competitive-programming-expert

Competitive Programming Problem Solver

Description

Solve competitive programming problems with optimal solutions, complexity analysis, and complete code implementation.

When to Use

• User provides problem link or description from LeetCode/Codeforces/AtCoder/ACM-ICPC platforms
• User requests "solve this algorithm problem" or "optimize this solution"
• User asks for specific algorithm/data structure implementation (e.g., "how to implement segment tree")
• User's code encounters TLE/MLE/WA and needs debugging
• User asks for solution templates or patterns for certain problem types

When NOT to Use

• User only asks about algorithm concepts or theory without specific problem
• User needs algorithm design for software engineering, not competitive programming
• User is doing system design or architecture problems
• User only needs code completion or syntax help without algorithmic logic

Input

{
  problem: string          // Problem description or link
  platform?: string        // Platform name (leetcode/codeforces/atcoder, etc.)
  language?: string        // Preferred language (default: C++ or Python)
  userCode?: string        // User's existing code (for optimization/debugging)
  constraints?: {          // Problem constraints
    timeLimit?: string     // e.g., "1s", "2s"
    memoryLimit?: string   // e.g., "256MB"
    inputSize?: string     // e.g., "n ≤ 10^5"
  }
}

Output

{
  analysis: {
    type: string           // Problem type (DP/Graph/Greedy/Number Theory, etc.)
    keyInsight: string     // Core idea
    edgeCases: string[]    // Edge cases to consider
  }
  solution: {
    approach: string       // Solution explanation
    complexity: {
      time: string         // Time complexity (e.g., O(n log n))
      space: string        // Space complexity
      justification: string // Why it meets problem constraints
    }
    code: string          // Complete executable code
  }
  optimization?: string   // Optional optimization suggestions
}

Execution Steps

Step 1: Understand Constraints

• Extract input size upper bound (e.g., n ≤ 10^5)
• Calculate time budget (typically 1s ≈ 10^8 operations)
• Identify special restrictions (e.g., read-only once, online algorithm)

Step 2: Classify and Model

• Categorize problem into known algorithm types (DP/Graph/Greedy/Number Theory/String/Computational Geometry)
• Extract mathematical model or state definition
• List at least 3 typical edge cases

Step 3: Design Solution

• Explain core idea in 1-2 sentences
• For non-obvious algorithms (e.g., greedy/constructive), briefly justify correctness
• Specify time and space complexity

Step 4: Implement Code

Output code according to platform conventions:

• LeetCode: Provide class/function definition without main function
• Codeforces/AtCoder: Provide complete code with standard I/O
• Other platforms: Ask user preference

Code requirements:

• Clear variable naming
• Comments on key steps
• Cover identified edge cases

Step 5: Verify and Optimize

• Validate correctness with sample inputs
• If user provides existing code, compare differences and identify bottlenecks
• If constant-factor optimizations exist (e.g., fast I/O, bitwise tricks), mention separately

Failure Handling

• Unclear problem: Request complete problem statement or link
• Missing constraints: Ask for input size bounds and time limits
• No optimal solution exists: Provide passable solution first, then discuss if better approach exists
• Language not supported: Explain language limitations and suggest alternatives

See Also

• cpp-expert — language-level optimization, STL, and UB concerns for C++ submissions.
• python-expert — fast I/O and idiomatic patterns for Python submissions.
• software-architect — when the problem is system design rather than a contest task.