This skill should be used when generating and editing images using the Gemini API (Nano Banana Pro). It applies when creating images from text prompts, editing existing images, applying style transfers, generating logos with text, creating stickers, product mockups, or any image generation/manipulation task. Supports text-to-image, image editing, multi-turn refinement, and composition from multiple reference images.
Generate and edit images using Google's Gemini API (Nano Banana Pro). Use this for creating images from text prompts, editing existing images, style transfers, logos with text, stickers, or product mockups. It triggers when you request any image generation or manipulation task, supporting text-to-image, multi-turn refinement, and composition from multiple reference images.
/plugin marketplace add SiTaggart/claude-devbench/plugin install devbench@devbench-marketplaceThis skill inherits all available tools. When active, it can use any tool Claude has access to.
requirements.txtscripts/compose_images.pyscripts/edit_image.pyscripts/gemini_images.pyscripts/generate_image.pyscripts/multi_turn_chat.pyGenerate and edit images using Google's Gemini API. The environment variable GEMINI_API_KEY must be set.
| Model | Resolution | Best For |
|---|---|---|
gemini-3-pro-image-preview | 1K-4K | All image generation (default) |
Note: Always use this Pro model. Only use a different model if explicitly requested.
gemini-3-pro-image-preview1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9
1K (default), 2K, 4K
import os
from google import genai
from google.genai import types
client = genai.Client(api_key=os.environ["GEMINI_API_KEY"])
# Basic generation (1K, 1:1 - defaults)
response = client.models.generate_content(
model="gemini-3-pro-image-preview",
contents=["Your prompt here"],
config=types.GenerateContentConfig(
response_modalities=['TEXT', 'IMAGE'],
),
)
for part in response.parts:
if part.text:
print(part.text)
elif part.inline_data:
image = part.as_image()
image.save("output.png")
from google.genai import types
response = client.models.generate_content(
model="gemini-3-pro-image-preview",
contents=[prompt],
config=types.GenerateContentConfig(
response_modalities=['TEXT', 'IMAGE'],
image_config=types.ImageConfig(
aspect_ratio="16:9", # Wide format
image_size="2K" # Higher resolution
),
)
)
# 1K (default) - Fast, good for previews
image_config=types.ImageConfig(image_size="1K")
# 2K - Balanced quality/speed
image_config=types.ImageConfig(image_size="2K")
# 4K - Maximum quality, slower
image_config=types.ImageConfig(image_size="4K")
# Square (default)
image_config=types.ImageConfig(aspect_ratio="1:1")
# Landscape wide
image_config=types.ImageConfig(aspect_ratio="16:9")
# Ultra-wide panoramic
image_config=types.ImageConfig(aspect_ratio="21:9")
# Portrait
image_config=types.ImageConfig(aspect_ratio="9:16")
# Photo standard
image_config=types.ImageConfig(aspect_ratio="4:3")
Pass existing images with text prompts:
from PIL import Image
img = Image.open("input.png")
response = client.models.generate_content(
model="gemini-3-pro-image-preview",
contents=["Add a sunset to this scene", img],
config=types.GenerateContentConfig(
response_modalities=['TEXT', 'IMAGE'],
),
)
Use chat for iterative editing:
from google.genai import types
chat = client.chats.create(
model="gemini-3-pro-image-preview",
config=types.GenerateContentConfig(response_modalities=['TEXT', 'IMAGE'])
)
response = chat.send_message("Create a logo for 'Acme Corp'")
# Save first image...
response = chat.send_message("Make the text bolder and add a blue gradient")
# Save refined image...
Include camera details: lens type, lighting, angle, mood.
"A photorealistic close-up portrait, 85mm lens, soft golden hour light, shallow depth of field"
Specify style explicitly:
"A kawaii-style sticker of a happy red panda, bold outlines, cel-shading, white background"
Be explicit about font style and placement:
"Create a logo with text 'Daily Grind' in clean sans-serif, black and white, coffee bean motif"
Describe lighting setup and surface:
"Studio-lit product photo on polished concrete, three-point softbox setup, 45-degree angle"
Generate images based on real-time data:
response = client.models.generate_content(
model="gemini-3-pro-image-preview",
contents=["Visualize today's weather in Tokyo as an infographic"],
config=types.GenerateContentConfig(
response_modalities=['TEXT', 'IMAGE'],
tools=[{"google_search": {}}]
)
)
Combine elements from multiple sources:
response = client.models.generate_content(
model="gemini-3-pro-image-preview",
contents=[
"Create a group photo of these people in an office",
Image.open("person1.png"),
Image.open("person2.png"),
Image.open("person3.png"),
],
config=types.GenerateContentConfig(
response_modalities=['TEXT', 'IMAGE'],
),
)
CRITICAL: The Gemini API returns images in JPEG format by default. When saving, always use .jpg extension to avoid media type mismatches.
# CORRECT - Use .jpg extension (Gemini returns JPEG)
image.save("output.jpg")
# WRONG - Will cause "Image does not match media type" errors
image.save("output.png") # Creates JPEG with PNG extension!
If you specifically need PNG format:
from PIL import Image
# Generate with Gemini
for part in response.parts:
if part.inline_data:
img = part.as_image()
# Convert to PNG by saving with explicit format
img.save("output.png", format="PNG")
Check actual format vs extension with the file command:
file image.png
# If output shows "JPEG image data" - rename to .jpg!
.jpg extensionresponseModalities: ["IMAGE"]) won't work with Google Search groundingThis skill should be used when the user asks to "create a slash command", "add a command", "write a custom command", "define command arguments", "use command frontmatter", "organize commands", "create command with file references", "interactive command", "use AskUserQuestion in command", or needs guidance on slash command structure, YAML frontmatter fields, dynamic arguments, bash execution in commands, user interaction patterns, or command development best practices for Claude Code.
This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.
This skill should be used when the user asks to "create a hook", "add a PreToolUse/PostToolUse/Stop hook", "validate tool use", "implement prompt-based hooks", "use ${CLAUDE_PLUGIN_ROOT}", "set up event-driven automation", "block dangerous commands", or mentions hook events (PreToolUse, PostToolUse, Stop, SubagentStop, SessionStart, SessionEnd, UserPromptSubmit, PreCompact, Notification). Provides comprehensive guidance for creating and implementing Claude Code plugin hooks with focus on advanced prompt-based hooks API.