Does it support Gemini Live?

While this skill covers standard API development, it recommends the specialized 'gemini-live-api-dev' skill for real-time WebSocket streaming and voice features.

Which Gemini models does this skill support?

This skill supports the latest generation of models including Gemini 3.5 Flash, Gemini 3.1 Pro/Flash, and Gemma 4, while explicitly deprecating legacy 1.5 and 2.0 versions.

Can this skill help with multimodal inputs?

Absolutely. It provides implementation patterns for processing text, images, audio, and video content using Gemini's native multimodal capabilities.

How does it handle API documentation?

The skill prioritizes fetching real-time, up-to-date information via MCP search tools if installed, or falls back to an indexed documentation fetcher to ensure accuracy.

Does it support the new Google GenAI SDKs?

Yes, it specifically enforces the use of the new unified SDKs: 'google-genai' for Python, '@google/genai' for JS, and the new Go and Java libraries.

Gemini API Development | Claude Code Skill

The Gemini API Development skill provides Claude with the specialized knowledge required to build, optimize, and maintain applications powered by Google's generative models. It enforces the use of current high-performance models like Gemini 3.5 Flash and Gemma 4 while ensuring developers utilize the latest unified SDKs (google-genai) across Python, JavaScript, Go, and Java. By providing patterns for multimodal processing, function calling, and structured outputs, this skill streamlines the transition from legacy implementations to modern AI architectures, backed by real-time documentation retrieval via MCP integration.

Características Principales

01Advanced function calling and structured output patterns

02Multimodal integration for text, image, audio, and video processing

03Support for Gemini 3.5, 3.1, and Gemma 4 models

043,794 GitHub stars

05Unified SDK implementation for Python, Node.js, Go, and Java

06Automated documentation lookup via MCP tools

Casos de Uso

01Developing multimodal AI agents capable of reasoning across different media types

02Migrating legacy generative AI codebases to the modern google-genai SDK architecture

03Implementing reliable tool-use and structured data extraction in production apps

Tieline — product intent grounded in code

Gemini API Development

Características Principales

Casos de Uso

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