Generate and edit images using Gemini's Nano Banana Pro model (gemini-3-pro-image-preview). Use this skill when the user asks you to generate images, create visuals, edit photos, create logos, generate product mockups, or perform any image generation/editing task.
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
Generate and edit images using Gemini's Nano Banana Pro model (gemini-3-pro-image-preview). Use this skill when the user asks you to generate images, create visuals, edit photos, create logos, generate product mockups, or perform any image generation/editing task.
Use when this workflow matches the user request: Generate and edit images using Gemini's Nano Banana Pro model (gemini-3-pro-image-preview). Use this skill when the user asks you to generate images, create visuals, edit photos, create logos, generate product mockups, or perform any image generation/editing task.
Export the key in your shell profile (~/.zshrc, ~/.bashrc, etc.):
export GEMINI_API_KEY="your_api_key_here"
Restart your terminal or run source ~/.zshrc (or ~/.bashrc)
The skill will not work without this configuration.
Pre-flight Check
Before making any API call, verify the key is set:
if [ -z "$GEMINI_API_KEY" ]; thenecho"ERROR: GEMINI_API_KEY is not set. Please export it in your shell profile."exit 1
fi
If the key is missing, stop and tell the user to set it using the instructions above.
Configuration
Model: gemini-3-pro-image-preview
API Key: Read from the GEMINI_API_KEY environment variable
Iterating on User-Provided Images
When the user provides a path to an image they want to edit or iterate on, use this workflow:
Step 1: Read and encode the image to base64
# Get the image path from user
IMG_PATH="/path/to/user/image.png"# Detect mime type
[[ == *.png ]];
MIME_TYPE=
[[ == *.jpg ]] || [[ == *.jpeg ]];
MIME_TYPE=
[[ == *.webp ]];
MIME_TYPE=
MIME_TYPE=
[[ == ]];
IMG_BASE64=$( -i )
IMG_BASE64=$( -w0 )
if
"$IMG_PATH"
then
"image/png"
elif
"$IMG_PATH"
"$IMG_PATH"
then
"image/jpeg"
elif
"$IMG_PATH"
then
"image/webp"
else
"image/png"
fi
# Encode to base64 (works on both macOS and Linux)
if
"$(uname)"
"Darwin"
then
base64
"$IMG_PATH"
else
base64
"$IMG_PATH"
fi
Step 2: Send image with edit prompt (File-Based Approach)
IMPORTANT: Always use a file-based approach for the request body. Base64-encoded images are too large for command-line arguments and will cause "argument list too long" errors.
# User's edit request
EDIT_PROMPT="Add a santa hat to the person in this image"# Write request to a JSON file (avoids command line length limits)cat > /tmp/gemini_request.json << JSONEOF
{
"contents": [{
"parts": [
{"text": "$EDIT_PROMPT"},
{
"inline_data": {
"mime_type": "$MIME_TYPE",
"data": "$IMG_BASE64"
}
}
]
}],
"generationConfig": {
"responseModalities": ["TEXT", "IMAGE"]
}
}
JSONEOF# Call the API using the file
curl -s -X POST \
"https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type: application/json" \
-d @/tmp/gemini_request.json > /tmp/gemini_response.json
Step 3: Extract and save the edited image
# Extract image from response and save
python3 -c "
import json
import base64
with open('/tmp/gemini_response.json') as f:
data = json.load(f)
for part in data['candidates'][0]['content']['parts']:
if 'inlineData' in part:
img_data = part['inlineData']['data']
mime = part['inlineData']['mimeType']
ext = 'png' if 'png' in mime else 'jpg'
with open('edited_image.' + ext, 'wb') as out:
out.write(base64.b64decode(img_data))
print(f'Saved: edited_image.{ext}')
elif 'text' in part:
print(part['text'])
"
Complete Example (File-Based)
For iterating on images, always use file-based requests:
# Variables
IMG_PATH="/path/to/image.png"
EDIT_PROMPT="Make the background a sunset beach"
OUTPUT_PATH="edited_output.png"# Detect mime type and encode
MIME_TYPE=$([[ "$IMG_PATH" == *.png ]] && echo"image/png" || echo"image/jpeg")
IMG_BASE64=$(base64 -i "$IMG_PATH" 2>/dev/null || base64 -w0 "$IMG_PATH")
# Write request to file (required - base64 images are too large for command line)cat > /tmp/gemini_request.json << JSONEOF
{
"contents": [{
"parts": [
{"text": "$EDIT_PROMPT"},
{"inline_data": {"mime_type": "$MIME_TYPE", "data": "$IMG_BASE64"}}
]
}],
"generationConfig": {
"responseModalities": ["TEXT", "IMAGE"]
}
}
JSONEOF# Call API and extract image
curl -s -X POST \
"https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type: application/json" \
-d @/tmp/gemini_request.json > /tmp/gemini_response.json
# Save the output image
python3 -c "
import json, base64
with open('/tmp/gemini_response.json') as f:
data = json.load(f)
for part in data.get('candidates', [{}])[0].get('content', {}).get('parts', []):
if 'inlineData' in part:
with open('$OUTPUT_PATH', 'wb') as f:
f.write(base64.b64decode(part['inlineData']['data']))
print('Saved: $OUTPUT_PATH')
"
Multi-Image Input (Combine/Compose)
To combine elements from multiple images (also uses file-based approach):
IMG1_PATH="/path/to/image1.png"
IMG2_PATH="/path/to/image2.png"
PROMPT="Put the dress from the first image on the person in the second image"
IMG1_BASE64=$(base64 -i "$IMG1_PATH" 2>/dev/null || base64 -w0 "$IMG1_PATH")
IMG2_BASE64=$(base64 -i "$IMG2_PATH" 2>/dev/null || base64 -w0 "$IMG2_PATH")
# Write request to filecat > /tmp/gemini_request.json << JSONEOF
{
"contents": [{
"parts": [
{"text": "$PROMPT"},
{"inline_data": {"mime_type": "image/png", "data": "$IMG1_BASE64"}},
{"inline_data": {"mime_type": "image/png", "data": "$IMG2_BASE64"}}
]
}],
"generationConfig": {"responseModalities": ["TEXT", "IMAGE"]}
}
JSONEOF
curl -s -X POST \
"https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type: application/json" \
-d @/tmp/gemini_request.json > /tmp/gemini_response.json
Capabilities
Text-to-Image Generation
Generate high-quality images from text descriptions
Support for photorealistic, stylized, and artistic outputs
Accurate text rendering in images (logos, infographics, diagrams)
Image Editing
Add or remove elements from images
Inpainting with semantic masking (edit specific parts)
Style transfer (apply artistic styles to photos)
Multi-image composition (combine elements from multiple images)
from google import genai
from google.genai import types
from PIL import Image
client = genai.Client()
input_image = Image.open('input.png')
prompt = "Add a wizard hat to the cat in this image"
response = client.models.generate_content(
model="gemini-3-pro-image-preview",
contents=[prompt, input_image],
config=types.GenerateContentConfig(
response_modalities=['TEXT', 'IMAGE']
)
)
for part in response.parts:
if part.inline_data isnotNone:
image = part.as_image()
image.save("edited_image.png")
Multi-Image Composition
from google import genai
from google.genai import types
from PIL import Image
client = genai.Client()
image1 = Image.open('dress.png')
image2 = Image.open('model.png')
prompt = "Put the dress from the first image on the model from the second image"
response = client.models.generate_content(
model="gemini-3-pro-image-preview",
contents=[image1, image2, prompt],
config=types.GenerateContentConfig(
response_modalities=['TEXT', 'IMAGE'],
image_config=types.ImageConfig(
aspect_ratio="3:4",
image_size="2K"
)
)
)
With Google Search Grounding
from google import genai
from google.genai import types
client = genai.Client()
response = client.models.generate_content(
model="gemini-3-pro-image-preview",
contents="Visualize the current weather forecast for San Francisco",
config=types.GenerateContentConfig(
response_modalities=['TEXT', 'IMAGE'],
image_config=types.ImageConfig(aspect_ratio="16:9"),
tools=[{"google_search": {}}]
)
)
Prompting Best Practices
1. Be Descriptive, Not Keyword-Based
Instead of: cat, wizard hat, cute
Write: A fluffy orange cat wearing a small knitted wizard hat, sitting on a wooden floor with soft natural lighting from a window
Artistic styles: "in the style of Van Gogh", "minimalist illustration", "photorealistic"
Mood: "warm and cozy atmosphere", "dramatic noir lighting"
3. For Text in Images
Be explicit about:
The exact text to render
Font style (descriptively): "clean, bold, sans-serif font"
Placement and size
4. For Editing
Describe what to change and what to preserve
Use "keep everything else unchanged"
Reference specific elements clearly
5. For Product/Commercial Images
Mention:
Lighting setup: "three-point softbox lighting"
Background: "clean white studio background"
Camera angle: "slightly elevated 45-degree shot"
Resolution and Aspect Ratio Reference
Aspect Ratio
1K Resolution
2K Resolution
4K Resolution
1:1
1024x1024
2048x2048
4096x4096
16:9
1376x768
2752x1536
5504x3072
9:16
768x1376
1536x2752
3072x5504
3:2
1264x848
2528x1696
5056x3392
2:3
848x1264
1696x2528
3392x5056
Common Use Cases
Logo Creation
Create a modern, minimalist logo for a coffee shop called 'The Daily Grind'.
The text should be in a clean, bold, sans-serif font.
Black and white color scheme. Put the logo in a circle.
Product Photography
A high-resolution, studio-lit product photograph of a minimalist ceramic
coffee mug in matte black on a polished concrete surface. Three-point
softbox lighting with soft, diffused highlights. Slightly elevated
45-degree camera angle. Sharp focus on steam rising from the coffee.
Style Transfer
Transform this photograph of a city street at night into Vincent van Gogh's
'Starry Night' style. Preserve the composition but render with swirling,
impasto brushstrokes and deep blues with bright yellows.
Infographic
Create a vibrant infographic explaining photosynthesis as a recipe.
Show "ingredients" (sunlight, water, CO2) and "finished dish" (sugar/energy).
Style like a colorful kids' cookbook, suitable for 4th graders.
Error Handling
Common issues:
No image returned: Check that response_modalities includes 'IMAGE'
Safety filters: Some prompts may be blocked; try rephrasing
Rate limits: Implement exponential backoff for retries
Large images: For 4K, ensure sufficient timeout settings
Dependencies
To use the Python SDK:
pip install google-genai pillow
For JavaScript:
npm install @google/genai
Important Notes
All generated images include a SynthID watermark
The model uses a "thinking" process for complex prompts
For best text rendering, generate text first, then request image with that text
Images are not stored by the API - save outputs locally
Limitations
Requires the upstream tool, account, API key, or local setup when the workflow names one.
Does not authorize destructive, production, paid, or external-message actions without explicit user approval.
Validate generated artifacts or recommendations against the user's real sources before treating them as final.
