it into a safe, shorter template rather than copying it.

Deliverable Style

• If the user asks for an image, provide a prompt plus suggested model, aspect ratio, and any reference-role mapping.
• If the user asks for an edit, provide the preservation constraints first, then the requested changes.
• If the user asks for multiple options, vary composition and lighting before varying everything else.