image-generation_skill

Overview

This skill enables image generation and editing with Gemini using the Python SDK. It defaults to the gemini-3-pro-image-preview model and supports text-to-image, image-to-image edits, multi-reference composition, and Google Search grounding. Use it to create or modify images programmatically and to handle returned image parts reliably.

How this skill works

The skill detects the task type (text-to-image, edit, or multi-reference) and issues requests to Gemini via the Python SDK. It sets the appropriate model and response modalities (use response_modalities=["IMAGE"] for image-only outputs), waits for the response (generation can take ~30 seconds), and saves outputs using part.as_image(). If no images are returned, the skill surfaces a clear error and troubleshooting hints.

When to use it

• Generate new images from text prompts (text-to-image).
• Edit existing images or run image-to-image transformations.
• Compose scenes from multiple reference images (multi-reference composition).
• Ground image generation with Google Search references for accurate visual context.
• Automate image creation in scripts or agent workflows using the Python SDK.

Best practices

• Default to gemini-3-pro-image-preview; use gemini-2.5-flash-image as a faster/cheaper alternative when quality trade-offs are acceptable.
• Ensure GEMINI_API_KEY is set in environment or .env before running requests.
• Set response_modalities=["IMAGE"] when you only need images to reduce noise in responses.
• Allow 30–60 seconds for generation; increase shell/tool timeouts to 60–120 seconds to avoid premature failures.
• Always call part.as_image() for returned image parts and handle the no-image case with clear error messages and troubleshooting tips.

Example use cases

• Create marketing visuals from text prompts for social media campaigns.
• Edit a product photo to change background, color, or add branding.
• Compose a scene using multiple reference images for a concept board.
• Quickly prototype UI assets or character concepts from text descriptions.
• Automate batch image generation in a content pipeline using the Python SDK.