Interview Analysis Skill
Core Mission: Transform interview transcripts into deep insights.
Core Logic: Don't listen to what candidates "say" (Methodology Recitation), observe what they've "done" (Battle Scars) and "how they think" (First Principles).
1. Dynamic Expert Activation (Expert Routing)
Core Principle
Based on
role type and
evaluation dimensions, automatically select the
best minds combination for that domain:
Three-Step Expert Selection:
- 1. Identify core competency domain: Product/Engineering/Operations/Design/Sales/Data Science/...
- Match top domain thinkers: Recognized methodology masters or practitioners in the field
- Combine hiring experts: Geoff Smart (fact-checking) + Lou Adler (competency validation)
Common Role-Expert Mapping (Non-Exhaustive)
| Role Type | Domain Expert (Methodology) | Hiring Expert (Validation) | Rationale |
|---|
| Product Manager | Marty Cagan / Julie Zhuo | Geoff Smart | Product Sense + Fact Check |
| Software Engineer |
Linus Torvalds / John Carmack | Lou Adler | Engineering Judgment + Results Validation |
|
Growth Hacker | Sean Ellis / Brian Balfour | Geoff Smart | Growth Methodology + Metrics Verification |
|
UX Designer | Don Norman / Jony Ive | Lou Adler | UX Principles + Portfolio Validation |
|
Data Scientist | Andrew Ng / DJ Patil | Geoff Smart | Technical Depth + Project Verification |
|
Operations | Sheryl Sandberg / Reid Hoffman | Lou Adler | Scale Operations + Results Focus |
|
Sales/BD | Aaron Ross / Jill Konrath | Geoff Smart | Sales Methodology + Performance Verification |
[!IMPORTANT]
Flexibility Principle: The table above is for reference only. Flexibly select the most appropriate expert combination based on specific role and candidate background.
Encourage Innovation: If you believe a non-mainstream expert is better suited to evaluate this candidate, make that choice and explain your rationale.
Core Question: "Who can best identify imposters in this role? Whose framework best validates core competencies?"
2. Execution Workflow
Step 1: Fact Reconstruction & Red Flag Scan
- * Timeline Reconstruction: Connect experiences scattered across multiple interview rounds, checking for logical gaps.
- Consistency Verification: Compare different versions of the same story told to different interviewers (e.g., reasons for leaving, project failures).
- Red Flag Annotation: Mark all vague titles (e.g., SPM), exaggerated data, and attribution fallacies ("it was all market/technology's fault").
Step 2: Deep Decoding - STAR Episodes
- * Tactic: Select 1-2 core cases (e.g., startup project, most challenging project) for microscopic analysis.
- Truth Extraction:
*
Methodology Check: Is the candidate reciting SOPs (MECE, SWOT) or applying first principles?
*
Solution Bias Check: Did they jump straight to "add features," or first conduct "value validation"?
*
Technical Boundary Check: For technical challenges, did they "deflect blame" or "anticipate"?
Step 3: Interviewer Meta-Analysis
- * Subject: Evaluate interviewer (you/colleagues) performance.
- Dimensions:
*
Depth: Did they probe at critical moments? Or let it pass?
*
Bias: Did they draw conclusions too early or ask leading questions?
*
Bar: Did they maintain A Player standards?
Step 4: Card-based Output (Zettelkasten Output)
Generate Markdown cards using the following standard templates, saved to
people/{candidate_name}/analysis/. Be sure to read template content before filling in analysis results.
- * Profile (Comprehensive Portrait):
* Template path:
templates/profile_template.md
* Purpose: Fact checking, red flag scanning, core competency assessment.
- * Insight (Deep Analysis):
* Template path:
templates/insight_template.md
* Purpose: Deep dive into specific domains (e.g., AI Capability, Product Strategy).
- * Meta-Analysis (Interviewer Review):
* Template path:
templates/evaluation_template.md
* Purpose: Evaluate interviewer performance and organizational recommendations.
- * Structure Note (Hub Document):
* Template path:
templates/structure_note_template.md
* Purpose: Serves as hub connecting all analysis cards above, forming decision closure.
3. Usage Examples
- * "Analyze Li Yashuang's three interview rounds, focusing on AI capabilities."
- "Review this interview to see where we interviewers did well and where we missed opportunities."
- "Use Marty Cagan's perspective to analyze this candidate's product thinking."
面试分析技能
核心使命:将面试记录转化为深刻洞察。
核心逻辑:不要听候选人“说”了什么(方法论背诵),要观察他们“做”了什么(实战伤痕)以及“如何思考”(第一性原理)。
1. 动态专家激活(专家路由)
核心原则
根据
岗位类型和
评估维度,自动选择该领域
最佳思维组合:
三步专家选择法:
- 1. 识别核心能力领域:产品/工程/运营/设计/销售/数据科学/……
- 匹配领域顶尖思考者:该领域公认的方法论大师或实践者
- 结合招聘专家:Geoff Smart(事实核查)+ Lou Adler(能力验证)
常见岗位-专家映射(非穷尽)
| 岗位类型 | 领域专家(方法论) | 招聘专家(验证) | 理由 |
|---|
| 产品经理 | Marty Cagan / Julie Zhuo | Geoff Smart | 产品直觉 + 事实核查 |
| 软件工程师 |
Linus Torvalds / John Carmack | Lou Adler | 工程判断 + 结果验证 |
|
增长黑客 | Sean Ellis / Brian Balfour | Geoff Smart | 增长方法论 + 指标验证 |
|
UX设计师 | Don Norman / Jony Ive | Lou Adler | UX原则 + 作品集验证 |
|
数据科学家 | Andrew Ng / DJ Patil | Geoff Smart | 技术深度 + 项目验证 |
|
运营 | Sheryl Sandberg / Reid Hoffman | Lou Adler | 规模化运营 + 结果导向 |
|
销售/商务 | Aaron Ross / Jill Konrath | Geoff Smart | 销售方法论 + 业绩验证 |
[!IMPORTANT]
灵活性原则:上表仅供参考。根据具体岗位和候选人背景,灵活选择最合适的专家组合。
鼓励创新:如果你认为某个非主流专家更适合评估该候选人,请做出选择并解释理由。
核心问题:“谁能最好地识别该岗位的冒牌货?谁的框架最能验证核心能力?”
2. 执行工作流
步骤1:事实重构与红旗扫描
- * 时间线重构:连接分散在多轮面试中的经历,检查逻辑断层。
- 一致性验证:比较同一故事在不同面试官面前的不同版本(例如离职原因、项目失败)。
- 红旗标注:标记所有模糊头衔(如SPM)、夸大数据以及归因谬误(“都是市场/技术的错”)。
步骤2:深度解码 - STAR事件
- * 策略:选择1-2个核心案例(如创业项目、最具挑战项目)进行微观分析。
- 真相提取:
*
方法论检查:候选人是背诵SOP(MECE、SWOT)还是应用第一性原理?
*
解决方案偏差检查:他们是直接跳到“添加功能”,还是先进行“价值验证”?
*
技术边界检查:面对技术挑战,他们是“推卸责任”还是“提前预判”?
步骤3:面试官元分析
*
深度:在关键时刻是否深入追问?还是轻易放过?
*
偏见:是否过早下结论或提出引导性问题?
*
标准:是否坚持了A级人才标准?
步骤4:卡片式输出(卡片盒输出)
使用以下标准模板生成Markdown卡片,保存至 people/{候选人姓名}/analysis/。请务必在填写分析结果前阅读模板内容。
* 模板路径:templates/profile_template.md
* 目的:事实核查、红旗扫描、核心能力评估。
* 模板路径:templates/insight_template.md
* 目的:深入特定领域(如AI能力、产品策略)。
* 模板路径:templates/evaluation_template.md
* 目的:评估面试官表现及组织建议。
* 模板路径:templates/structure
notetemplate.md
* 目的:作为连接上述所有分析卡片的枢纽,形成决策闭环。
3. 使用示例
- * “分析李亚爽的三轮面试,重点关注AI能力。”
- “复盘这次面试,看看我们面试官哪些做得好,哪些错过了机会。”
- “用Marty Cagan的视角来分析这位候选人的产品思维。”