🎯 What You'll Learn   ✅ Prerequisites   🤖 Copilot Family   📚 Course Structure   📋 Command Reference

GitHub Copilot CLI for Beginners

✨ Learn to supercharge your development workflow with AI-powered command-line assistance.

GitHub Copilot CLI brings AI assistance directly to your terminal. Instead of switching to a browser or code editor, you can ask questions, generate full-featured applications, review code, generate tests, and debug issues without leaving your command line.

Think of it as having a knowledgeable colleague available 24/7 who can read your code, explain confusing patterns, and help you work faster!

📘 Prefer a web experience? You can follow this course right here on GitHub, or view it on Awesome Copilot for a more traditional browsing experience.

This course is designed for:

• Software Developers who want to use AI from the command line
• Terminal users who prefer keyboard-driven workflows over IDE integrations
• Teams looking to standardize AI-assisted code review and development practices

🎯 What You'll Learn

This hands-on course takes you from zero to productive with GitHub Copilot CLI. You'll work with a single Python book collection app throughout all chapters, progressively improving it using AI-assisted workflows. By the end, you'll confidently use AI to review code, generate tests, debug issues, and automate workflows: all from your terminal.

No AI experience required. If you can use a terminal, you can learn this.

Perfect for: Developers, students, and anyone who has experience with software development.

✅ Prerequisites

Before starting, ensure you have:

• GitHub account: Create one free
• GitHub Copilot access: Free offering, Monthly subscription, or Free for students/teachers
• Terminal basics: Comfortable with cd, ls, running commands

🤖 Understanding the GitHub Copilot Family

GitHub Copilot has evolved into a family of AI-powered tools. Here's where each one lives:

| Product | Where It Runs | Description | |---------|---------------|----------| | GitHub Copilot CLI(this course) | Your terminal | Terminal-native AI coding assistant | | GitHub Copilot | VS Code, Visual Studio, JetBrains, etc. | Agent mode, chat, inline suggestions | | Copilot on GitHub.com | GitHub | Immersive chat about your repos, create agents, and more | | GitHub Copilot cloud agent | GitHub | Assign issues to agents, get PRs back |

This course focuses on GitHub Copilot CLI, bringing AI assistance directly to your terminal.

📚 Course Structure

| Chapter | Title | What You'll Build | |:-------:|-------|-------------------| | 00 | 🚀 Quick Start | Installation and verification | | 01 | 👋 First Steps | Live demos + three interaction modes | | 02 | 🔍 Context and Conversations | Multi-file project analysis | | 03 | ⚡ Development Workflows | Code review, debug, test generation | | 04 | 🤖 Create Specialized AI Assistants | Custom agents for your workflow | | 05 | 🛠️ Automate Repetitive Tasks | Skills that load automatically | | 06 | 🔌 Connect to GitHub, Databases & APIs | MCP server integration | | 07 | 🎯 Putting It All Together | Complete feature workflows |

📖 How This Course Works

Each chapter follows the same pattern:

1. Real-World Analogy: Understand the concept through familiar comparisons
2. Core Concepts: Learn the essential knowledge
3. Hands-On Examples: Run actual commands and see results
4. Assignment: Practice what you learned
5. What's Next: Preview of the following chapter

Code examples are runnable. Every copilot text block in this course can be copied and run in your terminal.

📋 GitHub Copilot CLI Command Reference

The GitHub Copilot CLI command reference helps you find commands and keyboard shortcuts to help you use Copilot CLI effectively.

🙋 Getting Help

• 🐛 Found a bug? Open an Issue
• 📚 Official Docs: GitHub Copilot CLI Documentation

Contributing

Note: The code used in the course is designed to generate specific types of output during reviews, explanations, and debugging so we aren't able to accept PRs that change the existing code.

How to contribute:

1. Fork this repository and clone it to your machine
2. Create a feature branch (git checkout -b my-improvement)
3. Make your changes
4. Submit a pull request

License

This project is licensed under the terms of the MIT open source license. Please refer to the LICENSE file for the full terms.