Agent skill

gemini-image

Invoke Google Gemini for image generation and understanding using the Python google-genai SDK. Supports gemini-3-pro-image-preview (generation + understanding), gemini-2.5-flash-image (fast generation), and vision models for analysis.

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill gemini-image-rdfitted-claude-code-setup --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 9 KB
Bundled scripts: none
Path: skills/ai-llm/gemini-image-rdfitted-claude-code-setup/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Gemini Image Skill Invoke Google Gemini models for image generation, image understanding, and visual analysis using the Python `google-genai` SDK. ## Available Models | Model ID | Description | Best For | Output Format | |----------|-------------|----------|---------------| | `gemini-3-pro-image-preview` | Best image generation + understanding | High-quality image gen, complex visual analysis | JPEG | | `gemini-2.5-flash-image` | Fast image generation | Quick image creation | PNG | | `gemini-3-pro-preview` | Multimodal understanding | Image analysis without generation | N/A | | `gemini-2.5-flash` | Fast vision | Quick image analysis | N/A | ## Configuration **API Key**: `${GEMINI_API_KEY}` ## Usage ### Image Generation ```bash 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':

What's inside
Steps it walks through
  1. Available Models
  2. Configuration
  3. Usage
  4. Image Generation
  5. Image Understanding (Analyze Image from File)
  6. Image Understanding (From URL)
  7. Workflow
  8. Example Invocations
  9. Generate Product Image
  10. Analyze Screenshot
  11. OCR / Extract Text from Image
  12. Compare Two Images
  13. Image Generation Parameters
  14. Supported Image Formats
Ships with 1 file
  • metadata.json
Commands it runs
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(
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:
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About this skill
What does the gemini-image skill do?

Invoke Google Gemini for image generation and understanding using the Python google-genai SDK. Supports gemini-3-pro-image-preview (generation + understanding), gemini-2.5-flash-image (fast generation), and vision models for analysis.

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill gemini-image-rdfitted-claude-code-setup --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going