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Tips and Tricks for Getting More Out of Gemini CLI

Tips and Tricks for Getting More Out of Gemini CLI

If you’re already using Gemini CLI but feel like you’re only scratching the surface, you’re not alone. Most developers use it for basic code generation, missing the powerful features that can transform their entire workflow. These practical tips will help you unlock Gemini CLI’s full potential and significantly boost your productivity.

Key Takeaways

  • Configure GEMINI.md files for persistent project context across all AI interactions
  • Create custom TOML commands to streamline repetitive development tasks
  • Use multimodal inputs to analyze images and diagrams directly in your terminal
  • Leverage memory management and checkpoints for safer experimentation

Prerequisites and Setup

Before diving into advanced features, ensure you have Node.js version 18 or higher installed. If you haven’t set up your API key yet, follow the official Gemini CLI setup guide to get started.

Master Project Context with GEMINI.md

The most underutilized Gemini CLI productivity feature is the GEMINI.md file. Place this file in your project root to provide persistent context that shapes every interaction with the AI.

# Project Context
- Tech stack: React 18, TypeScript, Tailwind CSS
- Code style: Functional components only
- Testing: Vitest with React Testing Library
- Conventions: Use custom hooks for state logic

# Guidelines
- Always include error boundaries
- Prefer composition over inheritance
- Write tests alongside new features

This configuration ensures consistent, project-aligned suggestions without repeating yourself. The AI automatically reads this file, making your Gemini CLI workflows more efficient and contextually aware.

Create Custom Commands for Repetitive Tasks

Transform complex, multi-step prompts into simple Gemini CLI commands by creating custom shortcuts. In your .gemini/commands/ directory, create TOML files for frequently used operations:

# review.toml
[command]
description = "Comprehensive code review"
prompt = """
Review the following code for:
1. Security vulnerabilities
2. Performance issues
3. Code style violations
4. Missing error handling
Provide specific suggestions with examples.
"""

Now you can simply type /review instead of writing out the entire prompt. This approach saves time and ensures consistency across your team’s code reviews.

Leverage Multimodal Inputs for Visual Debugging

One of the most powerful Google Gemini CLI features is multimodal support. You can analyze screenshots, mockups, or diagrams directly in your terminal workflow:

# Analyze a UI screenshot
gemini
> Analyze path/to/screenshot.png for accessibility issues in this design

This capability is invaluable for UI/UX debugging, converting designs to code, or understanding complex system architectures from diagrams.

Use Memory Management for Long-Running Projects

Gemini CLI includes the often-overlooked memory feature, which maintains context across sessions. Enable memory to avoid re-explaining your project structure:

# Store important context
gemini --remember "our API uses GraphQL with Apollo Server"

# Later sessions will retain this knowledge
gemini "Create a new resolver" # Automatically uses GraphQL context

This feature dramatically improves efficiency for ongoing projects where context continuity matters.

Experiment Safely with Checkpoint and Restore

Before making major changes, use the checkpoint feature to save your current state:

# Create a checkpoint before major changes
gemini /restore save "before-refactoring-auth"

# After experimenting, if needed:
gemini /restore load "before-refactoring-auth"

This safety net encourages experimentation without fear of losing working code, making it easier to explore different architectural approaches.

Optimize File References and Context Windows

Gemini CLI can handle up to 1 million tokens in its context window. Use file references strategically to provide comprehensive context without overwhelming the system:

# Reference multiple files for holistic understanding
gemini "Refactor this component considering:" --files src/components/Header.tsx,src/styles/theme.ts,src/hooks/useAuth.ts

For large codebases, selectively reference only relevant files rather than entire directories to maintain response quality and speed.

Streamline Terminal Tasks with Shell Mode

Use Gemini in shell mode to convert natural language into commands:

gemini
> find all TypeScript files modified in the last week
# Returns: find . -name "*.ts" -mtime -7

This feature is particularly useful for complex git operations, system administration tasks, or when working with unfamiliar tools.

Best Practices for Maximum Productivity

  1. Start sessions with context: Always begin with relevant file references or a brief project summary
  2. Use specific, actionable prompts: “Generate a React hook for form validation with Zod” beats “make a form”
  3. Combine features: Use GEMINI.md with custom commands for powerful, context-aware automations
  4. Clear context periodically: For long sessions, clear irrelevant context to maintain response quality

Conclusion

These Gemini CLI tips transform it from a simple code generator into a comprehensive development partner. By mastering GEMINI.md configuration, custom commands, multimodal inputs, and memory management, you’ll work faster and more efficiently. Start with one or two techniques that address your immediate pain points, then gradually incorporate others as they become relevant to your workflow.

FAQs

Yes, Gemini CLI works with any project structure. Create a GEMINI.md file documenting your dependencies and conventions. The AI will respect these constraints when generating code, ensuring compatibility with your existing setup.

Gemini CLI runs locally but sends prompts to Google’s API. Never include API keys or passwords in prompts. Use environment variables and reference them symbolically when discussing authentication or configuration.

Checkpoints are temporary AI session states that preserve context and conversation history. Version control manages actual code changes. Use checkpoints for experimenting with AI suggestions before committing code changes to git.

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