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Claude-skill-registry gemini-genai

Google python-genai SDK for Gemini 3 Flash, Gemini 3 Pro, and Gemini models. Use when building with Google's Gemini API, google-genai, implementing thinking/reasoning, structured outputs, function calling, image generation, or multimodal. Triggers on "gemini", "google ai", "genai".

install
source · Clone the upstream repo
git clone https://github.com/majiayu000/claude-skill-registry
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/gemini-genai" ~/.claude/skills/majiayu000-claude-skill-registry-gemini-genai && rm -rf "$T"
manifest: skills/data/gemini-genai/SKILL.md
tags
#ai-generation#reasoning#api-integration#multimodal#structured-output
source content

Google Gemini python-genai SDK

Model IDs

ModelID
Gemini 3 Flash
gemini-3-flash-preview
Gemini 3 Pro
gemini-3-pro-preview
Gemini 3 Pro Image
gemini-3-pro-image-preview
Gemini 2.5 Flash
gemini-2.5-flash
Gemini 2.5 Pro
gemini-2.5-pro

Gemini 3 Flash Capabilities

Token Limits: 1,048,576 input / 65,536 output (confirmed 1M context)

Inputs: Text, Image, Video, Audio, PDF

Supported: Batch API, Caching, Code execution, File search, Function calling, Search grounding, Structured outputs, Thinking, URL context

Not Supported: Audio generation, Image generation (use gemini-3-pro-image-preview), Live API, Grounding with Google Maps

Knowledge cutoff: January 2025

Critical: Temperature for Gemini 3

Keep 

temperature=1.0
- Lower values cause response looping.

Thinking Levels (Gemini 3)

config=types.GenerateContentConfig(
    thinking_config=types.ThinkingConfig(thinking_level="high")  # minimal|low|medium|high
)

  • minimal
    : No thinking, lowest latency (Flash only)
  • high
    : Maximum reasoning (default)

Async Pattern

async with genai.Client().aio as client:
    response = await client.models.generate_content(...)

# Async chat
chat = client.aio.chats.create(model="gemini-3-flash-preview")

Media Resolution (v1alpha required)

client = genai.Client(http_options=types.HttpOptions(api_version='v1alpha'))

types.Part(
    inline_data=types.Blob(mime_type="image/jpeg", data=image_bytes),
    media_resolution={"level": "media_resolution_high"}  # low|medium|high|ultra_high
)

Tokens: low=280, medium=560, high=1120

Built-in Tools (Gemini 3)

config=types.GenerateContentConfig(
    tools=[{"google_search": {}}],    # Web search
    # tools=[{"url_context": {}}],    # Fetch URL content
    # tools=[{"code_execution": {}}], # Run code
)

Enum Response

from enum import Enum

class Category(Enum):
    A = "A"
    B = "B"

config={
    "response_mime_type": "text/x.enum",
    "response_schema": Category,
}

Image Generation

response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents="A cyberpunk city",
    config=types.GenerateContentConfig(
        response_modalities=["IMAGE"],
        image_config=types.ImageConfig(aspect_ratio="16:9", image_size="4K"),
    ),
)
image = response.parts[0].as_image()
image.save("out.png")

Grounded (with search): Add 

tools=[{"google_search": {}}]

Response Helpers

response.text           # Text content
response.parsed         # Auto-parsed JSON (when using response_json_schema)
response.function_calls # List of function calls (cleaner than candidates drilling)

Pricing (Gemini 3 Flash)

Input: $0.50/1M | Output: $3.00/1M | Context caching: up to 90% reduction

Resources