What is the Gemini Claude Code skill?

It is a specialized plugin that allows the Claude Code CLI to interact with Google's Gemini models, enabling cross-model collaboration and advanced reasoning within your dev environment.

Which Gemini models are supported?

All Gemini model variants are supported. You can easily switch between them by setting the GEMINI_MODEL environment variable (e.g., gemini-1.5-pro).

How does the skill handle long-running code analysis?

The skill includes a fixed 2-hour timeout (7,200,000ms) to ensure that complex analysis or large-scale code generation tasks aren't interrupted prematurely.

Does this skill require a separate Gemini API key?

Yes, you must have the Gemini CLI installed and properly authenticated on your system for the skill to execute commands successfully.

Gemini AI Integration

Name: Gemini AI Integration
Author: Activer007

byActiver007

Data Science & ML

Integrates Google's Gemini models into your workflow for advanced reasoning and multi-perspective code analysis.

About

The Gemini skill bridges the gap between Claude Code and Google's Gemini CLI, allowing developers to leverage the unique strengths of models like Gemini 1.5 Pro and Flash directly within their terminal. This skill is ideal for tasks requiring complex reasoning, deep code analysis, or a 'second opinion' from a different leading LLM. By using specialized scripts and the uv package manager, it provides a stable environment for long-running AI tasks, security audits, and architectural reviews that benefit from Google's latest AI research.

Key Features

Configurable model selection via GEMINI_MODEL environment variables
Seamless execution of Gemini CLI commands through Claude Code
Automatic Python environment management using uv run
0 GitHub stars
Extended 2-hour timeout for complex reasoning and generation tasks
Directory-aware execution for context-specific code analysis

Use Cases

Performing deep security audits using Gemini's advanced reasoning capabilities
Generating diverse implementation patterns by leveraging multi-model outputs
Getting an alternative AI perspective on complex debugging or logic issues

About

Key Features

Configurable model selection via GEMINI_MODEL environment variables
Seamless execution of Gemini CLI commands through Claude Code
Automatic Python environment management using uv run
0 GitHub stars
Extended 2-hour timeout for complex reasoning and generation tasks
Directory-aware execution for context-specific code analysis

Use Cases

Performing deep security audits using Gemini's advanced reasoning capabilities
Generating diverse implementation patterns by leveraging multi-model outputs
Getting an alternative AI perspective on complex debugging or logic issues