
nano-banana
Generate images from text prompts using Google Nano Banana (Gemini image generation) via eng0 data API. Use when user asks to generate, create, or make images, pictures, illustrations, or artwork from descriptions. Triggers include "generate image", "create image", "make a picture", "draw", "illustrate", "image of", "picture of", "nano banana". Do NOT use for image editing or manipulation of existing images.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/rebyteai-template/rebyte-skills --skill nano-banana- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads nano-banana automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Nano Banana - Image Generation API
Generate images from text prompts using Google Nano Banana (Gemini's native image generation).
Base URL
https://api.eng0.ai/api/data
Models
| Model | ID | Best For | Max Resolution |
|---|---|---|---|
| Flash | flash | Fast generation, high-volume tasks | 1024px |
| Pro | pro | Professional quality, complex prompts | 4K |
Generate Image
Create an image from a text description.
curl -X POST https://api.eng0.ai/api/data/images/generate \
-H "Content-Type: application/json" \
-d '{
"prompt": "A futuristic cityscape at sunset with flying cars",
"model": "flash",
"aspectRatio": "16:9"
}'
Parameters:
| Name | Type | Required | Default | Description |
|---|---|---|---|---|
prompt | string | Yes | - | Text description of the image to generate |
model | string | No | flash | flash (fast, 1024px) or pro (high-quality, up to 4K) |
aspectRatio | string | No | 1:1 | Output aspect ratio |
imageSize | string | No | 1K | 1K, 2K, or 4K (4K only with pro model) |
Aspect Ratios:
| Ratio | Use Case |
|---|---|
1:1 | Square (social media, icons) |
16:9 | Landscape (presentations, banners) |
9:16 | Portrait (mobile, stories) |
4:3 | Standard landscape |
3:4 | Standard portrait |
21:9 | Ultra-wide (cinematic) |
2:3, 3:2, 4:5, 5:4 | Various formats |
Response:
{
"image": {
"base64": "iVBORw0KGgoAAAANSUhEUgAA...",
"mimeType": "image/png",
"dataUrl": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA..."
},
"description": "A vibrant futuristic cityscape..."
}
Response Fields:
| Field | Description |
|---|---|
image.base64 | Base64-encoded image data |
image.mimeType | Image MIME type (typically image/png) |
image.dataUrl | Ready-to-use data URL for HTML/CSS |
description | Model's description of the generated image |
Common Workflows
Generate a Simple Image
curl -X POST https://api.eng0.ai/api/data/images/generate \
-H "Content-Type: application/json" \
-d '{
"prompt": "A cute robot holding a cup of coffee"
}'
Generate High-Quality 4K Image
curl -X POST https://api.eng0.ai/api/data/images/generate \
-H "Content-Type: application/json" \
-d '{
"prompt": "Professional product photo of a sleek smartphone on marble surface",
"model": "pro",
"imageSize": "4K",
"aspectRatio": "4:3"
}'
Generate Social Media Banner
curl -X POST https://api.eng0.ai/api/data/images/generate \
-H "Content-Type: application/json" \
-d '{
"prompt": "Abstract gradient background with geometric shapes in blue and purple",
"aspectRatio": "16:9"
}'
Generate Mobile Story Image
curl -X POST https://api.eng0.ai/api/data/images/generate \
-H "Content-Type: application/json" \
-d '{
"prompt": "Cozy coffee shop interior with warm lighting",
"aspectRatio": "9:16"
}'
Using with Python
import requests
import base64
from pathlib import Path
BASE_URL = "https://api.eng0.ai/api/data"
def generate_image(
prompt: str,
model: str = "flash",
aspect_ratio: str = "1:1",
image_size: str = "1K"
) -> dict:
"""Generate an image from a text prompt."""
response = requests.post(
f"{BASE_URL}/images/generate",
json={
"prompt": prompt,
"model": model,
"aspectRatio": aspect_ratio,
"imageSize": image_size
}
)
return response.json()
def save_image(result: dict, filepath: str) -> None:
"""Save generated image to a file."""
if "image" in result:
image_data = base64.b64decode(result["image"]["base64"])
Path(filepath).write_bytes(image_data)
print(f"Saved to {filepath}")
else:
print(f"Error: {result.get('error', 'Unknown error')}")
# Example: Generate and save an image
result = generate_image(
prompt="A serene mountain landscape at dawn with mist in the valley",
aspect_ratio="16:9"
)
save_image(result, "landscape.png")
# Example: Generate high-quality product image
result = generate_image(
prompt="Minimalist tech gadget on white background, studio lighting",
model="pro",
image_size="4K"
)
save_image(result, "product.png")
Using with Node.js
const fs = require('fs');
const BASE_URL = 'https://api.eng0.ai/api/data';
async function generateImage(prompt, options = {}) {
const response = await fetch(`${BASE_URL}/images/generate`, {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({
prompt,
model: options.model || 'flash',
aspectRatio: options.aspectRatio || '1:1',
imageSize: options.imageSize || '1K'
})
});
return response.json();
}
function saveImage(result, filepath) {
if (result.image) {
const buffer = Buffer.from(result.image.base64, 'base64');
fs.writeFileSync(filepath, buffer);
console.log(`Saved to ${filepath}`);
} else {
console.error('Error:', result.error || 'Unknown error');
}
}
// Example usage
(async () => {
const result = await generateImage(
'A neon-lit cyberpunk street scene at night',
{ aspectRatio: '21:9' }
);
saveImage(result, 'cyberpunk.png');
})();
Prompt Tips
Be Specific
Bad: "A dog"
Good: "A golden retriever puppy playing in autumn leaves, warm sunlight"
Include Style
"Oil painting style portrait of a woman in Renaissance clothing"
"Minimalist vector illustration of a mountain range"
"Photorealistic close-up of a dewdrop on a leaf"
Specify Lighting
"... with dramatic side lighting"
"... golden hour sunlight"
"... soft diffused studio lighting"
Add Context
"... in the style of Studio Ghibli"
"... professional product photography"
"... vintage 1970s film aesthetic"
Model Comparison
| Feature | Flash | Pro |
|---|---|---|
| Speed | Fast | Slower |
| Max Resolution | 1024px | 4K |
| Complex Prompts | Good | Excellent |
| Text Rendering | Good | Sharp |
| Best For | Quick iterations, previews | Final assets, print |
When to use Flash:
- Rapid prototyping
- Social media content
- High-volume generation
- When speed matters
When to use Pro:
- Professional/commercial use
- Print materials
- Complex compositions
- When quality matters
Error Handling
No Image Generated:
{
"error": "No image generated",
"message": "SAFETY"
}
This occurs when the prompt triggers content safety filters.
Invalid Parameters:
{
"error": "Invalid parameters",
"message": "Missing required parameter: prompt"
}
Important Notes
- All generated images include invisible SynthID watermarking
- Images are generated server-side; base64 responses can be large
- The
promodel takes longer but produces higher quality - 4K resolution is only available with the
promodel - Content safety filters may block certain prompts
Combining with Other Skills
This skill provides image generation. Combine with:
- deep-research - Generate illustrations for research reports
- market-data - Create visualizations of financial data
Example workflow:
- Research a topic (deep-research)
- Generate relevant illustrations (this skill)
- Compile into a report
FAQ
What does the nano-banana skill do?
Generate images from text prompts using Google Nano Banana (Gemini image generation) via eng0 data API. Use when user asks to generate, create, or make images, pictures, illustrations, or artwork from descriptions. Triggers include "generate image", "create image", "make a picture", "draw", "illustrate", "image of", "picture of", "nano banana". Do NOT use for image editing or manipulation of existing images.
How do I install nano-banana?
Run npx skills add https://github.com/rebyteai-template/rebyte-skills --skill nano-banana in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/nano-banana/ (personal) or .claude/skills/nano-banana/ (project).
Which AI agents can use nano-banana?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is nano-banana safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains nano-banana?
It is published by rebyteai-template in rebyteai-template/rebyte-skills. It has 69 recorded installs.
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