mikonos

Interview Analysis

商業營運v1.0.0
作者:mikonos更新於 2026-05-11interview-analysis@clawhub_mikonos/interview-analysis
增長分析使用者營運#Sales

據 AlphaMoat 統計,截至 2026-09,Interview Analysis 累計下載 4,008 次、獲 2 個星標,在全部 5,078 個 AI 技能中排名第 2,555 位,在「商業營運」分類(297 個)中排名第 144 位。

累計安裝
127
累計下載
4008
星標
2
全站排名
#2,555
全站前 51% · 「商業營運」前 49%
平台累計安裝累計下載星標評論
ClawHub ↗12739032-
SkillHub ↗12540082—

各平台的計數口徑不同,統計時間不一致。

這個技能是做什麼的

該技能根據職位型別選擇領域專家視角,對面試記錄進行深入分析,關注候選人實際做過的事情和思考方式,以區分真實能力與方法論複述。面試記錄和生成的候選人卡片可能包含機密招聘資料,應存放在有訪問控制的位置,並明確保留和刪除規則。 由人工複核依據,不要將分析直接作為最終錄用決定。

資料釋出於 2026-10-29

可以用來做什麼

  • 分析面試記錄中的實際專案經驗
  • 根據職位型別組織評估視角
  • 從面試內容中提煉候選人洞察

什麼時候用

  • 需要深入分析專業崗位面試記錄時
  • 需要結合崗位特點組織評估維度時
  • 可以保護記錄並在決策前複核證據時

示例提示詞

以下是根據技能文件生成的示例,不代表實際執行結果。

根據我提供的面試記錄提煉候選人做過的具體工作及其推理過程;請逐條引用記錄中的依據,不要將分析直接視為最終招聘決定。

月度資料趨勢

累計下載4008
2121313541482026-03 · 累計下載 22612026-05 · 累計下載 31512026-06 · 累計下載 33172026-07 · 累計下載 35972026-08 · 累計下載 37702026-09 · 累計下載 400826-0326-09
星標2
1222026-03 · 星標 12026-05 · 星標 22026-06 · 星標 22026-07 · 星標 22026-08 · 星標 22026-09 · 星標 226-0326-09

2026-09 起統計方法調整:同名技能按作者分別統計,並覆蓋全部技能。圖中虛線為調整前的月份,與之後的資料不直接比較,也不計算跨越調整的增長。

月份累計下載環比星標
2026-094008—2
2026-083770+4.8%2
2026-073597+8.4%2
2026-063317+5.3%2
2026-053151—2
2026-032261—1

SKILL.md 原文

以下是作者釋出的原始檔案,按純文字顯示,本站未做修改或稽核;其中的連結不可點選,命令請核實後再執行。抓取於 2026-10-23。

5.1 KB · ClawHub v1.0.0
---
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).
展開全部(共 82 行)
## 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."

「商業營運」分類下的熱門技能

資料月份:2026-09 · 下載/星標/安裝數聚合自公開技能註冊庫(ClawHub、SkillHub),按月更新。口徑說明見資料方法。