gh-copilot

Suggest a command, given a description

$ gh copilot suggest "[Install git]"

$ gh copilot suggest [[-t|--target]] git "[Undo the most recent local commits]"

$ gh copilot explain "[traceroute example.com]"

$ echo 'eval "$(gh copilot alias -- bash)"' >> ~/.bashrc

$ echo 'eval "$(gh copilot alias -- zsh)"' >> ~/.zshrc

$ gh copilot config

gh-copilot <subcommand> [options] [arguments]

<subcommand>
    Specifies the AI operation to perform, such as suggest, explain, refactor, or chat.

--prompt <text>
    Provides a direct natural language prompt or question to the AI.

--file <path>
    Specifies a file to provide context for the AI, or to apply changes to. Used with subcommands like explain or refactor.

--language <lang>
    Hints the desired programming language for the AI's output, e.g., 'python', 'javascript', 'bash'.

--output <format>
    Defines the output format for the AI's response, such as 'text', 'markdown', or 'json'.

--line <start>[:<end>]
    When used with --file, specifies a particular line or range of lines within the file for AI analysis or modification.

--diff
    Used with refactor, shows a diff of proposed changes before applying them.

--apply
    Used with refactor, automatically applies the AI's proposed changes to the specified file.

--model <name>
    Selects a specific underlying AI model if multiple are available.

--verbose
    Enables verbose output for debugging or detailed information.

The gh-copilot command provides a command-line interface to AI-powered coding assistance, conceptually integrating features similar to GitHub Copilot directly into your Linux terminal. It allows developers to generate code snippets, explain complex code, refactor existing functions, or get quick answers to programming questions without leaving their development environment. By leveraging advanced AI models, gh-copilot aims to boost developer productivity by offering intelligent suggestions, boilerplate code generation, and contextual help based on the provided input or file content. It's designed to be a pragmatic tool for various programming tasks, from scripting and prototyping to debugging and learning. The command typically interacts with an underlying AI service (like GitHub Copilot's API) to process requests and return relevant, AI-generated content, making it a powerful companion for daily coding activities. Its utility extends across multiple programming languages and development workflows, bringing the power of an AI pair programmer directly to the command line, enhancing efficiency and reducing context switching.

This command typically requires an active subscription to a service like GitHub Copilot and an internet connection to function. AI-generated content, while often helpful, can sometimes be incorrect, incomplete, or contain 'hallucinations.' Users should always review and test AI-generated code carefully before deployment. Depending on the underlying AI service, there might be API rate limits or usage costs.

• suggest: Generates code snippets or solutions based on a prompt or context.
• explain: Provides explanations for code, commands, or concepts.
• refactor: Suggests and optionally applies improvements or changes to existing code.
• chat: Initiates an interactive conversational session with the AI.

Authentication details (e.g., API keys or tokens) and default settings (like preferred language or model) are usually managed through environment variables or a configuration file, typically located in ~/.config/gh-copilot/ or similar, to secure credentials and customize behavior.

The concept of integrating AI into development workflows gained significant traction with the rise of large language models. GitHub Copilot, initially released in 2021, popularized AI pair programming by providing real-time code suggestions within popular IDEs. The emergence of tools like gh-copilot reflects a natural progression to extend these powerful AI capabilities to the command-line interface, allowing developers to leverage AI assistance within their shell scripts, automation tools, and overall terminal-centric workflows, mirroring the trend of bringing powerful GUI features into the CLI for increased efficiency.

gh(1), git(1), curl(1), jq(1)