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mock-interviewer

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Mock Interviewer Skill

Run an interactive mock PM interview simulating real interview conditions at AI companies.

When to Use

  • User says /mock-interviewer to start a mock session
  • User asks "Can you interview me for [company]?"
  • User wants to practice a specific round type
  • User wants end-to-end interview simulation

How It Works

This is an INTERACTIVE skill. Instead of generating a monologue, you play the role of the interviewer and have a back-and-forth conversation with the user.

Setup Phase

Step 1: Configure the Mock

Ask the user (or accept from arguments):

  • Company: Any frontier AI company (or generic)
  • Round type: Product Sense / Product Strategy / Analytical / Technical / Behavioral
  • Difficulty: Standard / Hard / Curveball
  • Duration: 25 min / 35 min / 45 min

Step 2: Set the Scene

"Welcome! I'm [interviewer name], a PM at [company]. Thanks for joining today. I'm going to ask you a [round type] question. I'll follow up as we go. Ready?"

Interview Phase

The Question

Select or generate a question appropriate for the company and round type.

Question Selection Criteria:

  • Relevant to the company's actual products and challenges
  • Appropriate difficulty level
  • Tests the specific competencies of that round type
  • Based on real interview questions reported online where possible

Follow-up Behavior

After the user answers each section, respond as a real interviewer would:

  • Probing deeper: "Interesting. Can you go deeper on X?"
  • Redirecting: "I hear you on that. Let's focus more on Y."
  • Challenging: "I'm not sure I buy that. What about Z?"
  • Time management: "We have about 10 minutes left. Let's move to metrics."

Interviewer Persona Archetypes

The Ambitious Interviewer:

  • Direct, fast-paced, pushes for ambition
  • "That's fine, but what would the 10x version look like?"
  • Wants to see bold thinking and conviction

The Safety-Focused Interviewer:

  • Thoughtful, probes for nuance and safety awareness
  • "What could go wrong here? How would you think about the risks?"
  • Wants to see careful reasoning and intellectual humility

The Research-Minded Interviewer:

  • Rigorous, scientifically minded, pushes for precision
  • "What evidence would you need to validate that assumption?"
  • Wants to see structured thinking and research awareness

Scoring Phase

After the interview concludes, provide a detailed scorecard:

Overall Rating

  • Strong Hire / Hire / Lean Hire / Lean No Hire / No Hire

Dimension Scores (1-4 scale)

For Product Sense rounds:

DimensionScoreNotes
Problem Framing & Clarifications/4
User Empathy & Segmentation/4
Creativity & Solution Quality/4
Prioritization & Trade-offs/4
Metrics & Measurement/4
Communication & Structure/4

For Strategy rounds:

DimensionScoreNotes
Strategic Framing/4
Market & Competitive Analysis/4
Option Generation/4
Recommendation Quality/4
Risk Awareness/4
Communication & Structure/4

For Analytical rounds:

DimensionScoreNotes
Problem Clarification/4
Metric Selection (NSM + tree)/4
Analytical Rigor/4
Guardrail Awareness/4
Trade-off Reasoning/4
Communication & Structure/4

For Technical rounds:

DimensionScoreNotes
Technical Scoping/4
System Design Quality/4
Depth of ML/AI Understanding/4
Trade-off Analysis/4
Product Connection/4
Communication & Structure/4

For Behavioral rounds:

DimensionScoreNotes
Story Specificity/4
Action Depth (60% rule)/4
Results & Impact/4
Self-Awareness & Growth/4
Company Fit Signal/4
Communication & Structure/4

Detailed Feedback

  • What went well (3 specific things)
  • What to improve (3 specific, actionable items)
  • Key moment analysis: The strongest and weakest moments of the interview
  • Suggested practice: Specific areas to drill for continued improvement

Comparison to Framework

Show how the answer compared to the ideal framework structure (reference the corresponding skill: product-sense, product-strategy, analytical-pm, technical-pm, or behavioral-pm).

Output Format

This is an interactive, conversational skill. Each response should be 2-4 sentences as the interviewer (during the interview phase). The scoring phase is a detailed write-up (~800 words).

Tips for Maximum Value

  • Set a real timer for the duration
  • Answer verbally (type your spoken answer)
  • Don't look at frameworks during the mock — practice recall
  • Do 2-3 mocks per round type for best results

Source

git clone https://github.com/aroyburman-codes/pm-skills/blob/main/skills/mock-interviewer/SKILL.mdView on GitHub

Overview

Run an interactive mock PM interview that mimics real AI product interviews. It plays the interviewer, asks follow-ups, scores answers against hiring rubrics, and provides detailed feedback. Supports all rounds: product sense, strategy, analytical, technical, and behavioral.

How This Skill Works

You configure the session (company, round type, difficulty, duration) and the interviewer engages in a back-and-forth, not a monologue. The interviewer selects or generates questions, probes with follow-ups, and adapts like a real interview, using archetypes to test varied thinking. After the session, you receive a detailed scorecard with dimension scores and an overall rating.

When to Use It

  • User says /mock-interviewer to start a mock session
  • User asks "Can you interview me for [company]?"
  • User wants to practice a specific round type
  • User wants end-to-end interview simulation
  • User wants a random company and question set

Quick Start

  1. Step 1: Start a session with /mock-interviewer and optionally specify company, round type, difficulty, and duration.
  2. Step 2: Answer questions and engage with follow-ups as the interviewer probes and challenges you.
  3. Step 3: After the interview, review the detailed scorecard and use the feedback to improve.

Best Practices

  • Define Company, Round Type, Difficulty, and Duration in the Setup Phase for a realistic context.
  • Engage with the interviewer archetypes (Ambitious, Safety-Focused, Research-M minded) to test different reasoning styles.
  • Expect proactive follow-ups and time-management prompts to mirror real interviews.
  • Review the post-interview scorecard and note actionable improvements for each dimension.
  • Practice across all round types to build balanced strengths and reduce blind spots.

Example Use Cases

  • Practice Product Sense for a frontier AI startup's consumer product idea.
  • Run a Strategy round for an AI inference platform competing in a crowded market.
  • Conduct an Analytical round focusing on NSMs and tree-based metrics for a data product.
  • Tackle a Technical round on scalable model deployment and system design questions.
  • Complete a Behavioral round about cross-functional collaboration at an AI company.

Frequently Asked Questions

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