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.claude-plugin/plugin.json Normal file
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{
"name": "nanobanana-skill",
"description": "Generate or edit images using Google Gemini API via nanobanana",
"version": "1.0.0",
"author": {
"name": "Pengfei Ni",
"url": "https://github.com/feiskyer/claude-code-settings"
},
"skills": [
"./skills"
]
}

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README.md Normal file
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# nanobanana-skill
Generate or edit images using Google Gemini API via nanobanana

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plugin.lock.json Normal file
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skills/nanobanana-skill/SKILL.md Normal file
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---
name: nanobanana-skill
description: Generate or edit images using Google Gemini API via nanobanana. Use when the user asks to create, generate, edit images with nanobanana, or mentions image generation/editing tasks.
allowed-tools: Bash
---
# Nanobanana Image Generation Skill
Generate or edit images using Google Gemini API through the nanobanana tool.
## Requirements
1. **GEMINI_API_KEY**: Must be configured in `~/.nanobanana.env` or `export GEMINI_API_KEY=<your-api-key>`
2. **Python3 with depedent packages installed**: google-genai, Pillow, python-dotenv. They could be installed via `python3 -m pip install -r ${CLAUDE_PLUGIN_ROOT}/skills/nanobanana-skill/requirements.txt` if not installed yet.
3. **Executable**: `${CLAUDE_PLUGIN_ROOT}/skills/nanobanana-skill/nanobanana.py`
## Instructions
### For image generation
1. Ask the user for:
- What they want to create (the prompt)
- Desired aspect ratio/size (optional, defaults to 9:16 portrait)
- Output filename (optional, auto-generates UUID if not specified)
- Model preference (optional, defaults to gemini-3-pro-image-preview)
- Resolution (optional, defaults to 1K)
2. Run the nanobanana script with appropriate parameters:
```bash
python3 ${CLAUDE_PLUGIN_ROOT}/skills/nanobanana-skill/nanobanana.py --prompt "description of image" --output "filename.png"
```
3. Show the user the saved image path when complete
### For image editing
1. Ask the user for:
- Input image file(s) to edit
- What changes they want (the prompt)
- Output filename (optional)
2. Run with input images:
```bash
python3 ${CLAUDE_PLUGIN_ROOT}/skills/nanobanana-skill/nanobanana.py --prompt "editing instructions" --input image1.png image2.png --output "edited.png"
```
## Available Options
### Aspect Ratios (--size)
- `1024x1024` (1:1) - Square
- `832x1248` (2:3) - Portrait
- `1248x832` (3:2) - Landscape
- `864x1184` (3:4) - Portrait
- `1184x864` (4:3) - Landscape
- `896x1152` (4:5) - Portrait
- `1152x896` (5:4) - Landscape
- `768x1344` (9:16) - Portrait (default)
- `1344x768` (16:9) - Landscape
- `1536x672` (21:9) - Ultra-wide
### Models (--model)
- `gemini-3-pro-image-preview` (default) - Higher quality
- `gemini-2.5-flash-image` - Faster generation
### Resolution (--resolution)
- `1K` (default)
- `2K`
- `4K`
## Examples
### Generate a simple image
```bash
python3 ${CLAUDE_PLUGIN_ROOT}/skills/nanobanana-skill/nanobanana.py --prompt "A serene mountain landscape at sunset with a lake"
```
### Generate with specific size and output
```bash
python3 ${CLAUDE_PLUGIN_ROOT}/skills/nanobanana-skill/nanobanana.py \
--prompt "Modern minimalist logo for a tech startup" \
--size 1024x1024 \
--output "logo.png"
```
### Generate landscape image with high resolution
```bash
python3 ${CLAUDE_PLUGIN_ROOT}/skills/nanobanana-skill/nanobanana.py \
--prompt "Futuristic cityscape with flying cars" \
--size 1344x768 \
--resolution 2K \
--output "cityscape.png"
```
### Edit existing images
```bash
python3 ${CLAUDE_PLUGIN_ROOT}/skills/nanobanana-skill/nanobanana.py \
--prompt "Add a rainbow in the sky" \
--input photo.png \
--output "photo-with-rainbow.png"
```
### Use faster model
```bash
python3 ${CLAUDE_PLUGIN_ROOT}/skills/nanobanana-skill/nanobanana.py \
--prompt "Quick sketch of a cat" \
--model gemini-2.5-flash-image \
--output "cat-sketch.png"
```
## Error Handling
If the script fails:
- Check that `GEMINI_API_KEY` is exported or set in ~/.nanobanana.env
- Verify input image files exist and are readable
- Ensure the output directory is writable
- If no image is generated, try making the prompt more specific about wanting an image
## Best Practices
1. Be descriptive in prompts - include style, mood, colors, composition
2. For logos/graphics, use square aspect ratio (1024x1024)
3. For social media posts, use 9:16 for stories or 1:1 for posts
4. For wallpapers, use 16:9 or 21:9
5. Start with 1K resolution for testing, upgrade to 2K/4K for final output
6. Use gemini-3-pro-image-preview for best quality, gemini-2.5-flash-image for speed

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skills/nanobanana-skill/nanobanana.py Executable file
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#!/usr/bin/env python3
# Generate or edit images using Google Gemini API
import os
import argparse
import uuid
from pathlib import Path
from dotenv import load_dotenv
from google import genai
from google.genai import types
from PIL import Image
from io import BytesIO
# Load environment variables
load_dotenv(os.path.expanduser("~") + "/.nanobanana.env")
# Google API configuration from environment variables
api_key = os.getenv("GEMINI_API_KEY") or ""
if not api_key:
raise ValueError(
"Missing GEMINI_API_KEY environment variable. Please check your .env file."
)
# Initialize Gemini client
client = genai.Client(api_key=api_key)
# Aspect ratio to resolution mapping
ASPECT_RATIO_MAP = {
"1024x1024": "1:1", # 1:1
"832x1248": "2:3", # 2:3
"1248x832": "3:2", # 3:2
"864x1184": "3:4", # 3:4
"1184x864": "4:3", # 4:3
"896x1152": "4:5", # 4:5
"1152x896": "5:4", # 5:4
"768x1344": "9:16", # 9:16
"1344x768": "16:9", # 16:9
"1536x672": "21:9", # 21:9
}
def main():
# Parse command-line arguments
parser = argparse.ArgumentParser(
description="Generate or edit images using Google Gemini API"
)
parser.add_argument(
"--prompt",
type=str,
required=True,
help="Prompt for image generation or editing",
)
parser.add_argument(
"--output",
type=str,
default=f"nanobanana-{uuid.uuid4()}.png",
help="Output image filename (default: nanobanana-<UUID>.png)",
)
parser.add_argument(
"--input", type=str, nargs="*", help="Input image files for editing (optional)"
)
parser.add_argument(
"--size",
type=str,
default="768x1344",
choices=list(ASPECT_RATIO_MAP.keys()),
help="Size/aspect ratio of the generated image (default: 768x1344 / 9:16)",
)
parser.add_argument(
"--model",
type=str,
default="gemini-3-pro-image-preview",
choices=["gemini-3-pro-image-preview", "gemini-2.5-flash-image"],
help="Model to use for image generation (default: gemini-3-pro-image-preview)",
)
parser.add_argument(
"--resolution",
type=str,
default="1K",
choices=["1K", "2K", "4K"],
help="Resolution of the generated image (default: 1K)",
)
args = parser.parse_args()
# Get aspect ratio from size
aspect_ratio = ASPECT_RATIO_MAP.get(args.size, "16:9")
# Build contents list for the API call
contents = []
# Check if input images are provided
if args.input and len(args.input) > 0:
# Use images.generate_content() with images for editing
print(f"Editing images with prompt: {args.prompt}")
print(f"Input images: {args.input}")
print(f"Aspect ratio: {aspect_ratio} ({args.size})")
# Add prompt first
contents.append(args.prompt)
# Add all input images
for img_path in args.input:
image = Image.open(img_path)
contents.append(image)
else:
print(f"Generating image (size: {args.size}) with prompt: {args.prompt}")
contents.append(args.prompt)
# Generate or edit image with config
response = client.models.generate_content(
model=args.model,
contents=contents,
config=types.GenerateContentConfig(
response_modalities=['TEXT', 'IMAGE'],
tools=[types.Tool(google_search=types.GoogleSearch())],
image_config=types.ImageConfig(
aspect_ratio=aspect_ratio,
image_size=args.resolution,
),
),
)
if (response.candidates is None
or len(response.candidates) == 0
or response.candidates[0].content is None
or response.candidates[0].content.parts is None):
raise ValueError("No data received from the API.")
# Extract image from response
image_saved = False
for part in response.candidates[0].content.parts:
if part.text is not None:
print(f"{part.text}", end="")
elif part.inline_data is not None and part.inline_data.data is not None:
image = Image.open(BytesIO(part.inline_data.data))
image.save(args.output)
image_saved = True
print(f"\n\nImage saved to: {args.output}")
if not image_saved:
print(f"\n\nWarning: No image data found in the API response. This usually means the model returned only text. Please try again with a different prompt to make image generation more clear.")
if __name__ == "__main__":
main()

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skills/nanobanana-skill/requirements.txt Normal file
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python-dotenv
httpx[socks]
google-genai
Pillow