What should I do if Gemini returns an empty response?

If the model returns an empty response, try explicitly asking it to 'continue' within the same session to prompt further output.

Why should I use Gemini CLI with Claude Code?

It allows you to process much larger contexts (up to 1M+ tokens) and multimodal files like PDFs and images that your current environment might not handle directly.

Can Gemini CLI handle local files?

Yes, you can use the @filename syntax within your prompt to reference specific code files or assets for Gemini to analyze.

How do I continue a conversation with Gemini?

You can use the list-sessions command to find your session UUID, then use the -r flag to resume that specific conversation thread.

How do I handle long processing times?

Since Gemini can take a significant amount of time for massive contexts, it is recommended to set a max timeout and use exponential backoff when waiting for bash output.

Gemini CLI Integration

Name: Gemini CLI Integration
Author: metrovoc

bymetrovoc

•

Data Science & ML

Integrates Google's Gemini models into the Claude Code workflow to handle massive context windows and multimodal file processing.

About

The Gemini CLI skill empowers Claude to leverage Google's Gemini models for tasks requiring extreme context lengths of up to 1M+ tokens, advanced reasoning, and multimodal analysis of images or PDFs. This integration allows users to seamlessly switch between models for specific workloads, manage conversational sessions via UUIDs, and process large file sets that might exceed standard limits. It is particularly useful when Claude needs to reference massive repositories or deep domain-specific knowledge that benefits from Gemini's specific training and architectural strengths.

Key Features

Direct file referencing using @ syntax for precise code explanations
Advanced reasoning and deep domain-specific knowledge retrieval
1 GitHub stars
Support for 1M+ token context windows for massive codebase analysis
Persistent session management to list and resume specific conversations
Multimodal processing for images, PDFs, and large data files

Use Cases

Extracting and reasoning over information within complex PDF documentation or visual assets
Analyzing massive repositories that exceed standard LLM context limits
Solving deep architectural problems requiring Gemini's specific reasoning capabilities

About

Key Features

Direct file referencing using @ syntax for precise code explanations
Advanced reasoning and deep domain-specific knowledge retrieval
1 GitHub stars
Support for 1M+ token context windows for massive codebase analysis
Persistent session management to list and resume specific conversations
Multimodal processing for images, PDFs, and large data files

Use Cases

Extracting and reasoning over information within complex PDF documentation or visual assets
Analyzing massive repositories that exceed standard LLM context limits
Solving deep architectural problems requiring Gemini's specific reasoning capabilities