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Skillsquestion-generator

question-generator

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Updated 10-20 05:43

About this skill

Question Generator Skill

Trigger Conditions

When the user needs to:

  • Generate interview questions for a target company
  • Practice specific types of interview questions
  • Get questions of different difficulty levels
  • Update the question bank
  • View question practice history

Core Capabilities

1. Question Generation Engine

Generate questions based on the following dimensions:

  • JD analysis results: Skill requirements extracted from jd-analyzer
  • Company characteristics: Company type, tech stack, business domain
  • Job level: Junior/Mid/Senior/Staff/Principal
  • Interview type: Technical/System Design/Behavioral/HR
  • Difficulty level: Easy/Medium/Hard

2. Question Types

Technical Interview Questions

Programming Problems:

  • Algorithms and data structures
  • LeetCode-style problems
  • Code optimization
  • Edge case handling

Technical Depth Questions:

  • Language features
  • Framework principles
  • System architecture
  • Performance optimization
  • Best practices

Design Questions:

  • API design
  • Database design
  • Class design
  • Design pattern application

System Design Questions

Classic system design:

  • URL Shortener
  • Twitter Timeline
  • Chat System
  • File Storage
  • Rate Limiter
  • Key-Value Store

Large-scale systems:

  • Distributed systems
  • Microservice architecture
  • Data consistency
  • Load balancing
  • Caching strategies
  • Message queues

Behavioral Interview Questions

STAR method questions:

  • Teamwork
  • Conflict resolution
  • Leadership
  • Challenging projects
  • Failure experiences
  • Showcasing achievements

Situational Questions:

  • How to handle emergencies
  • How to work with difficult colleagues
  • How to work under pressure
  • How to learn new technologies

HR Interview Questions

  • Self-introduction
  • Career planning
  • Salary expectations
  • Reason for leaving
  • Why choose this company
  • Strengths and weaknesses

3. Difficulty Level Algorithm

Easy:

  • Basic concepts and definitions
  • Direct application
  • Single-step problems
  • Suitable for: Junior positions, Warm-up

Medium:

  • Requires multi-step thinking
  • Combines multiple concepts
  • Moderately complex
  • Suitable for: Mid-level positions, Core interviews

Hard:

  • Requires in-depth analysis
  • System-level thinking
  • Optimization and Trade-offs
  • Suitable for: Senior+ positions, Screening interviews

4. Question Bank Management

Question Storage Structure

{
  "question_id": "unique_id",
  "type": "technical|system_design|behavioral|hr",
  "category": "algorithm|database|distributed_system|teamwork",
  "difficulty": "easy|medium|hard",
  "question_text": "...",
  "tags": ["array", "dynamic_programming", "google"],
  "created_at": "2024-01-01",
  "times_asked": 0,
  "success_rate": 0.0,
  "related_skills": ["Python", "Algorithms"]
}

Practice history tracking

{
  "practice_session": {
    "session_id": "unique_id",
    "date": "2024-01-01",
    "company_id": "...",
    "questions_practiced": [...],
    "performance": {...},
    "improvement_areas": [...]
  }
}

Workflow

Step 1: Read context

Use the Read tool to read:

  • data/companies/{company}.json - Target company and JD analysis
  • data/resume/base.json - User's base resume
  • data/questions/technical.json - Existing technical question bank
  • data/questions/behavioral.json - Existing behavioral question bank

Step 2: Analyze requirements

Extract from the JD analysis:

  • Required skills (required_skills)
  • Preferred skills (preferred_skills)
  • Job level
  • Company type (Big Tech/Startup/etc.)
  • Tech stack

Step 3: Generate questions

Generate questions based on the analysis results:

{
  "generated_questions": {
    "company": "Google",
    "position": "Software Engineer L3",
    "technical": {
      "easy": [
        {
          "question_id": "tech_001",
          "question": "实现一个LRU Cache",
          "category": "data_structure",
          "related_skills": ["Hash Table", "Doubly Linked List"],
          "estimated_time": "20-30 minutes",
          "follow_up_questions": ["如何O(1)时间复杂度实现？", "如何处理并发？"]
        }
      ],
      "medium": [...],
      "hard": [...]
    },
    "system_design": [...],
    "behavioral": [...],
    "hr": [...]
  }
}

Step 4: Update question banks

Add the newly generated questions to the corresponding question bank files:

  • Technical questions → data/questions/technical.json
  • Behavioral questions → data/questions/behavioral.json
  • System design → data/questions/system_design.json
  • HR questions → data/questions/hr.json

Step 5: Record generation history

Update data/analytics/question_generation.json:

{
  "generation_history": [
    {
      "timestamp": "2024-01-01T10:00:00Z",
      "company_id": "google",
      "position": "Software Engineer L3",
      "questions_generated": 15,
      "question_types": ["technical", "behavioral", "system_design"],
      "difficulty_distribution": {
        "easy": 5,
        "medium": 7,
        "hard": 3
      }
    }
  ]
}

Integration points

Integration with jd-analyzer

  • Read skill requirements from the JD analysis results
  • Generate relevant technical questions based on skill match
  • Generate preparation-oriented questions targeting skill gaps

Integration with interview-coach

  • Pass the generated questions to interview-coach for mock interviews
  • Receive interview feedback and adjust question difficulty
  • Recommend additional practice questions based on performance

Integration with interview-simulator

  • Provide the question bank to the simulator
  • Update question difficulty ratings based on simulation results
  • Track question success rates

Question generation strategy

Company-specific strategy

Google:

  • Emphasize algorithms and data structures
  • Scalability of system design
  • Open-ended questions ("Design a...")
  • Leadership Principle-related behavioral questions

Meta (Facebook):

  • Practical programming questions
  • Distributed systems
  • "Move Fast" culture-related behavioral questions
  • Impact and sense of achievement

Amazon:

  • System design and architecture
  • Practical problem solving
  • Leadership Principles behavioral questions
  • Customer obsession

Microsoft:

  • Product design thinking
  • Cross-platform development
  • Growth Mindset
  • Team collaboration

Startup:

  • Full-stack capability
  • Fast learning ability
  • Adapting to change
  • Multitasking

Job level strategy

Junior (L2-L3):

  • Fundamental concepts and coding ability
  • Learning potential
  • Easy to medium difficulty
  • Basic system design

Mid-level (L4):

  • Project experience
  • Problem-solving ability
  • Medium to hard
  • Complete system design

Senior (L5):

  • Architecture design
  • Technical leadership
  • Hard level
  • Complex system design

Staff+ (L6+):

  • Cross-team collaboration
  • Technical strategy
  • Highly challenging questions
  • Multi-system design

Output format

Standard question output

## 技术面试问题 - 中等难度

### 问题: 实现一个分布式缓存系统

**背景**:
- 需要支持高并发读写
- 数据需要持久化
- 节点可能故障

**要求**:
1. 设计系统架构
2. 讨论数据一致性策略
3. 处理节点故障
4. 优化性能

**评估要点**:
- CAP理论理解
- 分片策略
- 复制机制
- 一致性协议
- 性能优化

**预计时间**: 45分钟
**相关技能**: Distributed Systems, Cache, Consistency

Batch question output

{
  "batch_questions": {
    "company": "Google",
    "position": "Software Engineer L3",
    "total_questions": 20,
    "questions_by_type": {
      "technical": 8,
      "system_design": 4,
      "behavioral": 5,
      "hr": 3
    },
    "estimated_preparation_time": "2-3 weeks",
    "priority_order": [...]
  }
}

Best practices

  1. Question diversity: Avoid repetition, make each generation slightly different
  2. Progressive difficulty: From easy to difficult, help users build confidence
  3. Relevance: Ensure questions are highly relevant to the target position
  4. Timeliness: Update questions based on current technology trends
  5. Feedback-driven: Adjust question difficulty based on user performance
  6. Comprehensive coverage: Balance various types of questions, without favoring any particular type

Command integration

This skill can be invoked by the following commands:

  • /interview/prep - Generate interview preparation question sets
  • /interview/simulate - Provide questions for mock interviews
  • /company/questions - Generate company-specific questions
  • /weakness/practice - Generate practice questions targeting weaknesses

Data file dependencies

  • data/companies/*.json - Company and JD data
  • data/resume/base.json - User resume
  • data/questions/*.json - Question bank
  • data/analytics/question_generation.json - Generation history