🚀 From 2 Weeks to 30 Seconds: AI-Powered Codebase Onboarding (Built with Copilot CLI)

Levan Dalbashvili — Sun, 08 Feb 2026 15:36:43 +0000

This is a submission for the GitHub Copilot CLI Challenge

What I Built

MCP Orchestrator - A CLI tool that orchestrates multiple AI agents to solve real developer problems.

The Problem: New developers waste 1-2 weeks understanding a codebase before becoming productive. They spend days reading scattered docs, finding entry points, and understanding architecture.

My Solution: 4 independent MCP (Model Context Protocol) agents working together in a pipeline to analyze any repository and generate a comprehensive onboarding guide in 30 seconds.

Architecture

    ┌──────────────────────────────────────────────────────────────────┐
    │                      MCP ORCHESTRATOR                            │
    │                   (Pipeline Coordinator)                         │
    └────────────────────────────┬─────────────────────────────────────┘
                                 │
              ┌──────────────────┼──────────────────┬──────────────────┐
              │                  │                  │                  │
              ▼                  ▼                  ▼                  ▼
      ┌───────────────┐  ┌───────────────┐  ┌───────────────┐  ┌───────────────┐
      │   Agent 1     │  │   Agent 2     │  │   Agent 3     │  │   Agent 4     │
      │ Architecture  │  │ Entry Point   │  │ Documentation │  │  Onboarding   │
      │   Analyzer    │  │   Detector    │  │    Finder     │  │   Generator   │
      └───────┬───────┘  └───────┬───────┘  └───────┬───────┘  └───────┬───────┘
              │                  │                  │                  │
              │ Analyzes:        │ Locates:         │ Finds:           │ Combines:
              │ • Modules        │ • Main files     │ • READMEs        │ • All data
              │ • Classes        │ • CLI cmds       │ • Docs           │ • Into guide
              │ • Tests          │ • APIs           │ • Docstrings     │ • Learning
              │                  │                  │                  │   path
              └──────────────────┴──────────────────┴──────────────────┘
                                         │
                                         ▼
                           ┌──────────────────────────┐
                           │   Onboarding Guide       │
                           │   ✓ Project Overview     │
                           │   ✓ Quick Start          │
                           │   ✓ Architecture Map     │
                           │   ✓ Learning Path        │
                           └──────────────────────────┘

Each agent is an independent MCP server analyzing specific aspects of the codebase. The orchestrator coordinates them and passes data between steps.

Result: A comprehensive guide with project overview, quick start, architecture breakdown with module-level insights, and a "Your First Hour" learning path.

GitHub: github.com/levdalba/MCP-Terminal-Orchestrator

Demo

Watch it analyze Facebook's React repository (233k+ stars):

What it finds:

Project Type: Node.js with JavaScript/TypeScript
109 Entry Points across 20+ packages
74 Documentation files
Module-level insights (e.g., "Defines AppConfig class")
Complete architecture breakdown
Personalized learning path

Try it yourself:

# Install
pip install -e .

# Run on any repository
mcp pipeline examples/onboard_repo_pipeline.yml \
    --registry ./examples/onboarding_registry.json

My Experience with GitHub Copilot CLI

TL;DR: 80% faster development, 3-4 days instead of 2-3 weeks

I used GitHub Copilot CLI (copilot command) extensively throughout this project. Here's what made the biggest impact:

1. Architecture Design

Command I used:

copilot -p "Propose a minimal Python CLI repo layout for an MCP orchestrator using Typer, including pyproject.toml deps and an entrypoint module name." --model claude-sonnet-4.5 -s

Impact: Copilot designed the entire project structure with proper separation of concerns, recommended the right dependencies (typer[all]), and set up the entrypoint. This saved me ~2 days of architectural decisions.

2. MCP Protocol Implementation

Command I used:

copilot -p "Propose a minimal JSON-RPC framing approach for stdio transport in a Python MCP CLI." --model claude-sonnet-4.5 -s

Impact: Copilot explained how to implement JSON-RPC 2.0 communication with MCP servers, handling line-delimited messages and error responses. I implemented the JsonRpcClient based on this guidance. Without it, I would've spent days reading the MCP spec and debugging protocol issues.

3. Testing Strategy

Command I used:

copilot -p "Draft 3 pytest tests for registry loading and pipeline parsing in this repo." --model claude-sonnet-4.5 -s

Impact: Copilot generated comprehensive test cases covering edge cases I hadn't considered. This accelerated test writing from ~1 day to 3 hours and improved code quality.

What AI Excelled At:

✅ Boilerplate generation - Project structure, config files, setup

✅ API design - Clean CLI patterns and command structure

✅ Error handling - Comprehensive edge case coverage

✅ Documentation - README structure and examples

What Needed Human Oversight:

⚠️ Environment variables - Copilot's initial approach lost PATH, I had to fix subprocess env handling

⚠️ Real-world validation - Testing on actual repos (React, Django) revealed edge cases

⚠️ UX decisions - Demo presentation and visual formatting required human judgment

Time Breakdown:

Task	Without AI	With Copilot CLI	Savings
Project setup	2 days	2 hours	90%
Core implementation	1 week	2 days	70%
Testing	1 day	3 hours	80%
Documentation	1 day	1 hour	90%
Total	2-3 weeks	3-4 days	~80%

Key Takeaway:

GitHub Copilot CLI isn't just autocomplete - it's an AI pair programmer. I used it for:

Design decisions ("How should 4 agents work together?")
Debugging ("Find where we're checking membership on None")
Learning ("Explain JSON-RPC framing for MCP")

It dramatically accelerated development while teaching me MCP protocol concepts I didn't know. The --model claude-sonnet-4.5 flag was particularly powerful for architectural questions.

Would I use it again? Absolutely. It transformed a 2-3 week project into a 3-4 day sprint while maintaining code quality.

Built with ❤️ and AI assistance

Forem: Levan Dalbashvili

🚀 From 2 Weeks to 30 Seconds: AI-Powered Codebase Onboarding (Built with Copilot CLI)

What I Built

Architecture

Demo

My Experience with GitHub Copilot CLI

TL;DR: 80% faster development, 3-4 days instead of 2-3 weeks

1. Architecture Design

2. MCP Protocol Implementation

3. Testing Strategy

What AI Excelled At:

What Needed Human Oversight:

Time Breakdown:

Key Takeaway:

See CONTRIBUTING.md for detailed prompts and learnings.