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nanobanana

Generate and edit images with Google Gemini Nano Banana using prompts and reference images.

Updated Aug 23, 2026
Search Tags:#gemini#image-generation#reference-images#ai-art#batch-generation#background-removal#sticker-sheets
One-click install
npx skills add https://github.com/neighbor-game-ai/DreamCore-V2-sandbox --skill nanobanana-neighbor-game-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: nanobanana
Source: https://github.com/neighbor-game-ai/DreamCore-V2-sandbox/tree/main/.claude/skills/nanobanana
Command: npx skills add https://github.com/neighbor-game-ai/DreamCore-V2-sandbox --skill nanobanana-neighbor-game-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires google-genai, pillow, numpy, scipy, opencv-python.

What problem does it solve?

Nano Banana automates image generation and editing using Google Gemini, enabling quick creation and modification of artwork and stickers without manual drawing.

Core Features & Use Cases

  • Generate images from prompts with optional reference images and magenta-background mode.
  • Edit or stylize existing images by applying reference styles or batch-processing sheets for stickers.
  • Prepare outputs with background removal and object splitting for sticker sheets.

Quick Start

Provide a Gemini Nano Banana prompt to generate or edit an image, optionally using a reference image and magenta background settings.

Frequently Asked Questions about nanobanana

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate and edit images using Google Gemini?▼

To generate and edit images with Gemini, you provide a text prompt and optional reference images. The system handles style transfer, magenta-background generation, and output post-processing like background removal automatically.

Can I use a reference image for style transfer in AI image generation?▼

Yes, you can use reference images for style transfer in AI image generation. The workflow accepts optional reference inputs to apply specific visual styles to newly generated artwork or modify existing images.

What's the best way to automate batch generation and sticker sheet splitting?▼

Batch generation and sticker sheet splitting are automated by processing generated images with OpenCV and NumPy. The system automatically detects object boundaries to split sheets and remove backgrounds for individual stickers.

Do I need Python dependencies like OpenCV and Pillow to remove image backgrounds?▼

Yes, Python dependencies like OpenCV, Pillow, NumPy, and SciPy are required to remove image backgrounds. These libraries handle the automated post-processing tasks for magenta-background detection and sticker sheet splitting.

What is magenta-background mode used for in AI art generation?▼

Magenta-background mode in AI art generation is used to create a uniform background color. This specific color channel allows automated post-processing tools to easily isolate subjects and perform precise background removal.

Does this image generation workflow support multi-image inputs and batch processing?▼

Yes, this image generation workflow supports multi-image inputs and batch processing. It validates inputs and handles both single and multi-image workflows to generate artwork or process sticker sheets efficiently.

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