interview-coach

Interview Coach Skill

Trigger conditions

When the user needs to:

• Prepare for technical interviews
• Prepare for system design interviews
• Practice behavioral interviews (STAR method)
• Conduct mock interviews
• Get interview feedback
• Analyze interview performance

Core capabilities

1. Interview preparation

Generate a preparation guide based on the target company and JD:

• Analyze the company and JD
• Identify key skills and knowledge points
• Generate a customized preparation checklist
• Recommend practice questions (by priority)
• Provide review resources

2. Mock interviews

Technical interview simulation

• Coding problems (LeetCode style)
• Algorithms and data structures
• Code quality assessment
• Time and space complexity analysis
• Optimization suggestions

System design interview simulation

• Classic system design questions
• Architecture design skills
• Scalability discussion
• Trade-offs analysis
• Component design

Behavioral interview simulation

• STAR method training
• Common behavioral questions
• Storytelling techniques
• Feedback and improvement suggestions

HR interview simulation

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

3. Feedback analysis

After each mock interview, provide:

• Technical accuracy: Whether the answer is correct
• Completeness: Whether key points were missed
• Clarity of communication: Whether the expression is clear
• Time management: Whether it was completed within the allotted time
• Improvement suggestions: Specific directions for improvement

4. Progress tracking

Record and analyze:

• Practice history
• Identification of weak areas
• Study plan adjustment
• Preparation progress assessment

STAR method guidance

STAR method explained

A structured approach to answering behavioral interview questions:

S - Situation (10-20%)

Provide the necessary background:

• Concise background
• Relevant information
• Do not over-explain

T - Task (10%)

Clarify the goal and responsibility:

• What problem you needed to solve
• The challenges faced
• What the goal was

A - Action (50-60%)

The most important part：

• The specific actions you took
• Your thought process
• Why you chose this approach
• Avoid using "we"; emphasize "I"

R - Result (20-30%)

Quantify the results:

• Quantifiable outcomes
• Positive results
• If the results were not good, emphasize what was learned
• Connection to the goal

STAR example

Question: Describe a technical challenge you solved.

Poor answer：

"We had a performance problem, I optimized the database queries, and then the performance improved."

Good answer (STAR)：

Situation: "At Company ABC, during the Double 11 peak, our order system's response time exceeded 5 seconds, causing user complaints and lost orders."

Task: "I was responsible for reducing the response time to under 500ms to ensure stable system operation."

Action: "I took the following steps:

1. Used performance profiling tools to identify bottlenecks and found that database queries were the main problem
2. Analyzed slow query logs and found several unoptimized JOIN operations
3. Introduced Redis caching for hot data
4. Optimized SQL queries and added necessary indexes
5. Implemented read-write separation to distribute the load
6. Conducted stress testing to verify the improvement results"

Result: "Response time dropped from 5 seconds to 200ms, exceeding the 500ms target. System throughput increased by 300%, successfully supporting the 10x business peak of Double 11."

Interview types and strategies

Technical interview

Problem-solving process

1. Understand the problem (1-2 minutes)

   • Restate the problem
   • Clarify inputs and outputs
   • Ask about boundary conditions
   • Give examples

2. Analysis and design (2-3 minutes)

   • Discuss possible approaches
   • Analyze time/space complexity
   • Choose the optimal approach
   • Use diagrams to aid thinking

3. Implementation (15-20 minutes)

   • Write the framework first
   • Implement step by step
   • Add comments
   • Consider edge cases

4. Testing (2-3 minutes)

   • Normal cases
   • Edge cases
   • Exception cases

5. Optimization (2-3 minutes)

   • Identify optimization points
   • Discuss trade-offs
   • Propose improvement plans

Common patterns

• Two pointers
• Fast and slow pointers
• Hash table
• BFS/DFS
• Dynamic programming
• Greedy algorithm

System design interview

Design framework

1. Understand the problem

   • Functional requirements
   • Non-functional requirements (scalability, availability, latency)
   • Scale estimation
   • Constraints

2. High-level design

   • Architecture diagram
   • Key components
   • Data flow

3. Detailed design

   • API design
   • Data model
   • Algorithm selection
   • Trade-offs discussion

4. Scalability

   • Load balancing
   • Database sharding
   • Caching strategy
   • CDN

5. Reliability

   • Redundancy and backup
   • Failure detection and recovery
   • Data consistency

Classic problems

• URL shortener service
• Design Twitter
• Design YouTube
• Design a chat system
• Design a file storage system

Behavioral interview

Core competency categories

1. Problem solving
2. Leadership
3. Teamwork
4. Adaptability
5. Initiative
6. Communication skills

Preparation strategy

• Prepare 5-8 core stories
• Use the STAR structure for each story
• Practice verbal delivery (2-3 minutes)
• Record yourself and listen back to improve

HR interview

Common questions

1. Self-introduction (2 minutes)
2. Why did you choose this company
3. Career plan (3-5 years)
4. Strengths and weaknesses
5. Salary expectations
6. Why are you leaving your current company

Answering strategy

• Self-introduction: highlight experience relevant to the position
• Why choose us: research the company and answer sincerely
• Career plan: show willingness to grow
• Strengths: explain with examples
• Weaknesses: genuine but improvable
• Salary: give a range and emphasize growth

Interview feedback framework

Technical interview feedback

{
  "question_id": "q_001",
  "type": "coding",
  "performance": {
    "approach_quality": 4,
    "code_quality": 3,
    "optimization": 3,
    "communication": 4,
    "time_management": 4
  },
  "strengths": [
    "思路清晰，考虑了多种方案",
    "代码结构良好，可读性强",
    "与面试官沟通积极"
  ],
  "areas_for_improvement": [
    "需要更深入考虑边界情况",
    "可以进一步优化时间复杂度",
    "可以更主动地讨论trade-offs"
  ],
  "specific_suggestions": [
    "在写代码前先列出所有边界情况",
    "练习更多动态规划题目",
    "思考时多思考为什么这个方案"
  ]
}

System design feedback

{
  "question_id": "q_002",
  "type": "system_design",
  "performance": {
    "architecture_quality": 4,
    "scalability_consideration": 3,
    "tradeoff_discussion": 3,
    "communication": 4
  },
  "strengths": [
    "系统架构清晰",
    "考虑了可扩展性",
    "画图辅助说明"
  ],
  "areas_for_improvement": [
    "数据库分片讨论不够深入",
    "可以更详细讨论一致性",
    "可以提出更多alternatives"
  ]
}

Behavioral interview feedback

{
  "question_id": "q_003",
  "type": "behavioral",
  "performance": {
    "situation": 4,
    "task": 4,
    "action": 3,
    "result": 3,
    "communication": 4
  },
  "strengths": [
    "故事结构清晰",
    "情境描述恰当",
    "表达流利"
  ],
  "areas_for_improvement": [
    "Action部分需要更多细节",
    "Result可以更加量化",
    "避免使用'我们'，多用'我'"
  ]
}

Interview preparation checklist

Technical preparation

• Review algorithms and data structures
• Practice LeetCode (at least 30 problems)
• Review system design fundamentals
• Prepare for language-specific questions
• Prepare for deep technical questions on projects

Behavioral preparation

• Prepare 5-8 STAR stories
• Practice self-introduction
• Prepare an answer for why you are leaving
• Prepare your career plan
• Research the target company

HR preparation

• Prepare for salary negotiation
• Prepare for benefits-related questions
• Prepare questions to ask the interviewer
• Learn about the company culture
• Prepare your reason for leaving

Company-specific styles

Google

• Values: "Googleyness" (curiosity, learning, empathy)
• Style: Open-ended, exploratory
• Key: Coachable, teamwork, technical excellence

Meta

• Values: Impact, rapid iteration
• Style: Direct, results-oriented
• Key: Move fast, Ship code, Impact

Amazon

• Values: Leadership principles
• Style: Structured, data-driven
• Key: Customer Obsession, Ownership

Common mistakes

Technical interview

1. Starting to write code directly without thinking
2. Not communicating and writing code silently
3. Not considering edge cases
4. Not optimizing the solution
5. Not testing the code

System design

1. Designing directly without asking about requirements
2. Ignoring scale estimation
3. Not discussing trade-offs
4. Going into details too early
5. Not drawing diagrams

Behavioral interview

1. Insufficient preparation and making things up on the spot
2. The situation is too long
3. Too many "we", too few "I"
4. Results not quantified
5. Negative content

---

Deficiency Analysis System

Deficiency Classification System

1. Technical deficiencies (technical)

Subcategories：

• algorithms - Algorithms (DP, greedy, graph theory, backtracking, etc.)
• data_structures - Data structures (trees, graphs, heaps, hash tables, etc.)
• coding - Coding ability (syntax, debugging, code quality, optimization)
• system_design - System design (architecture, scalability, reliability, performance)
• domain_knowledge - Domain knowledge (networking, OS, databases, distributed systems)

Identification signals：

• Unable to solve problems independently
• Inappropriate algorithm selection
• Logic errors in code
• Lacks understanding of fundamentals
• Unable to optimize the solution

2. Communication deficiencies (communication)

Subcategories：

• clarity - Clarity of expression (is the reasoning clear)
• structure - Logical structure (is it organized)
• terminology - Terminology usage (accuracy of technical terms)
• conciseness - Conciseness (is it verbose)
• listening - Listening comprehension (does he/she understand the question)

Identification signals：

• Reasoning jumps around; interviewer cannot follow
• Verbose expression; cannot grasp the key points
• Uses inappropriate terminology
• Misunderstands the question requirements
• Requires multiple clarifications from the interviewer

3. Time management (time_management)

Subcategories：

• analysis_time - Analysis takes too long (thinks too long)
• coding_speed - Slow coding speed (typing/syntax not proficient)
• planning - Unreasonable time allocation (loose at the start, rushed at the end)
• prioritization - Prioritization (spends too much time on secondary issues)

Identification signals：

• Not completed within the time limit
• Takes too long on easy problems
• No time to test the code
• Gets stuck on unimportant details

4. Thinking patterns (thinking_pattern)

Subcategories：

• decomposition - Problem decomposition (can complex problems be broken down)
• abstraction - Abstraction ability (can commonalities be extracted)
• pattern_recognition - Pattern recognition (can the problem type be identified)
• optimization - Optimization thinking (can optimization points be actively sought)
• trade_offs - Trade-off consideration (can the pros and cons of solutions be analyzed)

Identification signals：

• At a loss when facing complex problems
• Unable to recognize similar problems
• Considers only a single solution
• Does not discuss pros and cons comparisons
• Lacks in-depth thinking

5. Other deficiencies (other)

Subcategories：

• domain - Domain-specific knowledge (e.g., frontend, backend, DevOps)
• tools - Tool usage (Git, IDE, debugging tools)
• language - Language ability (spoken English/expression)
• confidence - Self-confidence (is he/she confident)
• stress_management - Stress management (handling nervousness)

Deficiency severity assessment

Severity levels

• high (8-10 points): Severely affects the interview outcome; must be improved immediately
  • For example: does not know basic algorithms, code is completely wrong, completely fails to understand the problem
• medium (5-7 points): Affects interview performance; requires short-term improvement
  • For example: the optimization plan is not good enough, the expression is not clear enough, the time slightly exceeds
• low (1-4 points): slight impact, can be improved in the long term
  • For example: code style, terminology usage, minor detail issues

Impact score (impact_score)

Factors to consider:

• Degree of impact on the final result
• Importance to the company/position
• Impact on subsequent interviews
• Impact on the offer

Score range: 1-10 points

Weakness analysis process

Step 1: Collect evidence

For each weakness, record:

• Specific problem/scenario
• Details of the user's performance
• Interviewer feedback (if any)
• Basis for scoring

Step 2: Classify and file

Classify weaknesses into the 5 major categories and corresponding subcategories

Step 3: Assess severity

• Score according to the degree of impact on the interview result
• Consider frequency of occurrence
• Consider the importance of the position

Step 4: Generate improvement suggestions

For each weakness provide:

• Specific improvement measures
• Recommended learning resources
• Practice suggestions
• Time planning

Step 5: Identify recurring issues

Compare with historical weaknesses:

• Calculate semantic similarity (text similarity 70% + keyword overlap 30%)
• Similarity ≥ 0.75 is judged as duplicate
• Mark as recurring

Improvement suggestion generation

Improvement suggestion template for technical weaknesses

问题：[具体技术不足]
根本原因：[为什么会出现这个问题]

改进计划：
1. 短期（1-2周）：
   - [具体学习内容]
   - [练习题量]
   - [验证方式]

2. 中期（3-4周）：
   - [深化内容]
   - [实战项目]
   - [总结归纳]

推荐资源：
- 书籍：[推荐书籍]
- 在线课程：[课程名称]
- 练习平台：[LeetCode/牛客等]
- 视频教程：[推荐视频]

Improvement suggestion template for communication weaknesses

问题：[具体沟通不足]
改进方法：

练习策略：
1. Think Aloud 练习
   - 边思考边说出来
   - 录音回听改进
   - 找人模拟练习

2. 结构化表达
   - 使用总-分-总结构
   - 先说结论再展开
   - 使用第一、第二、第三

3. 主动确认
   - 定期确认理解
   - 询问是否需要澄清
   - 征求反馈

Recurring weakness identification algorithm

Semantic similarity calculation

相似度 = 文本相似度 × 0.7 + 关键词重叠度 × 0.3

其中：
- 文本相似度：描述文本的语义相似度（使用embedding）
- 关键词重叠度：关键词/术语的重叠比例

判定阈值：≥ 0.75

Example

新不足："动态规划状态转移方程推导困难"
历史不足："DP问题无法独立设计状态"
相似度：0.82 → 判定为重复

Priority scoring algorithm

Calculation formula

priority = (frequency_score × 0.3 + severity_score × 0.3 + impact_score × 0.2 + status_weight × 0.2) × 10

Where:

• frequency_score: frequency score (1-10, the more frequent, the higher)
• severity_score: average severity (1-10)
• impact_score: impact score (1-10)
• status_weight: status weight
  • not_improved: 10
  • partially_improved: 5
  • improved: 2
  • resolved: 0

Result range: 0-100 points

Weakness tracking status

Status definitions

• not_improved: not improved (first occurrence or continuous occurrence)
• partially_improved: partially improved (absent in 3 consecutive interviews)
• improved: improved (absent in 5 consecutive interviews)
• resolved: resolved (absent in 7 consecutive interviews)

Status transition rules

Upgrade (improvement)：

• not_improved → partially_improved: absent in 3 consecutive interviews
• partially_improved → improved: absent in 5 consecutive interviews
• improved → resolved: absent in 7 consecutive interviews

Downgrade (recurrence)：

• improved/resolved → partially_improved: reappears
• partially_improved → not_improved: reappears

Weakness analysis report template

# 面试不足分析报告

## 基本信息
- 面试ID: {{interview_id}}
- 公司: {{company}}
- 类型: {{interview_type}}
- 日期: {{date}}
- 分析时间: {{analysis_timestamp}}

## 总体评估
- 总体评分: {{overall_score}}/10
- 识别问题: {{total_issues}}个
- 严重问题: {{critical_issues}}个
- 重复出现: {{recurring_issues}}个

## 🔴 紧急改进（重复出现）

### {{weakness_name}}
- **ID**: {{weakness_id}}
- **出现次数**: {{occurrence_count}}次
- **最近出现**: {{last_occurrence}}
- **历史**: {{history}}

#### 具体表现
- {{evidence_1}}
- {{evidence_2}}

#### 改进计划
1. [ ] {{action_1}}（截止: {{target_date}}）
2. [ ] {{action_2}}（截止: {{target_date}}）

#### 推荐资源
- {{resource_1}}
- {{resource_2}}

## 🟡 短期改进
[类似结构]

## 🟢 长期提升
[类似结构]

Best practices for weakness analysis

1. Objective and specific: based on specific performance, avoid subjective judgment
2. Actionable: improvement suggestions must be specific and executable
3. Clear priorities: distinguish urgent, important, and long-term
4. Positive encouragement: point out strengths and boost confidence
5. Continuous tracking: Track improvement progress and adjust plans in a timely manner

---

Advanced Features (New in Phase 3)

Smart Preparation Plan Generation

JD-based customized preparation

{
  "personalized_prep_plan": {
    "company": "Google",
    "position": "Software Engineer L4",
    "interview_date": "2024-01-25",
    "prep_days": 10,

    "daily_plan": {
      "day_1": {
        "focus": "算法基础 + Google文化",
        "tasks": [
          "完成2道Easy DP题 (建立信心)",
          "阅读Google工程博客 (了解文化)",
          "准备3个STAR故事 (Leadership)",
          "时间: 3小时"
        ]
      },
      "day_2": {
        "focus": "系统设计基础",
        "tasks": [
          "Design TinyURL (45分钟计时)",
          "观看Google系统设计视频",
          "复习CAP定理和一致性",
          "时间: 4小时"
        ]
      },
      "day_3-5": {
        "focus": "算法强化",
        "tasks": [
          "每天5道LeetCode Medium题",
          "严格计时 (25分钟/题)",
          "记录解题模式",
          "时间: 3-4小时/天"
        ]
      },
      "day_6-7": {
        "focus": "模拟面试 + 复盘",
        "tasks": [
          "2场模拟面试 (coding + system design)",
          "深入分析反馈",
          "针对性改进",
          "时间: 4小时/天"
        ]
      },
      "day_8-9": {
        "focus": "弱势加强",
        "tasks": [
          "针对弱点专项练习",
          "复习常见模式",
          "准备behavioral问题库",
          "时间: 3小时/天"
        ]
      },
      "day_10": {
        "focus": "最后准备 + 休息",
        "tasks": [
          "轻量练习 (2-3道简单题)",
          "准备面试官问题",
          "早休息 (充分睡眠)",
          "时间: 2小时"
        ]
      }
    },

    "resource_checklist": [
      "✅ LeetCode Premium (或免费题目)",
      "✅ System Design Primer",
      "✅ Google公司研究",
      "✅ 简历最终版本",
      "✅ 准备问面试官的问题",
      "✅ 模拟面试伙伴"
    ],

    "success_metrics": {
      "algorithm_accuracy": ">80% on Medium problems",
      "system_design": "能独立设计中等复杂度系统",
      "behavioral": "15+ STAR stories ready",
      "confidence_level": "7+/10"
    }
  }
}

Interview Round-Specific Preparation

Phone Screen (15-30 minutes)

focus: "快速验证基础技能"
expectations:
  coding: "1-2道简单-中等题"
  culture: "基本沟通和兴趣"

prep_strategy:
  - 刷30道Easy-Medium题
  - 准备2分钟self-introduction
  - 准备3个"为什么选择我们公司"的理由
  - 了解公司基本业务

success_criteria:
  - 能清晰交流思路
  - 代码无明显bug
  - 展现 genuine interest

Onsite Round 1: Coding (45-60 minutes)

focus: "算法深度 + 代码质量"
expectations:
  difficulty: "Medium-Hard"
  follow_ups: "优化、edge cases、alternative approaches"

prep_strategy:
  - 深度练习: DP, Graph, Tree
  - 每题必讨论testing
  - 优化: Time → Space → 代码简洁
  - 25分钟内完成Medium题

success_criteria:
  - 正确 + 优化思路
  - 清晰解释复杂度
  - 主动讨论edge cases
  - 时间管理良好

Onsite Round 2: System Design (45-60 minutes)

focus: "大规模系统设计能力"
expectations:
  complexity: "百万级用户考虑"
  depth: "架构决策 + trade-offs"

prep_strategy:
  - 掌握经典设计: URL shortener, Twitter, Uber
  - 练习7步设计法
  - 重点: Scalability, Availability, Consistency
  - 画图练习 (白板设计)

success_criteria:
  - requirements → design flow清晰
  - 主动讨论trade-offs
  - 考虑scalability和reliability
  - 图表清晰有逻辑

Onsite Round 3: Behavioral (45-60 minutes)

focus: "文化契合 + 软技能"
expectations:
  questions: "4-6个行为问题"
  style: "STAR方法, 量化成果"

prep_strategy:
  - 准备15-20个STAR故事
  - 覆盖所有公司文化维度
  - 练习时间控制 (每个故事2-3分钟)
  - 准备"向面试官提问"

success_criteria:
  - 每个问题都有STAR结构
  - 包含量化数据
  - 展现leadership和impact
  - 文化价值观匹配

Interview Success Probability Prediction

Machine learning-style scoring

{
  "success_probability_model": {
    "features": {
      "technical_skills": {
        "weight": 0.35,
        "score": 75,
        "impact": "High"
      },
      "communication": {
        "weight": 0.25,
        "score": 70,
        "impact": "Medium-High"
      },
      "cultural_fit": {
        "weight": 0.20,
        "score": 85,
        "impact": "Medium"
      },
      "experience_match": {
        "weight": 0.15,
        "score": 80,
        "impact": "Low-Medium"
      },
      "recommendations": {
        "weight": 0.05,
        "score": 90,
        "impact": "Bonus"
      }
    },

    "calculation": {
      "weighted_score": 76.75,
      "base_probability": "65%",
      "adjustments": {
        "hot_company": "-10%",
        "referral": "+15%",
        "high_demand_role": "+10%",
        "strong_market": "+5%"
      },
      "final_probability": "85%"
    },

    "confidence_interval": "±10%",
    "factors": "实际面试可能高于或低于预测",
    "disclaimer": "概率仅供参考，实际结果取决于面试表现"
  }
}

Mock Interview Plan Generator

Multi-round mock scheduling

{
  "mock_interview_schedule": {
    "week_1": {
      "day_1": {
        "type": "coding",
        "company": "Google",
        "difficulty": "Medium",
        "focus": "Time management",
        "partner": "AI or peer"
      },
      "day_3": {
        "type": "system_design",
        "company": "Amazon",
        "topic": "Design Uber",
        "focus": "Trade-offs discussion"
      },
      "day_5": {
        "type": "behavioral",
        "company": "Meta",
        "questions": ["Leadership", "Conflict", "Failure"],
        "focus": "STAR structure"
      }
    },
    "week_2": {
      "frequency": "3 mock interviews",
      "progression": "Easy → Medium → Hard",
      "companies": ["Google", "Meta", "Amazon", "Netflix", "Stripe"],
      "types": ["coding", "system_design", "behavioral", "hr"]
    }
  }
}

Pre-interview Mental Preparation

Mindset building

## 面试心态指南

### 正确的心态 ✅
1. **学习机会**: 将面试视为学习和改进的机会
2. **平等对话**: 面试是双向选择，你也在评估公司
3. **展现真我**: Be authentic, 不要过度表演
4. **信任准备**: 相信自己充分准备的能力
5. **享受过程**: 展示你的热情和技术兴趣

### 错误的心态 ❌
1. **完美主义**: 不可能完美，专注发挥最好水平
2. **过度紧张**: 紧张是正常的，适度紧张有助于专注
3. **比较心理**: 每个人路径不同，专注自己
4. **结果焦虑**: 一次面试不能定义你的价值
5. **讨好面试官**: 要专业，但不要卑躬屈膝

### 焦虑管理技巧
**面试前**:
- 深呼吸: 4-7-8呼吸法 (吸气4秒, 屏息7秒, 呼气8秒)
- 积极想象: 想象成功的场景
- 准备清单: 确认所有材料就绪
- 早到: 提前10-15分钟到达

**面试中**:
- 问题听不懂: 礼貌地请求clarify
- 卡住了: 诚实地说"Let me think", 不要慌张
- 出错了: 承认错误, 解释如何修正
- 时间压力: 专注当前步骤, 不要想太多

**面试后**:
- 记录: 立即记录问题和你的回答
- 复盘: 客观分析哪些做得好/不好
- 放下: 不要纠结，准备下一个
- 感谢: 发感谢信给面试官

Company-Specific Preparation Strategies

Google-specific preparation

emphasis: "技术卓越 + 可扩展性"

key_areas:
  algorithms:
    priority: "High"
    focus: ["DP", "Graph", "Tree", "Greedy"]
    pattern: "Start easy → Medium → Hard quickly"

  system_design:
    style: "Distributed systems at scale"
    hot_topics: [
      "Large-scale storage",
      "Distributed consensus",
      "Load balancing",
      "Data center design"
    ]

  cultural:
    values: ["Googleyness", "Curiosity", "Helpfulness"]
    behaviors:
      - "Think from user perspective"
      - "Collaborative problem solving"
      - "Learning orientation"
      - "Open to feedback"

  practice_specific:
    - "Practice explaining complex systems simply"
    - "Prepare examples of learning new things"
    - "Discuss projects with broad impact"
    - "Think about scalability from day 1"

Meta-specific preparation

emphasis: "Impact + Infrastructure"

key_areas:
  practical_coding:
    style: "Real-world problems"
    focus: ["System-oriented", "Production-ready"]
    common: ["API design", "Data pipelines", "Tooling"]

  system_design:
    style: "Infrastructure at massive scale"
    hot_topics: [
      "Edge computing",
      "CDN design",
      "Real-time systems",
      "Storage optimization"
    ]

  cultural:
    values: ["Move fast", "Ship code", "Impact"]
    behaviors:
      - "Focus on shipping value quickly"
      - "Discuss real-world impact"
      - "Show pragmatism over perfection"
      - "Demonstrate ownership"

  practice_specific:
    - "Prepare examples of fast iterations"
    - "Discuss trade-offs concretely"
    - "Show bias for action"
    - "Emphasize user impact"

Amazon-specific preparation

emphasis: "Customer obsession + Ownership"

key_areas:
  leadership_principles:
    critical: ["Customer Obsession", "Ownership", "Invent and Simplify"]
    behavioral: "Prepare 2-3 stories for EACH principle"
    integration: "Weave principles into all answers"

  system_design:
    style: "Customer-facing, scalable"
    focus: [
      "Customer journey",
      "Scalability for growth",
      "Operational excellence",
      "Data-driven decisions"
    ]

  practice_specific:
    - "Memorize all 16 Leadership Principles"
    - "Start with customer, work backwards"
    - "Use 'I' not 'we' to show ownership"
    - "Quantify impact (metrics, numbers)"

Interview Preparation Checklist

Final Checklist (Interview Eve)

## ✅ 面试前一天检查清单

### 材料
- [ ] 最终版简历 (PDF格式)
- [ ] 作品集/项目链接
- [ ] Github profile (整理好)
- [ ] LinkedIn profile (更新)
- [ ] Notepad and pen (两支笔)
- [ ] 2-3份纸质简历 (备用)

### 技术准备
- [ ] 刷题 (5-10题warm-up)
- [ ] 复习常见模式
- [ ] 准备系统设计案例
- [ ] 准备15+ STAR故事
- [ ] 简历每个项目的细节

### 公司研究
- [ ] 公司最新新闻
- [ ] 产品和服务了解
- [ ] 团队和部门信息
- [ ] 技术栈和架构
- [ ] 文化价值观

### 实务准备
- [ ] 确认面试时间/地点/平台
- [ ] 测试网络/摄像头/麦克风
- [ ] 准备问面试官的问题
- [ ] 充足睡眠 (7-8小时)
- [ ] 早餐/午餐安排
- [ ] 提前15分钟到达

### 心理准备
- [ ] 积极心态
- [ ] 放松练习
- [ ] 深呼吸准备
- [ ] 相信自己
- [ ] Good luck! 🍀

Interview Day Execution Flow

Day-of timeline

3_hours_before:
  - "轻量早餐 (高蛋白)"
  - "轻量复习 (5-10 LeetCode easy)"
  - "准备问面试官的问题"

1_hour_before:
  - "到达/登录"
  - "放松和深呼吸"
  - "最后一次材料检查"
  - "积极想象成功场景"

15_min_before:
  - "准备进入"
  - "深呼吸: 4-7-8 x 3次"
  - "调整心态: 这是展示机会"
  - "微笑和自信"

during_interview:
  - "Listen carefully to questions"
  - "Think out loud"
  - "Ask clarifying questions"
  - "Be authentic"
  - "Manage time well"

after_interview:
  - "立即记录反馈"
  - "感谢面试官"
  - "不要纠结具体表现"
  - "准备下一轮"
  - "发送感谢邮件"

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Phase 3 Enhancements: These advanced features make interview-coach a comprehensive interview preparation assistant, providing not only technical and behavioral guidance but also mental preparation, time planning, and success prediction.