ClickMapper — A Keyboard-Driven Chrome Extension to Eliminate Repetitive Clicks for Content Moderators

This is a submission for the GitHub Copilot CLI Challenge

What I Built

I built ClickMapper, a Chrome extension designed to help content moderators and reviewers work faster by mapping clickable areas on a webpage and triggering actions using keyboard shortcuts.

I have experience working as a content moderator/reviewer, and one of the biggest pain points in that role is performing repetitive clicks in the same spots over and over again to review content. This becomes mentally exhausting and inefficient over long sessions. I realized it would be much more productive if these actions could be simplified with keyboard controls instead of constant mouse movement.

There are existing automation tools like AutoHotkey, but in many companies those tools are banned due to security concerns (malware risks, false positives, or policy restrictions). Additionally, those tools are often not beginner-friendly and require scripting knowledge. Because of that gap, I decided to build ClickMapper as a safe, browser-based moderator companion that works within company restrictions while remaining easy to use.

This project focuses on improving productivity, reducing repetitive strain, and making moderation workflows more efficient without requiring advanced technical setup.

Demo

Project Link: github.com/splmdny
[Update 2/22/26] Official Chrome Store Link: https://chromewebstore.google.com/detail/dnajkjbjlcbbibijanlpakkfkjgehka
Video Demo: https://youtu.be/s2wMHisA7O8
Screenshots:

My Experience with GitHub Copilot CLI

GitHub Copilot CLI played a major role in accelerating development. I used it to:

1. Generate Chrome Extension Manifest v3 structure
2. Scaffold content scripts and background logic
3. Quickly prototype DOM-mapping and event-handling functions
4. Debug permission issues and refine code patterns
5. Explore alternative implementation approaches through prompt-driven iteration

The biggest impact was speed. Instead of spending time searching documentation or writing boilerplate manually, I could describe what I wanted in natural language and iterate rapidly. It felt like having a pair-programming assistant that understood both the architecture and the intent behind the feature.

However, from my experience, Copilot still requires manual adjustment—especially when creating UI that is truly usable and favorable for the builder’s specific needs. While it can generate good starting points, refining layout, interaction details, and overall user experience still depends on human decisions and iteration.

Copilot CLI also helped reduce context switching, allowing me to stay focused on solving the actual workflow problem for moderators rather than getting stuck on setup details.

Overall, it significantly improved productivity and made experimentation much easier throughout the build process.