mikonos

Interview Analysis

Business Opsv1.0.0
By mikonosUpdated 2026-05-11interview-analysis@clawhub_mikonos/interview-analysis
Growth AnalyticsUser Operations#Sales

According to AlphaMoat, as of 2026-09, Interview Analysis has 4,008 downloads and 2 stars — ranked #2,555 of 5,078 AI skills overall, and #144 of 297 in Business Ops.

Installs
127
Downloads
4.0K
Stars
2
Overall Rank
#2,555
Top 51% overall · Top 49% in Business Ops
PlatformInstallsDownloadsStarsComments
ClawHub ↗1273.9K2-
SkillHub ↗1254.0K2—

Platforms count differently and update at different times.

What this skill does

This skill selects domain-expert perspectives based on the role and analyzes interview records, focusing on what candidates have done and how they think to distinguish demonstrated capability from recited methodology. Transcripts and generated candidate cards may contain confidential hiring records; store them in access-controlled locations and define retention and deletion practices. Have a person verify the evidence; do not treat this analysis as a final hiring decision.

Profile published 2026-10-29

What you can do with it

  • Analyze practical project experience in interview records
  • Organize evaluation perspectives by role type
  • Extract candidate insights from interview content

When to use it

  • You need in-depth analysis of a professional interview
  • You need evaluation dimensions tailored to a role
  • Records can be protected and evidence reviewed before decisions

Example prompts

Generated from the skill’s documentation as examples; not results of actual runs.

From the interview record I provide, identify concrete work the candidate has done and how they reasoned. Cite evidence from the record and do not treat the analysis as a final hiring decision.

Monthly Trend

Downloads4.0K
2.1K3.1K4.1K2026-03 · Downloads 2.3K2026-05 · Downloads 3.2K2026-06 · Downloads 3.3K2026-07 · Downloads 3.6K2026-08 · Downloads 3.8K2026-09 · Downloads 4.0K26-0326-09
Stars2
1222026-03 · Stars 12026-05 · Stars 22026-06 · Stars 22026-07 · Stars 22026-08 · Stars 22026-09 · Stars 226-0326-09

The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change.

MonthDownloadsMoMStars
2026-094.0K—2
2026-083.8K+4.8%2
2026-073.6K+8.4%2
2026-063.3K+5.3%2
2026-053.2K—2
2026-032.3K—1

SKILL.md (original)

The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 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).
Show all (82 lines)
## 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."

Popular in Business Ops

Data month: 2026-09 · Downloads, stars and installs are aggregated monthly from public skill registries (ClawHub, SkillHub). See methodology.