The ultimate guide to the GitHub Copilot CLI | Full demo | GitHub Checkout

GitHub Copilot CLI: A Comprehensive Summary

Key Concepts:

• Copilot CLI: A command-line interface for interacting with GitHub Copilot directly from the terminal.
• Agentic AI Assistant: AI that can perform tasks autonomously based on user instructions.
• MCP (Model Collaboration Protocol) Server: A server that facilitates collaboration between different AI models and tools.
• Contextual Awareness: The ability of the AI to understand the current project, environment, and user intent.
• Permissions Management: The system for granting or denying the CLI access to run commands on the user's computer.

Introduction to Copilot CLI

Ryan introduces Copilot CLI as the "last remaining piece of the puzzle" in the Copilot suite, bringing AI assistance directly to the terminal. He emphasizes that developers spend a significant amount of time in the terminal, making it a natural place for Copilot integration.

Installation and Basic Usage

The installation process is straightforward:

1. Ensure Node.js (version 22 or higher) and npm are installed.
2. Run npm install -g copilot to install Copilot CLI globally.
3. Run copilot to launch the CLI.

Ryan demonstrates basic usage by asking Copilot to "Tell me about the layout of this project." The CLI analyzes the project structure, identifies the project type (Node.js based on package.json), and uses the find command to gather information.

Onboarding and Environment Setup

Copilot CLI can assist with onboarding into new codebases. Ryan asks Copilot to "make sure that our environment is all set up for building the GitHub CLI." This showcases the CLI's ability to identify required dependencies, libraries, and tools for a specific project. Andrea highlights the benefit of reducing the need to manually read documentation and copy-paste commands.

System Administration Tasks

Andrea demonstrates using Copilot CLI to identify and manage processes using specific ports. She asks "What process is using port 8080?" Copilot CLI uses lsof to identify the process (netcat) and provides the Process ID. Ryan then uses Copilot CLI to kill the process, demonstrating its ability to execute system commands. The CLI then re-runs lsof to confirm the process is terminated.

Issue Implementation and Code Modification

Ryan demonstrates how Copilot CLI can be used to implement issues directly from the terminal. He asks Copilot to "Let’s implement issue #9" in a Java project. The CLI identifies the relevant file, generates a diff showing the proposed changes, and prompts the user for confirmation before applying the changes. It also suggests writing tests for the new feature.

MCP Server and Issue Searching

The CLI integrates with an MCP server (including a default GitHub MCP server) to search for issues. The /mcp command allows users to add custom MCP servers. The CLI can sort issues based on difficulty (e.g., easy, medium). The CLI follows guidelines laid out in files like copilot-instructions.md or agents.md to ensure best practices are followed.

Permissions and Security

Before executing commands on the user's system, Copilot CLI requests permission. Users can grant one-time access or permanently allow specific commands using "Yes, always allow." The output of commands can be viewed using Control-R and then typing "/".

Feedback and Future Development

Ryan emphasizes the importance of user feedback and encourages users to provide suggestions using the /feedback command. He mentions that future features for Copilot coding agent will also be integrated into Copilot CLI.

Authentication and Subscription

Users need to log in with their GitHub account to use Copilot CLI. An active Copilot subscription is required to access premium features. The CLI handles authentication and token management automatically, eliminating the need for manual API key configuration.

Conclusion

Copilot CLI brings the power of GitHub Copilot directly to the terminal, enabling developers to leverage AI assistance for a wide range of tasks, including project onboarding, environment setup, system administration, issue implementation, and code modification. It aims to be a consistent AI collaborator that follows the user across different projects and environments. The tool is designed to be editor-agnostic, providing a unified experience regardless of the IDE used.