Gemini CLI Training Course

A comprehensive training course for mastering Google Gemini CLI - the open-source AI agent that brings Gemini directly into your terminal.

Course Overview

This 5-hour hands-on workshop covers everything from basic installation to advanced MCP configurations, custom commands, and enterprise workflows.

What You'll Learn

• Installation & Setup: Authentication strategies, environment configuration
• Core Commands: Interactive mode, one-shot prompts, file references
• Context Management: GEMINI.md files, hierarchical context loading
• Safety & Control: Sandbox modes, approval policies, checkpointing
• Advanced Features: MCP integration, extensions, custom commands
• Practical Skills: Real-world exercises in Python, JavaScript, and Java

Prerequisites

• Node.js 18+ installed
• Command-line experience
• Basic programming knowledge in at least one language
• Git familiarity
• Docker (optional, for sandbox mode)
• Google account for API access

Repository Structure

gemini-training/
├── slides.md                     # Slidev presentation
├── lab_handout.md               # Progressive hands-on labs
├── exercises/                    # Hands-on lab projects
│   ├── python/                  # Python projects
│   │   └── weather-app/         # Flask weather application
│   ├── javascript/              # JavaScript/TypeScript projects
│   │   └── task-manager/        # Node.js task manager
│   └── java/                    # Java projects
│       └── bookstore-api/       # Spring Boot REST API
├── config-examples/              # Sample configurations
│   ├── settings-basic.json      # Starter settings
│   ├── settings-advanced.json   # Full-featured settings
│   ├── settings-safe.json       # Safety-focused settings
│   └── settings-mcp.json        # MCP-focused settings
├── gemini-md-examples/          # GEMINI.md templates
│   ├── python-flask.md          # Python Flask template
│   ├── java-spring.md           # Spring Boot template
│   └── javascript-react.md      # React project template
└── commands/                     # Custom command examples
    ├── review.toml              # Code review command
    └── test-gen.toml            # Test generation command

Quick Start

1. Install Gemini CLI

# Via npm (recommended)
npm install -g @google/gemini-cli

# Verify installation
gemini --version

2. Authenticate

# Set API key (get from https://aistudio.google.com/apikey)
export GEMINI_API_KEY="your-api-key"

# Or add to ~/.gemini/.env
echo 'GEMINI_API_KEY=your-api-key' >> ~/.gemini/.env

3. Run Your First Command

gemini "Hello! What can you help me with?"

4. Start the Training

# Clone this repository
git clone https://github.com/kousen/gemini-training
cd gemini-training

# Install dependencies for slides
npm install

# Start the presentation
npm run dev

# Open browser to http://localhost:3030

Course Schedule

Section	Duration
Introduction & Setup	10 min
Installation & Prerequisites	15 min
Authentication & Account Setup	10 min
First Steps & Basic Interface	15 min
Core Commands & Functionality	65 min
Configuration & Customization	65 min
Advanced Features & Extensions	65 min
Practical Applications & Workflows	45 min
Wrap-up & Q&A	15 min

Key Gemini CLI Features Covered

Core Features

• Interactive REPL and one-shot modes
• File references with @ syntax
• Shell integration with ! prefix
• Sandbox mode for safe execution
• YOLO mode for autonomous operation

Configuration

• GEMINI.md context files (hierarchical)
• settings.json customization
• Environment variables
• Custom commands

Advanced Features

• Model Context Protocol (MCP) servers
• Extensions system
• Agent Skills
• Session management and checkpointing
• IDE integration (VS Code)

New in Gemini CLI 0.30 – 0.37

• Agent Skills stable and default-on; SDK + SessionContext (0.30)
• Policy Engine replaces --allowed-tools / tools.exclude (0.30)
• Gemini 3.1 Pro Preview available via /model (0.31)
• Workspace model steering + parallel extension loading (0.32)
• A2A remote agents over authenticated HTTP (0.33)
• Plan Mode is default-on; gVisor/LXC sandboxing on Linux (0.34)
• Multi-registry MCP/extensions, native macOS Seatbelt, Windows sandboxing, Git worktrees (0.36)
• Ctrl+G replaces Ctrl+X for external editor; persistent policy approvals (0.37)

Tips for Success

1. Start in Default Mode: Get comfortable before enabling YOLO mode
2. Use GEMINI.md: Provide context for better, more consistent results
3. Enable Checkpointing: Save snapshots before risky operations
4. Review Generated Code: Always verify AI-generated changes
5. Leverage MCP: Extend capabilities with custom tools

Useful Commands Reference

# Basic usage
gemini                          # Interactive mode
gemini "prompt"                 # One-shot mode
gemini -i "context"             # Interactive with initial prompt

# Configuration
gemini --sandbox                # Run in sandbox mode
gemini --approval-mode yolo     # Auto-approve all actions
gemini --model gemini-3-pro     # Specify model (e.g. gemini-3-pro, gemini-2.5-pro)

# Session management
gemini --resume                 # Resume last session
gemini --list-sessions          # Show available sessions

Slash Commands

Command	Description
/help	Show available commands
/clear	Clear conversation history
/memory show	View loaded context
/memory refresh	Reload GEMINI.md files
/init	Generate project GEMINI.md
/chat save <tag>	Save conversation
/resume	Open session browser
/rewind	Navigate/revert session history
/prompt-suggest	Get prompt ideas for the current task
/stats	Show token/quota/session stats
/restore	Recover from checkpoint
/compress	Summarize conversation

Resources

• Official Gemini CLI Documentation
• Google AI Studio - Get API keys
• MCP Server Registry
• Course Slides
• Lab Exercises

Instructor

Kenneth Kousen

• President, Kousen IT, Inc.
• Author & Technical Trainer
• ken.kousen@kousenit.com
• https://www.kousenit.com

License

This training material is licensed under the MIT License. See LICENSE file for details.

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Ready to start? Run npm run dev and navigate to http://localhost:3030!