mock-interviewer
Scannednpx machina-cli add skill aroyburman-codes/pm-skills/mock-interviewer --openclawMock Interviewer Skill
Run an interactive mock PM interview simulating real interview conditions at AI companies.
When to Use
- User says
/mock-interviewerto 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:
| Dimension | Score | Notes |
|---|---|---|
| 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:
| Dimension | Score | Notes |
|---|---|---|
| Strategic Framing | /4 | |
| Market & Competitive Analysis | /4 | |
| Option Generation | /4 | |
| Recommendation Quality | /4 | |
| Risk Awareness | /4 | |
| Communication & Structure | /4 |
For Analytical rounds:
| Dimension | Score | Notes |
|---|---|---|
| Problem Clarification | /4 | |
| Metric Selection (NSM + tree) | /4 | |
| Analytical Rigor | /4 | |
| Guardrail Awareness | /4 | |
| Trade-off Reasoning | /4 | |
| Communication & Structure | /4 |
For Technical rounds:
| Dimension | Score | Notes |
|---|---|---|
| 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:
| Dimension | Score | Notes |
|---|---|---|
| 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
- Step 1: Start a session with /mock-interviewer and optionally specify company, round type, difficulty, and duration.
- Step 2: Answer questions and engage with follow-ups as the interviewer probes and challenges you.
- 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.