GitHub Copilot CLIで作る手続き生成型ローグライク Dungeons & Desktops: Building a procedurally generated roguelike with GitHub Copilot CLI

GitHub has published a hands-on blog post walking through the creation of a procedurally generated roguelike game using GitHub Copilot CLI, the company's terminal-based AI coding agent. The project illustrates how far natural-language prompting can take a developer when building a non-trivial application without leaving the shell.

Roguelikes, a genre named after the 1980 game Rogue, traditionally feature randomly generated dungeons, turn-based exploration, ASCII or tile-based rendering, and permadeath. These constraints make the genre a popular sandbox for exploring algorithms like room placement, corridor carving, and field-of-view computation. In the post, GitHub uses Copilot CLI to scaffold the game loop, implement dungeon generation, add enemy behavior, and iterate on rendering, all through conversational prompts rather than manual editing.

GitHub Copilot CLI differs from the IDE-integrated Copilot most developers know. It operates as an agent inside the terminal, capable of reading and writing files, running commands, and reasoning over multi-step tasks. That positions it alongside other emerging terminal-native coding agents such as Anthropic's Claude Code, OpenAI's Codex CLI, and Cursor's agent mode. The category has grown quickly in 2024 and 2025, reflecting a broader shift toward AI tools that can drive entire workflows rather than autocomplete individual lines.

The article highlights practical lessons that likely generalize beyond games. Prompt granularity matters: asking for an entire system at once tends to produce brittle results, while smaller, testable steps yield code that is easier to verify. Iterating with the agent, inspecting diffs, and running the game between turns provides a feedback loop similar to pair programming. For hobby projects, this style can lower the barrier to entry for developers who want to experiment with unfamiliar algorithms.

There are caveats worth noting. AI-generated code can drift in style, introduce subtle bugs, or pick suboptimal data structures, especially in domains like procedural generation where correctness is hard to eyeball. Developers still need enough domain literacy to review output, and complex systems may require human-led architectural decisions. The blog frames Copilot CLI less as a replacement for engineering judgment and more as a collaborator that accelerates the creative and exploratory parts of building software.

As an onboarding piece, the article doubles as a marketing showcase for Copilot CLI and a gentle introduction to roguelike development. Readers curious about either topic can likely follow along on their own machine, and the choice of a playful, visible artifact, a game running in the terminal, makes the agent's contributions easy to evaluate. Whether terminal-based agents will become the dominant interface for AI coding remains an open question, but GitHub's continued investment suggests the company sees the CLI as a meaningful complement to its editor integrations.