Leetcode Teacher

LeetCode Teacher

An interactive technical interview preparation teacher that generates engaging coding playgrounds with real-world product challenges, pattern recognition training, and multi-language support.

What This Skill Does

Transforms technical interview prep into interactive, practical experiences:

• Interactive Code Playgrounds - Browser-based coding environments with instant feedback
• Multi-Language Support - Python, TypeScript, Kotlin, Swift
• Real Product Challenges - Practical scenarios from real companies
• Pattern Recognition - Learn the 20 essential coding patterns
• Progressive Difficulty - Easy → Medium → Hard → Expert
• Instant Feedback - Run tests in real-time with detailed explanations
• Technique Teaching - Master problem-solving approaches

Why This Skill Matters

Traditional LeetCode practice:

• Abstract, disconnected problems
• No pattern recognition guidance
• Trial and error approach
• Intimidating for beginners
• Limited language options

With this skill:

• Real product scenarios
• Pattern-based learning
• Guided problem-solving
• Progressive difficulty curve
• Multi-language practice
• Interactive, fun interface

Core Principles

1. Pattern-First Learning

• Recognize problem patterns
• Apply proven templates
• Build intuition through practice
• Master one pattern at a time

2. Real Product Context

• Instagram feed ranking
• Uber trip matching
• Netflix recommendation
• Slack message search
• Amazon inventory management

3. Progressive Difficulty

• Start with fundamentals
• Build complexity gradually
• Unlock advanced patterns
• Track skill progression

4. Multi-Language Mastery

• Practice in your target language
• Compare implementations
• Learn language-specific tricks
• Interview in any language

5. Interactive Learning

• Write code in browser
• Run tests instantly
• Get hints when stuck
• See optimal solutions
• Track progress

Problem Patterns Covered

Array & String Patterns

1. Two Pointers

Pattern: Use two pointers to scan array
Use when: Need to find pairs, triplets, or subarrays
Example: "Find Instagram users who like each other"
Complexity: O(n) time, O(1) space

2. Sliding Window

Pattern: Maintain a window that slides through array
Use when: Need to find subarray with certain property
Example: "Find trending topics in last N tweets"
Complexity: O(n) time, O(k) space

3. Fast & Slow Pointers

Pattern: Two pointers moving at different speeds
Use when: Detect cycles, find middle element
Example: "Detect circular dependency in package manager"
Complexity: O(n) time, O(1) space

Tree & Graph Patterns

4. Tree BFS

Pattern: Level-order traversal using queue
Use when: Need level-by-level processing
Example: "Show friends by degree of connection"
Complexity: O(n) time, O(w) space (w = max width)

5. Tree DFS

Pattern: Preorder, inorder, or postorder traversal
Use when: Need to explore all paths
Example: "Find all paths in file system"
Complexity: O(n) time, O(h) space (h = height)

6. Graph BFS

Pattern: Explore neighbors level by level
Use when: Shortest path, level-based exploration
Example: "Find shortest connection path on LinkedIn"
Complexity: O(V + E) time, O(V) space

7. Graph DFS

Pattern: Explore as far as possible before backtracking
Use when: Path finding, cycle detection
Example: "Detect circular references in social graph"
Complexity: O(V + E) time, O(V) space

8. Topological Sort

Pattern: Order nodes by dependencies
Use when: Task scheduling, build systems
Example: "Order courses based on prerequisites"
Complexity: O(V + E) time, O(V) space

Dynamic Programming Patterns

9. 0/1 Knapsack

Pattern: Include or exclude each item
Use when: Optimization with constraints
Example: "Select best ads within budget"
Complexity: O(n * capacity) time and space

10. Unbounded Knapsack

Pattern: Can use item unlimited times
Use when: Coin change, combinations
Example: "Minimum transactions to reach balance"
Complexity: O(n * target) time and space

11. Fibonacci Numbers

Pattern: Current state depends on previous states
Use when: Climbing stairs, tiling problems
Example: "Ways to navigate through app screens"
Complexity: O(n) time, O(1) space optimized

12. Longest Common Subsequence

Pattern: Compare two sequences
Use when: Diff tools, edit distance
Example: "Find similar code snippets"
Complexity: O(m * n) time and space

Other Essential Patterns

13. Modified Binary Search

Pattern: Binary search on sorted or rotated array
Use when: Search in O(log n)
Example: "Find version when bug was introduced"
Complexity: O(log n) time, O(1) space

14. Top K Elements

Pattern: Use heap to track K largest/smallest
Use when: Finding top items
Example: "Get top K trending hashtags"
Complexity: O(n log k) time, O(k) space

15. K-Way Merge

Pattern: Merge K sorted arrays/lists
Use when: Combining sorted data
Example: "Merge activity feeds from K users"
Complexity: O(n log k) time, O(k) space

16. Backtracking

Pattern: Try all possibilities with pruning
Use when: Generate permutations, combinations
Example: "Generate all valid parentheses combinations"
Complexity: Varies, often exponential

17. Union Find

Pattern: Track connected components
Use when: Network connectivity, grouping
Example: "Find connected friend groups"
Complexity: O(α(n)) amortized per operation

18. Intervals

Pattern: Merge, insert, or find overlapping intervals
Use when: Calendar scheduling, time ranges
Example: "Find free meeting slots"
Complexity: O(n log n) time, O(n) space

19. Monotonic Stack

Pattern: Maintain increasing/decreasing stack
Use when: Next greater/smaller element
Example: "Stock price span calculation"
Complexity: O(n) time, O(n) space

20. Trie

Pattern: Prefix tree for string operations
Use when: Autocomplete, prefix matching
Example: "Implement search autocomplete"
Complexity: O(m) time per operation (m = word length)

Real Product Challenge Examples

Easy Level

Instagram: Like Counter

Real Scenario: Count how many times user's posts were liked today
Pattern: Hash Map
Data Structure: Dictionary/HashMap
Languages: Python, TypeScript, Kotlin, Swift

Slack: Unread Messages

Real Scenario: Find first unread message in channel
Pattern: Linear Search with Flag
Data Structure: Array
Teaches: Early termination

Uber: Calculate Fare

Real Scenario: Compute trip cost based on distance and time
Pattern: Simple Calculation
Data Structure: Numbers
Teaches: Math operations, rounding

Medium Level

Netflix: Top N Recommendations

Real Scenario: Find top N movies by rating
Pattern: Top K Elements (Heap)
Data Structure: Priority Queue
Teaches: Heap operations, partial sorting

Amazon: Inventory Management

Real Scenario: Find products running low in stock
Pattern: Filtering with Threshold
Data Structure: Array + HashMap
Teaches: Multi-criteria filtering

Twitter: Trending Hashtags

Real Scenario: Find most used hashtags in time window
Pattern: Sliding Window + Frequency Count
Data Structure: Queue + HashMap
Teaches: Time-based window management

LinkedIn: Degrees of Connection

Real Scenario: Find connection path between two users
Pattern: BFS
Data Structure: Graph (Adjacency List)
Teaches: Shortest path, level tracking

Hard Level

Google Calendar: Find Meeting Slots

Real Scenario: Find free time slots for all attendees
Pattern: Interval Merging
Data Structure: Array of Intervals
Teaches: Sorting, merging overlapping intervals

Spotify: Playlist Shuffle

Real Scenario: True random shuffle avoiding artist repetition
Pattern: Modified Fisher-Yates
Data Structure: Array
Teaches: Randomization with constraints

GitHub: Merge Conflict Resolution

Real Scenario: Find longest common subsequence in files
Pattern: Dynamic Programming (LCS)
Data Structure: 2D Array
Teaches: DP state definition, optimization

Airbnb: Search Ranking

Real Scenario: Rank listings by multiple weighted criteria
Pattern: Custom Sorting + Heap
Data Structure: Priority Queue with Comparator
Teaches: Complex comparisons, tie-breaking

Interactive Playground Example

Python Playground

  
  
  🚀 LeetCode Teacher - Two Sum (Instagram Likes)
  
    * { margin: 0; padding: 0; box-sizing: border-box; }
    body {
      font-family: 'SF Mono', 'Monaco', 'Courier New', monospace;
      background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
      min-height: 100vh;
      padding: 20px;
      color: white;
    }
    .container {
      max-width: 1400px;
      margin: 0 auto;
      display: grid;
      grid-template-columns: 1fr 1fr;
      gap: 20px;
    }
    .panel {
      background: rgba(255, 255, 255, 0.1);
      backdrop-filter: blur(10px);
      border-radius: 15px;
      padding: 30px;
      box-shadow: 0 20px 60px rgba(0, 0, 0, 0.3);
    }
    h1 {
      font-size: 2.5em;
      margin-bottom: 10px;
      text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3);
    }
    .difficulty {
      display: inline-block;
      padding: 5px 15px;
      border-radius: 20px;
      font-size: 0.9em;
      font-weight: bold;
      margin-bottom: 20px;
    }
    .easy { background: #4CAF50; }
    .medium { background: #FF9800; }
    .hard { background: #F44336; }
    .problem {
      background: rgba(255, 255, 255, 0.1);
      padding: 20px;
      border-radius: 10px;
      margin: 20px 0;
      line-height: 1.6;
    }
    .code-editor {
      width: 100%;
      min-height: 400px;
      background: #1e1e1e;
      color: #d4d4d4;
      font-family: 'SF Mono', monospace;
      font-size: 14px;
      padding: 20px;
      border-radius: 10px;
      border: none;
      resize: vertical;
    }
    .controls {
      display: flex;
      gap: 10px;
      margin: 20px 0;
    }
    .btn {
      padding: 12px 30px;
      border: none;
      border-radius: 10px;
      font-size: 1em;
      font-weight: bold;
      cursor: pointer;
      transition: transform 0.2s;
    }
    .btn-run {
      background: linear-gradient(135deg, #4CAF50, #45a049);
      color: white;
    }
    .btn-hint {
      background: linear-gradient(135deg, #FF9800, #F57C00);
      color: white;
    }
    .btn-solution {
      background: linear-gradient(135deg, #2196F3, #1976D2);
      color: white;
    }
    .btn:hover { transform: translateY(-2px); }
    .output {
      background: #1e1e1e;
      color: #4CAF50;
      padding: 20px;
      border-radius: 10px;
      min-height: 100px;
      font-family: monospace;
      white-space: pre-wrap;
      margin-top: 20px;
    }
    .test-case {
      background: rgba(255, 255, 255, 0.05);
      padding: 15px;
      border-radius: 8px;
      margin: 10px 0;
      border-left: 4px solid #4CAF50;
    }
    .test-failed {
      border-left-color: #F44336;
    }
    .stats {
      display: flex;
      justify-content: space-around;
      margin: 20px 0;
      padding: 20px;
      background: rgba(255, 255, 255, 0.1);
      border-radius: 10px;
    }
    .stat {
      text-align: center;
    }
    .stat-value {
      font-size: 2em;
      font-weight: bold;
      color: #FFD700;
    }
    .pattern-badge {
      display: inline-block;
      background: rgba(255, 215, 0, 0.2);
      color: #FFD700;
      padding: 5px 15px;
      border-radius: 15px;
      margin: 5px;
      font-size: 0.9em;
    }
  

  
    
    
      🎯 Two Sum
      Easy
      Pattern: Hash Map
      Array

      
        📱 Real Product Scenario: Instagram Likes
        You're building Instagram's "Mutual Likes" feature. Given an array of user IDs who liked your post and a target sum, find two users whose IDs add up to the target.

        Problem:
        Given an array of integers nums and an integer target, return indices of two numbers that add up to target.

        Example:
        
Input: nums = [2, 7, 11, 15], target = 9
Output: [0, 1]
Explanation: nums[0] + nums[1] = 2 + 7 = 9
        

        Constraints:
        
          2 ≤ nums.length ≤ 10⁴
          Only one valid answer exists
          Can't use the same element twice
        
      

      
        
          0
          Tests Run
        
        
          0
          Passed
        
        
          0
          Attempts
        
      

      
    

    
    
      💻 Your Solution (Python)
      def two_sum(nums, target):
    """
    Find two numbers that add up to target.

    Args:
        nums: List of integers
        target: Target sum

    Returns:
        List of two indices

    Time: O(n²) - Brute force
    Space: O(1)

    TODO: Optimize to O(n) using hash map!
    """
    # Your code here
    pass

# Test your solution
if __name__ == "__main__":
    # Example test
    nums = [2, 7, 11, 15]
    target = 9
    result = two_sum(nums, target)
    print(f"Result: {result}")

      
        ▶️ Run Tests
        💡 Get Hint
        ✨ Show Solution
      

      Click "Run Tests" to test your solution...
    
  

  
    let currentHint = 0;
    let attempts = 0;
    let testsRun = 0;
    let testsPassed = 0;

    const hints = [
      "💡 Hint 1: The brute force solution uses two nested loops. Can you do better?",
      "💡 Hint 2: Think about using a hash map to store numbers you've seen.",
      "💡 Hint 3: For each number, check if (target - current number) exists in your hash map.",
      "💡 Hint 4: Store the number's index in the hash map as you iterate."
    ];

    const testCases = [
      { nums: [2, 7, 11, 15], target: 9, expected: [0, 1] },
      { nums: [3, 2, 4], target: 6, expected: [1, 2] },
      { nums: [3, 3], target: 6, expected: [0, 1] },
      { nums: [1, 5, 3, 7, 9, 2], target: 10, expected: [1, 4] }
    ];

    function runCode() {
      attempts++;
      document.getElementById('attempts').textContent = attempts;

      const code = document.getElementById('codeEditor').value;
      const output = document.getElementById('output');

      try {
        // Simple Python simulation (in real implementation, use Pyodide or backend)
        output.innerHTML = 'Running tests...\n\n';

        testCases.forEach((test, i) => {
          const testDiv = document.createElement('div');
          testDiv.className = 'test-case';

          // Simulate test execution
          testsRun++;
          const passed = Math.random() > 0.3; // Simulated result

          if (passed) {
            testsPassed++;
            testDiv.innerHTML = `
              ✓ Test ${i + 1} Passed
              Input: nums = [${test.nums}], target = ${test.target}
              Expected: [${test.expected}]
              Got: [${test.expected}]
            `;
          } else {
            testDiv.className += ' test-failed';
            testDiv.innerHTML = `
              ✗ Test ${i + 1} Failed
              Input: nums = [${test.nums}], target = ${test.target}
              Expected: [${test.expected}]
              Got: undefined
            `;
          }

          output.appendChild(testDiv);
        });

        document.getElementById('testsRun').textContent = testsRun;
        document.getElementById('testsPassed').textContent = testsPassed;

        if (testsPassed === testCases.length) {
          output.innerHTML += '\n🎉 All tests passed! Great job!';
        }

      } catch (e) {
        output.innerHTML = `❌ Error: ${e.message}`;
      }
    }

    function getHint() {
      const hintsDiv = document.getElementById('hints');
      if (currentHint  index

    for i, num in enumerate(nums):
        complement = target - num

        if complement in seen:
            return [seen[complement], i]

        seen[num] = i

    return []  # No solution found

# Test your solution
if __name__ == "__main__":
    nums = [2, 7, 11, 15]
    target = 9
    r

…

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [jamesrochabrun](https://github.com/jamesrochabrun)
- **Source:** [jamesrochabrun/skills](https://github.com/jamesrochabrun/skills)
- **License:** MIT

Install and usage instructions live in the source repository linked above.