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interview-analysis

interview-analysis

github.com/mikonosOther264 installsUpdated 37w ago
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About interview-analysis

interview-analysis is a skill for turning interview transcripts into candidate assessments and interviewer reviews. It routes evaluation perspectives by role, reconstructs facts across rounds, and organizes findings into linked Markdown cards.

Key features

  1. 01Role-based expert perspective selection
  2. 02Timeline and story consistency checks
  3. 03STAR episode deep analysis
  4. 04Interviewer performance review
  5. 05Markdown cards based on templates

Use cases

  1. 01Comparing a candidate's accounts across interview rounds
  2. 02Examining evidence behind claimed achievements
  3. 03Assessing role-specific competencies
  4. 04Reviewing interviewer depth and bias

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/mikonos/interview-analysis --skill interview-analysis

After installing, type/interview-analysisin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

README.md
templates/
profile_template.md
evaluation_template.md
insight_template.md
structure_note_template.md

Details

From the project README

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 TypeDomain Expert (Methodology)Hiring Expert (Validation)Rationale
Product ManagerMarty Cagan / Julie ZhuoGeoff SmartProduct Sense + Fact Check
Software EngineerLinus Torvalds / John CarmackLou AdlerEngineering Judgment + Results Validation
Growth HackerSean Ellis / Brian BalfourGeoff SmartGrowth Methodology + Metrics Verification
UX DesignerDon Norman / Jony IveLou AdlerUX Principles + Portfolio Validation
Data ScientistAndrew Ng / DJ PatilGeoff SmartTechnical Depth + Project Verification
OperationsSheryl Sandberg / Reid HoffmanLou AdlerScale Operations + Results Focus
Sales/BDAaron Ross / Jill KonrathGeoff SmartSales 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."

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