This demonstrates how AI-assisted development and specialized SDKs can drastically reduce the time needed to build functional internal tools. It highlights the shift from manual coding to high-level planning and architectural review using modern LLMs.
Every week, the GitHub team runs a stream called Rubber Duck Thursdays, where we build projects live, cowork with our community, and answer questions!
This week, we built a very fun project together using the GitHub Copilot CLI! Let me tell you about it.
💡 New to GitHub Copilot CLI? Here’s how to get started.
In a lot of social media tweets and launches, you often see accounts post things like:
We shipped the most amazing emoji list generator ever. It:
💻 Works in the CLI
🤖 Uses the Copilot SDK to intelligently convert your bullet points to relevent emoji
📋 Copies the result to the clipboard
It’s beautiful. But coming up with the perfect emoji is far too slow for me in this “move fast and break things” world. I have projects to build! Repos to vibe! Pull requests to merge! I can’t be thinking about emojis!
And thus, on the stream, we build an emoji list generator (very descriptively called Emoji List Generator) that:
🖥️ Runs in the terminal
📋 You paste or write a list
⌨️ You hit Ctrl + S
📎 You get the list on your clipboard
(Can you tell I’m dogfooding the product here?)
We used a few cool technologies for this project:
🖥️ @opentui/corefor the terminal UI
🤖 @github/copilot-sdkfor the AI brain
📋 clipboardyfor clipboard access
To start the project off, we opened up the GitHub Copilot CLI.
In plan mode using Claude Sonnet 4.6, we wrote:
I want to create an AI-powered markdown emoji list generator. Where, in this CLI app, if I paste in or write in some bullet points, it will replace those bullet points with relevant emojis to the given point in that list, and copies it to my clipboard. I'd like it to use GitHub Copilot SDK for the AI juiciness.
Copilot asked me a bunch of clarifying questions, for example around the tech stack and what libraries we should use (shoutout to Gabor in the chat for suggesting OpenTUI), and from there, we had a fully thought-out plan.mdfile for me to review and use!
We implemented the plan using Claude Opus 4.7 (which was recently released!) and a few minutes later, voilà, we had a fun little terminal UI to work with!

The project was small but mighty. In the CLI, we used some really cool tools all together:
📋 Plan mode
🤖 Autopilot mode
🔀 Multi-model workflow
🚩 The allow-alltools flag
🐙 The GitHub MCP server
If you’d like to build a project like this yourself, you can check out the docs for the GitHub Copilot CLI and the GitHub Copilot SDK today!
The emoji list generator is free and open source, just for you.
Happy building!
The post Building an emoji list generator with the GitHub Copilot CLI appeared first on The GitHub Blog.
Continue reading on the original blog to support the author
Read full articleThis change reflects the increasing cost of running agentic AI models. For engineers, it introduces a metered cost structure, requiring better management of AI consumption while enabling access to high-compute agentic features without the previous hard gates on usage.
This highlights how AI-driven workflows and the Model Context Protocol (MCP) enable engineers to rapidly build custom productivity tools. It showcases a shift toward 'plan-then-implement' development, allowing developers to focus on architecture while AI handles the implementation details.
GitHub Copilot CLI streamlines development by bringing AI-powered code generation and autonomous agents directly into the terminal. This reduces context switching, enabling faster iterative building and automated error correction within the local environment.
This feature addresses self-reflection bias in AI agents by using heterogeneous model families for peer review. It significantly improves accuracy in complex, multi-file coding tasks, helping engineers catch architectural flaws and silent bugs before they compound into major technical debt.