Skill: mikonos/interview-analysis (version 1.0.0)

--- name: interview-analysis description: Deep interview analysis using dynamic expert routing. Automatically selects top domain thinkers based on role type to distinguish genuine capability from performance, identifying Battle Scars over Methodology Recitation. Applicable to any professional position including product management, engineering, design, operations, sales, and data science. --- # 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/... 2. **Match top domain thinkers**: Recognized methodology masters or practitioners in the field 3. **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."

path: README.md
content: # Interview Analysis

**Dynamic Expert Routing for Candidate Assessment**

Transform interview transcripts into deep capability insights. The AI automatically selects domain-specific evaluation frameworks (Marty Cagan for PM, John Carmack for Engineering) and distinguishes genuine Battle Scars from Methodology Recitation.

---

## When to Use This Skill

Use this skill when:

- You have interview transcripts and need deep capability analysis
- You want to identify genuine "Battle Scars" vs "Methodology Recitation"
- You're evaluating candidates across diverse roles (PM, Engineering, Design, Sales, Data Science, etc.)
- You need structured output (Profile + Insights + Meta-Analysis cards) for hiring decisions

---

## How It Works: Expert Routing

| Candidate Role   | Domain Expert        | Hiring Expert | Output Focus                    |
|------------------|-----------------------|---------------|----------------------------------|
| Product Manager  | Marty Cagan / Julie Zhuo | Geoff Smart   | Product Sense + Fact Check      |
| Software Engineer| Linus Torvalds / John Carmack | Lou Adler     | Engineering Judgment + Results  |
| UX Designer      | Don Norman / Jony Ive | Lou Adler     | UX Principles + Portfolio       |
| Data Scientist   | Andrew Ng / DJ Patil  | Geoff Smart   | Technical Depth + Projects      |
| Growth / Sales   | Sean Ellis / Aaron Ross | Geoff Smart   | Methodology + Metrics Verification |

*The AI selects the most appropriate expert combination based on role context. You can override or add experts; the table is reference, not constraint.*

---

## What You Get

| Output           | Template                      | Purpose                                      |
|------------------|-------------------------------|----------------------------------------------|
| **Profile**      | `templates/profile_template.md`     | Resume verification, red flags, competency  |
| **Insight**      | `templates/insight_template.md`     | Domain-specific capability deep dive        |
| **Evaluation**   | `templates/evaluation_template.md`  | Interviewer meta-analysis & recommendations |
| **Structure Note** | `templates/structure_note_template.md` | Hub document linking all analysis cards    |

Cards are written to `people/{candidate_name}/analysis/` in Zettelkasten-friendly Markdown.

---

## Core Methodology

1. **Fact Reconstruction** — Timeline verification, consistency across rounds, red-flag annotation (vague titles, exaggerated data).
2. **Deep Decoding** — STAR episode analysis: first-principles vs SOP recitation, solution bias, technical boundary checks.
3. **Interviewer Meta-Analysis** — Depth of probing, bias control, bar-holding.

---

## Quick Reference

| Analysis Need              | Template                 | Expert Framework              |
|---------------------------|--------------------------|-------------------------------|
| Candidate credibility     | `profile_template.md`    | Geoff Smart (Topgrading)      |
| Role-specific capability  | `insight_template.md`    | Dynamic domain expert         |
| Interview quality review  | `evaluation_template.md` | Lou Adler + Kahneman          |

---

## Install

**ClawHub (OpenClaw)**:
```bash
npx clawhub@latest install interview-analysis
```

**Other (e.g. skills.sh)**:
```bash
npx skills add mikonos/interview-analysis
```

Compatible with Cursor, Claude Code, OpenClaw, and other agents that support the skills protocol.


path: templates/evaluation_template.md
content: # {YYYYMMDD}_Evaluation_Interviewer_Meta_Analysis_{Candidate_Name}

> [!TIP]
> **Purpose**: Through reviewing this interview, extract methodologies and raise organizational hiring standards.

## 1. {Interviewer A} ({Role})
*   **Methodology**: **{Method Name}**
    *   {Description}
*   **Highlights**:
    *   **{Highlight 1}**: {Description, cite specific questions}
*   **Areas for Improvement**:
    *   **{Improvement 1}**: {Description, how to do better}

## 2. {Interviewer B} ({Role})
...

## 3. Organizational Recommendations (Organizational Advice)
*   **Hiring Standard Alignment**: ...
*   **Process Optimization**: ...


path: templates/insight_template.md
content: # {YYYYMMDD}_Insight_{Candidate_Name}_{Domain}_Capability

> [!CAUTION/IMPORTANT]
> **Overall Assessment**:
> *   **{Domain A} Capability**: Tier {1-5}. {Brief comment}
> *   **{Domain B} Capability**: Tier {1-5}. {Brief comment}

---

## Part 1: {Core Competency} Deep Dive ({Domain} Deep Dive)

<!-- 
Guidance for AI:
Dynamically design {Expert_Model} and evaluation dimensions:

**Step 1: Select Expert Model**
- Reference SKILL.md Section 1's role-expert mapping table
- Flexibly adjust based on candidate's specific background

**Step 2: Design Evaluation Dimensions**
Choose 2-3 dimensions that **best expose imposters**, avoiding generic terms (like "communication skills" or "teamwork").

**Common Dimension Mapping** (Examples):
- **Product Role**: Discovery vs Delivery / Trade-off Thinking / User Empathy
- **Engineering Role**: First Principles Engineering / Code Quality Awareness / Technical Debt Trade-off
- **Operations Role**: Data-Driven vs Gut-Feel / Process Optimization / Stakeholder Management  
- **Design Role**: User Research Depth / Iteration Mindset / Design System Thinking
- **Growth Role**: Experiment Design / Metric Decomposition / Channel Strategy vs Tactics
- **Data Science Role**: Business Context Understanding / Statistical Rigor / ML vs Rule-based Trade-off

**Core Principles**:
- Each dimension must distinguish between "knowing" and "doing"
- Each dimension must be supported by specific interview evidence
- Dimensions should form a complete capability portrait
-->

### 1.1 {Dimension 1: Design based on role's core competency}
*Example: Product → Strategic Thinking; Engineering → System Design; Operations → Data-Driven Decision Making*
*   **Interview Evidence**:
    *   ...
*   **Deep Insight**:
    *   **{Insight Title}**: ...
    *   **Conclusion**: ...

### 1.2 {Dimension 2: Design based on role's core competency}
*Example: Product → Need Discovery; Engineering → Code Quality Awareness; Operations → Process Optimization*

*   ...

---

## Part 2: Critical Case Analysis (Case Study)

*Select 1-2 specific "pressure test" or "forensic probe" scenarios.*

### 2.1 Case: {Case Name}
*   **Interview Scenario/Follow-up**:
    *   Q: ...
    *   A: ...
*   **Essence Analysis**:
    *   **User vs Builder**: ...
    *   **First Principles**: ...

---

## 3. Score Summary

| Dimension | Score (0-10) | Assessment |
| :--- | :--- | :--- |
| **{Sub-Dimension 1}** | ... | ... |
| **{Sub-Dimension 2}** | ... | ... |


path: templates/profile_template.md
content: # {YYYYMMDD}_Candidate_Profile_{Candidate_Name}

> [!NOTE]
> **Analysis Perspective**: Based on {Expert_Persona_1} and {Expert_Persona_2}'s hiring philosophy.
> **Core Conclusion**: A **{core determination: e.g., strong executor / disguised strategist}**, but **{qualifying assessment: e.g., lacks foundational innovation / possesses high potential}**.
<!-- 
Guidance for AI:
Dynamically select {Expert_Persona_X} based on candidate's role:

**Expert Selection Strategy**:
- Persona 1 (Domain Expert): Top methodology master in the role's field
  - Examples: Product → Marty Cagan, Engineering → John Carmack, Design → Don Norman
- Persona 2 (Hiring Expert): Fact-checking expert (Geoff Smart) or competency expert (Lou Adler)

**Core Selection Questions**:
- Who can best identify "imposters" in this role?
- Whose framework best validates the authenticity of core competencies?
- If the role lacks obvious "masters," choose domain practitioners or first-principles thinkers

**Reference SKILL.md Section 1's expert mapping table**, but not limited to it.
-->

## 1. Resume Verification

| Time Period | Claimed Experience/Title | Actual Role Inference (Fact Check) | Concerns/Red Flags |
| :--- | :--- | :--- | :--- |
| **Company A** | Title | **Role** | ... |
| **Company B** | Title | **Role** | ... |

## 2. Core Competency Assessment

### 🔴 Disguise & Risks (The "Methodology Actor")
*Focus on where candidates "know but can't do," identifying "performative knowledge."*

*   **Evidence A ({label})**:
    *   **Phenomenon**: ...
    *   **Substance**: ...
*   **Evidence B ({label})**:
    *   ...

### 🟢 Highlights & Value (Green Signals)
*Focus on genuine achievements and qualities candidates unconsciously reveal.*

*   **{Highlight 1}**: ...
*   **{Highlight 2}**: ...

## 3. Final Verdict

*   **Hire?**: Suitable for {Role A} (rationale...)
*   **No Hire?**: Not suitable for {Role B} (rationale...)


path: templates/structure_note_template.md
content: # {Candidate_Name}_Interview_Assessment_Structure_Note

> [!IMPORTANT]
> **Decision Recommendation**: **{HIRE / NO HIRE}** (for {Position} role)
> **Alternative Suitable Role**: {Alternative role}

## 1. Core Evaluation Index

### 👤 Comprehensive Candidate Profile
*   [[{YYYYMMDD}_Candidate_Profile_{Name}]] - **{Keywords}**
    *   *Conclusion*: ...

### 🧠 Deep Capability Insights
*   [[{YYYYMMDD}_Insight_{Name}_{Domain}_Capability]] - **{Keywords}**
    *   *Conclusion*: ...
*   [[{YYYYMMDD}_Insight_{Name}_{Domain2}_Capability]]...

### 🎯 Interviewer Review (Meta-Analysis)
*   [[{YYYYMMDD}_Evaluation_Interviewer_Meta_Analysis_{Name}]]
    *   *Conclusion*: ...

## 2. Key Observations (Key Insights)

### 2.1 {Observation Point 1}
...

### 2.2 {Observation Point 2}
...

## 3. Next Steps Recommendations

1.  ...
2.  ...


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)