[GitHub Copilot] Copilot CLI 実機ヘルプから作る完全版チートシート ✨ (v1.0.66 時点) [GitHub Copilot] Copilot CLI 実機ヘルプから作る完全版チートシート ✨ (v1.0.66 時点)

GitHub Copilot CLI brings the company's AI coding assistant directly into the terminal, and a recent community write-up assembles a "complete" cheat sheet by walking through the tool's own built-in help output as of version 1.0.66. The approach matters because command-line tools in this category iterate quickly, and the help text printed by the binary itself is usually the most authoritative reference available at any given moment.

The premise of the article, published on Qiita, is straightforward: rather than rely on potentially outdated documentation, the author runs the help command across the CLI's subcommands and options and consolidates the results into a single reference. The author frames it as a personal exploration, notes that the views expressed are their own and not those of an employer, and invites corrections. That disclaimer is common for fast-moving tooling, where behavior can shift between releases.

Copilot CLI is GitHub's agentic command-line interface, distinct from the older gh copilot extension that offered suggest and explain commands for one-shot shell help. The newer tool is designed to act as an agent that can read and modify files in a working directory, run commands, and carry out multi-step tasks from natural-language prompts. It typically launches into an interactive session where users converse with the model, but it also supports a non-interactive or programmatic mode for scripting and automation pipelines.

A cheat sheet of this kind generally covers several layers. At the top are the invocation flags passed when starting the binary, including options to set a prompt directly, choose a model, resume a previous session, control logging or banners, and adjust how much autonomy the agent has over running tools and commands. Inside an interactive session, slash commands handle session management and configuration: commands to view help, sign in or out, clear context, switch models, and manage integrations are typical. Capturing both surfaces in one place is the value the article aims to provide.

Model selection and external integrations are increasingly central to these tools. Copilot CLI lets users pick among supported large language models, which have included offerings from providers such as OpenAI and Anthropic within the broader Copilot platform, and the available list can change over time. Many agentic CLIs, including this one, also support the Model Context Protocol (MCP), an open standard for connecting assistants to external data sources and tools. A thorough reference would note how servers are configured and which commands manage them.

Authentication ties the CLI to a GitHub account and an active Copilot subscription, and the tool reads repository context from the directory in which it runs. Because the agent can execute commands and edit files, permission and approval controls are an important part of the interface, and reference material that documents the relevant flags helps users understand how to run the tool safely in different environments.

The release sits within a wider shift toward terminal-based AI coding agents. Comparable tools include Anthropic's Claude Code, OpenAI's Codex CLI, Google's Gemini CLI, and the open-source Aider, each blending conversational prompting with direct file and shell access. GitHub positions Copilot CLI as an extension of the same Copilot ecosystem that spans the editor extension and the Copilot coding agent, so familiarity with one surface tends to carry over to the others.

Readers should treat any such cheat sheet as a snapshot. Version 1.0.66 is one point on a rapidly moving release line, and flags, slash commands, and default models can be added, renamed, or removed between updates. The most reliable way to confirm current behavior remains running the help command in a local install, which is precisely the method the article advocates. For users adopting Copilot CLI, a consolidated reference like this can shorten the learning curve, provided it is checked against the version actually installed rather than treated as permanent.