In today’s world of intelligent development tools, the idea of building a terminal-based AI coding agent—a “Mini Cursor”—offered a unique blend of challenge and innovation. This project aimed to create an AI assistant that could function directly in the terminal and help automate full-stack project development, from initializing file structures to writing code and executing commands like npm install or pip install.

Here’s a summary of what I learned and discovered while building this AI coding agent:

1. Importance of Clear Task Chaining

The core function of this agent was to understand high-level developer commands and break them into executable terminal steps. I learned that proper prompt engineering and internal command structuring were critical. For example, a command like “create a React app with backend in Node.js” had to be internally mapped to several discrete operations:

Folder structure creation
npm init and npm install steps
Setting up key files like index.js, App.jsx, etc.

The ability to break down and chain tasks using structured planning logic is what made the agent useful and predictable.

2. Automating File and Folder Structure

The AI agent was designed to generate appropriate folder/file structures based on project type. I discovered that including templates and modular blueprints (e.g., boilerplate code for Express or React) significantly reduced error and increased speed. Automating this helped developers skip boilerplate setup and jump directly into writing business logic.

3. Executing Shell Commands via Python

One of the most technical aspects was securely and effectively running shell commands (like npm install, git init, etc.) from within Python. The subprocess module became the backbone for this. Handling errors, directory changes (cd), and environment dependencies was a learning curve, especially in making the agent reliable across platforms.

4. Follow-Up Prompt Support

The agent was designed to support iterative development—meaning the user could continue refining the project through conversational prompts. I had to implement a memory layer that preserved project state between prompts. This helped simulate an intelligent developer assistant who remembers what’s already been built.

6. Limitations and Future Work

While powerful, the agent was still limited by:

Security sandboxing (running terminal commands can be risky)
Lack of GUI support (fully terminal-based)
No advanced debugging help (yet)

To overcome this, future iterations could use containers or virtual environments, integrate with GitHub Copilot APIs, or even plug into VS Code for visual feedback.

Final Thoughts:

This project not only strengthened my Python and terminal automation skills, but also deepened my understanding of how AI can transform software development workflows. It reinforced the importance of making developer tools that are intelligent, extensible, and conversational.

Creating a "Mini Cursor" was like building the future of terminal-based coding — one command at a time.

Building a Terminal-Based AI Coding Agent: Insights from Creating a Mini Cursor