Implement Python Algorithm Tutor with pattern-based learning system

[Copilot]

This PR implements a comprehensive Python-based algorithm study system that addresses the core dilemma outlined in the problem statement: optimizing time investment vs. study effectiveness for technical interview preparation.

What This Implements

The system provides a minimal but focused MVP that embodies evidence-based learning principles without falling into the "feature creep" trap mentioned in the problem analysis.

Core Features

🎯 Pattern-Based Learning System

• 10 essential algorithmic patterns covering 80%+ of interview questions
• Structured progression from Two Pointers and Sliding Window to advanced patterns like Tree traversals and Dynamic Programming
• Each pattern includes curated problems with difficulty progression (Easy → Medium → Hard)

📊 Smart Progress Tracking

• Mastery levels (0-100%) for each pattern with spaced repetition
• Automatic identification of weak areas needing attention
• Personalized study recommendations based on progress and review timing

🖥️ Rich CLI Interface

• Beautiful table displays showing patterns, progress, and recommendations
• Interactive study session management with problem suggestions
• Progress visualization and detailed reporting

Design Philosophy

This implementation directly addresses the concerns raised in the problem statement:

• Avoids Development Time Overhead: Built as a focused MVP in a few key files rather than an over-engineered system
• Prevents Feature Creep: Deliberately minimal feature set focused on core learning effectiveness
• Emphasizes Quality over Quantity: Pattern mastery tracking encourages deep understanding over problem grinding
• Evidence-Based: Implements spaced repetition and structured pattern recognition proven to be most effective

Usage Examples

# Get personalized study recommendations
algo-tutor recommend

# Study a specific pattern with guided problems
algo-tutor study "Two Pointers"

# Log completed session with progress tracking
algo-tutor complete "Two Pointers" --problems 2 --time 45 --difficulty medium

# View comprehensive progress dashboard
algo-tutor progress

Technical Implementation

• Modern Python: Uses Pydantic for data validation, Rich for beautiful CLI output, Click for command interface
• Persistent Storage: Simple JSON-based progress tracking without database overhead
• Comprehensive Testing: 21 unit tests covering all core functionality
• Code Quality: Formatted with Black, linted with flake8, imports sorted with isort

This system provides immediate value for algorithm interview preparation while avoiding the development time investment concerns mentioned in the analysis. Users can start structured, effective study immediately rather than spending weeks building custom tooling.

---

💡 You can make Copilot smarter by setting up custom instructions, customizing its development environment and configuring Model Context Protocol (MCP) servers. Learn more Copilot coding agent tips in the docs.

[codara-ai-code-review]

It seems you have not provided any code changes for review. Please share the code differences you'd like me to analyze, and I’ll be happy to provide you with actionable feedback.