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Best practices for AI image generation prompts with Gemini models. Activated when the user works with nanobanana image generation or editing tools. Provides guidance on prompt structure, style keywords, and iterative refinement.
This skill uses the workspace's default tool permissions.
Image Prompting Best Practices
When helping users generate or edit images with nanobanana, apply these guidelines.
Prompt Structure for Generation
A strong image prompt follows this pattern: [Subject] + [Style] + [Composition] + [Lighting/Mood] + [Details]
Example: "A cozy coffee shop interior, watercolor illustration style, wide angle view, warm golden lighting, with plants on shelves and a cat sleeping on a chair"
Model Selection Guide
nanobanana supports three Gemini models via the NANOBANANA_MODEL env var:
| Model | Best For |
|---|---|
gemini-2.5-flash-image (default) | Fast generation, prototyping, high-volume work |
gemini-3-pro-image-preview | Complex prompts, text rendering in images, high quality |
gemini-3.1-flash-image-preview | Latest features, advanced capabilities |
Recommend model changes when appropriate:
- User needs text in the image -> suggest Pro
- User is iterating rapidly -> stick with Flash (default)
- User wants highest quality for final output -> suggest Pro
Editing Best Practices
When using edit_image or continue_editing:
- Be specific about what to change: "Make the sky more orange" not "improve the colors"
- Reference specific areas: "Add a tree in the bottom-left corner"
- For style transfer, use reference images via the
referenceImagesparameter - Each edit creates a new file -- the original is always preserved
Constraints
- Prompts over 10,000 characters will be rejected
- Image files must be under 20MB
- Supported input formats: JPEG, PNG, WebP, GIF
- Output is always PNG
- File paths must resolve within
$HOMEor$TMPDIR(security constraint) - Images are saved to
~/nanobanana-images/
Iterative Workflow
The most effective image workflow is:
- Generate a base image with a detailed prompt
- Use
continue_editingfor incremental refinements - Each iteration should address ONE specific change
- If the result diverges too far, start fresh with
generate_image