Agent skill

interview-problem-bank

Curated bank of interview problems organized by company, pattern, and difficulty. Provides problem recommendations, coverage tracking, weak area identification, and premium problem alternatives for FAANG interview preparation.

Stars 514
Forks 31

Install this agent skill to your Project

npx add-skill https://github.com/a5c-ai/babysitter/tree/main/library/specializations/algorithms-optimization/skills/interview-problem-bank

Metadata

Additional technical details for this skill

author
babysitter-sdk
version
1.0
category
algorithms-optimization
priority
high
skill id
SK-ALGO-024

SKILL.md

interview-problem-bank

A specialized skill for curating, organizing, and recommending coding interview problems, with support for company-specific preparation, pattern-based practice, and progress tracking.

Purpose

Provide a comprehensive interview problem bank with:

  • Problems organized by FAANG company and difficulty
  • Pattern-based categorization (Blind 75, NeetCode 150, etc.)
  • Difficulty progression recommendations
  • Coverage tracking and weak area identification
  • Premium problem alternatives

Capabilities

Core Features

  1. Problem Organization

    • By company (Google, Meta, Amazon, Apple, Microsoft, etc.)
    • By pattern (Two Pointers, Sliding Window, DP, etc.)
    • By difficulty (Easy, Medium, Hard)
    • By topic (Arrays, Trees, Graphs, etc.)
    • By frequency (most asked in interviews)
  2. Curated Problem Lists

    • Blind 75 (essential problems)
    • NeetCode 150 (expanded essential list)
    • LeetCode Top Interview Questions
    • Company-specific top questions
    • Pattern-specific problem sets
  3. Progress Tracking

    • Problems solved by category
    • Weak area identification
    • Time spent per problem type
    • Success rate tracking
    • Spaced repetition for review
  4. Recommendations

    • Next problem based on progress
    • Problems to strengthen weak areas
    • Company-specific practice plans
    • Time-based study schedules

Problem Lists

Blind 75

The essential 75 problems covering all major patterns:

Category Count Topics
Arrays & Hashing 9 Two Sum, Group Anagrams, Top K Frequent
Two Pointers 5 Valid Palindrome, 3Sum, Container with Water
Sliding Window 6 Best Time to Buy Stock, Longest Substring
Stack 7 Valid Parentheses, Min Stack, Daily Temperatures
Binary Search 7 Search Rotated Array, Find Minimum
Linked List 11 Reverse LL, Merge Lists, Detect Cycle
Trees 15 Invert Tree, Max Depth, Level Order
Tries 3 Implement Trie, Word Search II
Heap/Priority Queue 7 Merge K Lists, Top K Frequent
Backtracking 9 Subsets, Permutations, Combination Sum
Graphs 13 Number of Islands, Clone Graph
Dynamic Programming 12 Climbing Stairs, House Robber, Coin Change
Greedy 8 Maximum Subarray, Jump Game
Intervals 6 Merge Intervals, Meeting Rooms
Math & Geometry 8 Rotate Image, Set Matrix Zeros
Bit Manipulation 7 Single Number, Number of 1 Bits

NeetCode 150

Extended list with 150 problems for comprehensive preparation:

  • All 75 Blind 75 problems
  • 75 additional problems for deeper coverage
  • More advanced problems per category

Company-Specific Lists

Company Focus Areas Top Patterns
Google Problem solving, optimization Arrays, DP, Graphs
Meta Arrays, Trees, System Design Binary Trees, Arrays
Amazon OOP, System Design, Leadership Trees, BFS/DFS
Apple iOS/macOS, algorithms Arrays, Trees
Microsoft Coding, System Design DP, Arrays, Graphs
Netflix Distributed Systems Graphs, DP

Usage

Get Recommended Problems

bash
# Get next problem based on progress
interview-problem-bank recommend --user progress.json

# Get problems for specific pattern
interview-problem-bank list --pattern "dynamic-programming" --difficulty medium

# Get company-specific problems
interview-problem-bank company --name google --count 50

Track Progress

bash
# Mark problem as solved
interview-problem-bank solve --problem "two-sum" --time 15 --attempts 1

# Get progress report
interview-problem-bank progress --user progress.json

# Identify weak areas
interview-problem-bank analyze --user progress.json

Generate Study Plan

bash
# Generate 4-week study plan
interview-problem-bank plan --weeks 4 --target google --level intermediate

# Generate daily practice set
interview-problem-bank daily --count 3 --user progress.json

Output Schema

Problem Entry

json
{
  "id": "two-sum",
  "title": "Two Sum",
  "difficulty": "Easy",
  "patterns": ["Arrays", "Hash Table"],
  "companies": ["Google", "Amazon", "Meta", "Apple", "Microsoft"],
  "frequency": 95,
  "url": "https://leetcode.com/problems/two-sum/",
  "premiumAlternative": null,
  "hints": [
    "Use a hash table for O(1) lookup",
    "Store complement as key, index as value"
  ],
  "timeToSolve": {
    "target": 10,
    "beginner": 20,
    "expert": 5
  },
  "relatedProblems": ["3sum", "4sum", "two-sum-ii"]
}

Progress Report

json
{
  "user": "user123",
  "totalSolved": 150,
  "byDifficulty": {
    "Easy": 50,
    "Medium": 80,
    "Hard": 20
  },
  "byPattern": {
    "Arrays": { "solved": 25, "total": 30 },
    "DP": { "solved": 15, "total": 25 },
    "Graphs": { "solved": 10, "total": 20 }
  },
  "weakAreas": ["Graphs", "Advanced DP", "Tries"],
  "recommendations": [
    { "problem": "course-schedule", "reason": "Strengthen Graphs" },
    { "problem": "word-break", "reason": "Practice DP" }
  ],
  "streak": 15,
  "lastPracticed": "2025-01-24"
}

Study Plan

json
{
  "duration": "4 weeks",
  "target": "Google",
  "level": "intermediate",
  "schedule": [
    {
      "week": 1,
      "focus": ["Arrays", "Strings", "Two Pointers"],
      "problems": [
        { "day": 1, "problems": ["two-sum", "valid-anagram", "contains-duplicate"] },
        { "day": 2, "problems": ["best-time-to-buy", "max-subarray", "product-except-self"] }
      ]
    },
    {
      "week": 2,
      "focus": ["Sliding Window", "Stack", "Binary Search"],
      "problems": [...]
    }
  ]
}

Pattern-Based Organization

Array Patterns

Pattern Key Problems Technique
Two Pointers 3Sum, Container with Water Converging pointers
Sliding Window Longest Substring, Min Window Expand/contract window
Prefix Sum Subarray Sum Equals K Cumulative sum
Kadane's Maximum Subarray Track max ending at i

Tree Patterns

Pattern Key Problems Technique
DFS Recursive Max Depth, Path Sum Recursion
BFS Level Order Level Order Traversal Queue
Construct Tree Build from Preorder/Inorder Divide and conquer

Graph Patterns

Pattern Key Problems Technique
BFS Shortest Path Word Ladder Level-by-level
DFS Connected Components Number of Islands Visit all nodes
Topological Sort Course Schedule Kahn's algorithm
Union Find Number of Connected DSU

DP Patterns

Pattern Key Problems Technique
1D Linear House Robber, Climbing Stairs dp[i] depends on dp[i-1], dp[i-2]
2D Grid Unique Paths, Min Path Sum dp[i][j] from neighbors
String DP Edit Distance, LCS dp[i][j] for substrings
Knapsack Coin Change, Partition Include/exclude item

Integration Options

MCP Server

InterviewReady MCP Server:

bash
# Access curated interview content
npm install -g interviewready-mcp-server

External Resources

Integration with Processes

This skill enhances:

  • faang-interview-prep - Structured FAANG preparation
  • mock-coding-interview - Problem selection for mocks
  • interview-problem-explanation - Explaining solutions
  • skill-gap-analysis - Identifying weak areas

Interview Preparation Timeline

1 Week Preparation

Focus on high-frequency problems:

  • Day 1-2: Arrays and Strings (15 problems)
  • Day 3-4: Trees and Graphs (10 problems)
  • Day 5-6: DP and Backtracking (10 problems)
  • Day 7: Review and mock interview

1 Month Preparation

Comprehensive coverage:

  • Week 1: Fundamentals (Arrays, Strings, Hash Tables)
  • Week 2: Data Structures (Trees, Graphs, Heaps)
  • Week 3: Algorithms (DP, Backtracking, Greedy)
  • Week 4: Review, mock interviews, weak areas

3 Month Preparation

Deep mastery:

  • Month 1: All Easy + Medium fundamentals
  • Month 2: Advanced Medium + Hard problems
  • Month 3: Company-specific + mock interviews

References

Error Handling

Error Cause Resolution
PROBLEM_NOT_FOUND Problem not in database Search by alternate name
PREMIUM_LOCKED LeetCode premium required Use alternative problem
INVALID_COMPANY Company not recognized Check supported companies
PROGRESS_LOAD_FAILED Cannot load progress file Initialize new progress

Best Practices

  1. Quality over quantity - Understand solutions deeply
  2. Pattern recognition - Group problems by pattern
  3. Time yourself - Practice under interview conditions
  4. Review regularly - Spaced repetition helps retention
  5. Mock interviews - Practice explaining solutions
  6. Company research - Focus on company-specific patterns

Expand your agent's capabilities with these related and highly-rated skills.

a5c-ai/babysitter

gsd-tools

Central utility skill for GSD operations. Provides config parsing, slug generation, timestamps, path operations, and orchestrates calls to other specialized skills. Acts as the unified entry point that the original gsd-tools.cjs provided via its lib/ modules (commands, config, core, init).

514 31
Explore
a5c-ai/babysitter

model-profile-resolution

Resolve model profile (quality/balanced/budget) at orchestration start and map agents to specific models. Enables cost/quality tradeoffs by selecting appropriate AI models for each agent role.

514 31
Explore
a5c-ai/babysitter

verification-suite

Plan structure validation, phase completeness checks, reference integrity verification, and artifact existence confirmation. Provides the structured verification layer ensuring GSD artifacts are well-formed and complete.

514 31
Explore
a5c-ai/babysitter

state-management

STATE.md reading, writing, and field-level updates. Provides cross-session state persistence via .planning/STATE.md with structured fields for current task, completed phases, blockers, decisions, and quick tasks.

514 31
Explore
a5c-ai/babysitter

git-integration

Git commit patterns, formats, and conventions for GSD methodology. Provides atomic commits per task, structured commit messages, planning file commits, branch management, and milestone tag operations.

514 31
Explore
a5c-ai/babysitter

frontmatter-parsing

YAML frontmatter parsing and manipulation for .planning/ documents. Provides read, write, update, query, and validation operations on frontmatter blocks in GSD markdown artifacts.

514 31
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results