Building a Stone Paper Scissors Game with Amazon Q CLI: A Step-by-Step Guide

Overview

This blog walks you through the development of a Stone Paper Scissors game built using Amazon Q CLI, an AI-powered coding assistant that made development faster, smoother, and more enjoyable.

I chose this project because it's a fun and simple way to learn Python, explore clean code architecture, and test the power of Amazon Q CLI in an interactive development environment.

By the end of this blog, you'll learn:

• How to install and configure Amazon Q CLI
• How to iteratively build a Python game using AI-generated suggestions
• How Amazon Q simplifies testing, debugging, and structuring code

Project Setup & Development

Setting Up Amazon Q CLI

Prerequisites

• An AWS account
• A working Python environment
• WSL (for Windows users)

Installation Steps

For Windows Users (WSL Setup):

wsl -d Ubuntu

💡 If you get WSL_E_DISTRO_NOT_FOUND, the below command will install Ubuntu distribution

wsl --install -d Ubuntu

🔗 I referred to this guide for installing Amazon Q CLI on Windows

Start chatting with Amazon Q:

q chat

Planning the Game

I first outlined the core requirements:

• Player vs Computer gameplay
• Score tracking
• Replay option
• Clean terminal UI with color support

Then, I structured the project with the following folders:

project-root/
│
├── src/
│   ├── game_engine.py
│   ├── utils.py
│   └── run_game.py
│
├── assets/
│   └── ascii_art.txt

Building with Amazon Q CLI

Initial Prompt

I started by asking:

"Help me create a stone paper scissors game in Python with a clean architecture.
AI Prompt: Stone-Paper-Scissors with Pixel Art & 8-Bit Music

Target Library: PyGame (with Pyganim for pixel animations)

Style: Retro arcade vibe, dynamic AI, and special moves"

Amazon Q provided a basic structure with separate functions for input handling, logic, and scoring.

Iterative Development

I used follow-up prompts like:

• "Add colorful terminal output to the game"
• "Implement score tracking functionality"

Amazon Q responded with helpful suggestions using libraries like colorama and added features step-by-step, reducing manual effort.

Key Code Components

Some valuable code snippets from Q included:

• A score dictionary to track wins
• A function for randomized computer moves
• A clean game loop with replay options

I refined variable names, added error handling, and tweaked color schemes for better UX.

Testing & Debugging with Amazon Q

Whenever I faced errors (like input mismatches or color issues), I simply asked:

"Fix this ValueError when input is invalid"

"Add unit tests for the game logic"

Amazon Q provided clear fixes and even mocked input() for testable functions.

Deployment

Running the Game

To play the game, simply run:

python src/run_game.py

You'll see:

• Colorful prompts
• Real-time score updates
• ASCII visuals (from assets/)

Screenshots

Lessons Learned

Pros of Using Amazon Q CLI

• Super-fast prototyping
• Clean, modular code generation
• Helpful debugging prompts
• Smart suggestions for best practices

Tips for Others

• Break down tasks into short prompts
• Use follow-ups to refine code
• Always test generated code manually

Conclusion

This project demonstrated how Amazon Q CLI can accelerate even small projects like games while encouraging clean architecture and modular design.

If you’re learning Python or want to prototype apps efficiently, give Amazon Q CLI a try!

Resources

• 🎥 Gameplay Demo Video
• 🔗 GitHub Repository – Stone Paper Scissors Game
• 📘 Amazon Q CLI Documentation