Gemini Image Skill

Gemini Image Skill

Invoke Google Gemini models for image generation, image understanding, and visual analysis using the Python google-genai SDK.

Available Models

Configuration

API Key: ${GEMINI_API_KEY}

Usage

Image Generation

python -c "
from google import genai
from google.genai import types

client = genai.Client(api_key=os.environ.get("GEMINI_API_KEY"))

response = client.models.generate_content(
    model='gemini-3-pro-image-preview',  # Returns JPEG | Use gemini-2.5-flash-image for PNG
    contents='Generate an image of a sunset over mountains',
    config=types.GenerateContentConfig(
        response_modalities=['IMAGE', 'TEXT']
    )
)

# Map mime types to file extensions
mime_to_ext = {'image/​png': '.png', 'image/​jpeg': '.jpg', 'image/​gif': '.gif', 'image/​webp': '.webp'}

# Save generated image
if response.candidates and response.candidates[0].content:
    for part in response.candidates[0].content.parts:
        if hasattr(part, 'inline_data') and part.inline_data:
            ext = mime_to_ext.get(part.inline_data.mime_type, '.png')
            filename = f'output{ext}'
            # Data is already raw bytes - no base64 decode needed
            with open(filename, 'wb') as f:
                f.write(part.inline_data.data)
            print(f'Image saved to {filename} ({part.inline_data.mime_type})')
        elif hasattr(part, 'text'):
            print(part.text)
"

Image Understanding (Analyze Image from File)

python -c "
from google import genai
from google.genai import types
import base64

client = genai.Client(api_key=os.environ.get("GEMINI_API_KEY"))

# Read image file - must be base64 encoded for INPUT
with open('IMAGE_PATH', 'rb') as f:
    image_data = base64.b64encode(f.read()).decode('utf-8')

response = client.models.generate_content(
    model='gemini-3-pro-preview',
    contents=[
        types.Content(parts=[
            types.Part(text='Describe this image in detail'),
            types.Part(inline_data=types.Blob(mime_type='image/​png', data=image_data))
        ])
    ]
)
print(response.text)
"

Image Understanding (From URL)

python -c "
from google import genai
from google.genai import types
import urllib.request
import base64

client = genai.Client(api_key=os.environ.get("GEMINI_API_KEY"))

# Fetch image from URL - must be base64 encoded for INPUT
url = 'IMAGE_URL_HERE'
with urllib.request.urlopen(url) as response:
    image_data = base64.b64encode(response.read()).decode('utf-8')

response = client.models.generate_content(
    model='gemini-3-pro-preview',
    contents=[
        types.Content(parts=[
            types.Part(text='What is in this image?'),
            types.Part(inline_data=types.Blob(mime_type='image/​jpeg', data=image_data))
        ])
    ]
)
print(response.text)
"

Workflow

When this skill is invoked:

1. Determine the task type:

   • Image Generation: User wants to create an image
   • Image Understanding: User wants to analyze an existing image
   • Image Editing: User wants to modify an image (generation with reference)

2. Select the appropriate model:

   • Image generation → gemini-3-pro-image-preview (JPEG) or gemini-2.5-flash-image (PNG)
   • Image analysis → gemini-3-pro-preview or gemini-2.5-flash

3. Prepare the input:

   • For generation: Text prompt describing desired image
   • For understanding: Load image file as base64

4. Execute and handle output:

   • Generation: Save binary image data to file
   • Understanding: Return text description

Example Invocations

Generate Product Image

python -c "
from google import genai
from google.genai import types

client = genai.Client(api_key=os.environ.get("GEMINI_API_KEY"))

response = client.models.generate_content(
    model='gemini-3-pro-image-preview',
    contents='Create a professional product photo of a sleek wireless headphone on a white background, studio lighting',
    config=types.GenerateContentConfig(
        response_modalities=['IMAGE', 'TEXT']
    )
)

mime_to_ext = {'image/​png': '.png', 'image/​jpeg': '.jpg', 'image/​gif': '.gif', 'image/​webp': '.webp'}

if response.candidates and response.candidates[0].content:
    for part in response.candidates[0].content.parts:
        if hasattr(part, 'inline_data') and part.inline_data:
            ext = mime_to_ext.get(part.inline_data.mime_type, '.png')
            with open(f'headphone{ext}', 'wb') as f:
                f.write(part.inline_data.data)
            print(f'Image saved to headphone{ext}')
"

Analyze Screenshot

python -c "
from google import genai
from google.genai import types
import base64

client = genai.Client(api_key=os.environ.get("GEMINI_API_KEY"))

with open('screenshot.png', 'rb') as f:
    image_data = base64.b64encode(f.read()).decode('utf-8')

response = client.models.generate_content(
    model='gemini-3-pro-preview',
    contents=[
        types.Content(parts=[
            types.Part(text='Analyze this UI screenshot. Identify any usability issues and suggest improvements.'),
            types.Part(inline_data=types.Blob(mime_type='image/​png', data=image_data))
        ])
    ]
)
print(response.text)
"

OCR / Extract Text from Image

python -c "
from google import genai
from google.genai import types
import base64

client = genai.Client(api_key=os.environ.get("GEMINI_API_KEY"))

with open('document.png', 'rb') as f:
    image_data = base64.b64encode(f.read()).decode('utf-8')

response = client.models.generate_content(
    model='gemini-3-pro-preview',
    contents=[
        types.Content(parts=[
            types.Part(text='Extract all text from this image. Preserve formatting where possible.'),
            types.Part(inline_data=types.Blob(mime_type='image/​png', data=image_data))
        ])
    ]
)
print(response.text)
"

Compare Two Images

python -c "
from google import genai
from google.genai import types
import base64

client = genai.Client(api_key=os.environ.get("GEMINI_API_KEY"))

with open('image1.png', 'rb') as f:
    img1_data = base64.b64encode(f.read()).decode('utf-8')
with open('image2.png', 'rb') as f:
    img2_data = base64.b64encode(f.read()).decode('utf-8')

response = client.models.generate_content(
    model='gemini-3-pro-preview',
    contents=[
        types.Content(parts=[
            types.Part(text='Compare these two images. What are the key differences?'),
            types.Part(inline_data=types.Blob(mime_type='image/​png', data=img1_data)),
            types.Part(inline_data=types.Blob(mime_type='image/​png', data=img2_data))
        ])
    ]
)
print(response.text)
"

Image Generation Parameters

When generating images, you can customize:

config=types.GenerateContentConfig(
    response_modalities=['IMAGE', 'TEXT'],  # Request both image and description
    temperature=1.0,  # Higher = more creative
    # Additional parameters may be model-specific
)

Supported Image Formats

Input (for understanding):

• PNG (image/​png)
• JPEG (image/​jpeg)
• GIF (image/​gif)
• WebP (image/​webp)

Output (from generation):

• PNG (default, image/​png)
• The API returns raw bytes in part.inline_data.data (NOT base64 encoded)
• Check part.inline_data.mime_type to determine the actual format returned

Error Handling

Common errors and solutions:

• Image too large: Resize image before sending (max varies by model)
• Unsupported format: Convert to PNG/​JPEG
• Generation blocked: Adjust prompt to comply with safety guidelines
• Rate limiting: Implement retry with exponential backoff

Notes

• Image generation requires response_modalities=['IMAGE', 'TEXT'] in config
• For best results with generation, be specific and descriptive in prompts
• Image understanding works with both local files and URLs
• Multiple images can be sent in a single request for comparison
• Gemini 3 Pro Image is NOT available via CLI - must use Python SDK

Tools to Use

• Bash: Execute Python commands
• Read: Load image files (binary mode)
• Write: Save generated images
• Glob: Find image files in directories