What is Gemini Context Caching and how does it save costs?

Context Caching allows you to store large contexts (over 2048 tokens) on Google's servers. Subsequent requests using the same context are charged at a significantly lower rate, often resulting in savings of up to 90%.

Does this skill support real-time web data?

Yes, it includes instructions for enabling 'Google Search Grounding,' which allows the model to search the live web to provide up-to-date information with source citations.

Can Gemini 3 Pro execute code directly?

Yes, the skill covers the 'Code Execution' tool, which enables the model to generate and run Python code in a secure sandboxed environment to solve complex mathematical or logical problems.

How do I ensure Gemini returns valid JSON?

By using the Structured Outputs feature with Pydantic (Python) or Zod (TypeScript) schemas, you can force the model to adhere to a specific JSON structure, which is essential for programmatic data processing.

Gemini 3 Pro Advanced Features

Name: Gemini 3 Pro Advanced Features
Author: adaptationio

byadaptationio

Data Science & ML

Implements advanced Gemini 3 Pro capabilities including function calling, structured outputs, and context caching for production-grade AI applications.

About

This skill provides a comprehensive implementation guide for production-ready Gemini 3 Pro features, enabling developers to extend model capabilities beyond basic text generation. It covers critical patterns for tool integration using OpenAPI 3.0, grounding via Google Search, safe code execution, and significant cost optimization through context caching. By leveraging these advanced patterns, developers can build more reliable, structured, and efficient AI-powered systems while maintaining seamless integration with popular frameworks like LangChain, Vercel AI, and Pydantic AI.

Key Features

Grounded Google Search and safe code execution tools
High-efficiency context caching for 90% cost reduction on large prompts
Guaranteed JSON structured outputs using Pydantic and Zod
Custom Function Calling with OpenAPI 3.0 support
Advanced batch processing and thought signature management
0 GitHub stars

Use Cases

Building autonomous agents with custom tool access and function calling
Optimizing enterprise applications requiring frequent reuse of large datasets
Developing real-time RAG systems with Google Search grounding

About

Key Features

Grounded Google Search and safe code execution tools
High-efficiency context caching for 90% cost reduction on large prompts
Guaranteed JSON structured outputs using Pydantic and Zod
Custom Function Calling with OpenAPI 3.0 support
Advanced batch processing and thought signature management
0 GitHub stars

Use Cases

Building autonomous agents with custom tool access and function calling
Optimizing enterprise applications requiring frequent reuse of large datasets
Developing real-time RAG systems with Google Search grounding