Agent skill
interview-prep
Pre-interview preparation for PM job interviews (Product Sense, Execution, Behavioral)
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/interview-prep-timothynguyen04-pmos
SKILL.md
Note: This skill is for PM career interviews (job interviews). For preparing to conduct user research interviews, see /interview-guide.
Interview Prep Skill
Prepare effectively for PM job interviews. Master Product Sense, Product Execution, Behavioral, and other interview types with structured research and practice strategies.
Quick Start
/interview-prep
Preparing for a PM job interview? I'll help you get ready.
Tell me:
1. What company and role? (e.g., "Senior PM at Stripe")
2. What interview type? (Product Sense / Execution / Behavioral / Design / Technical)
3. When is the interview? (so I can scope the prep plan)
I'll generate a research checklist, framework cheat sheets, practice questions,
and a day-of game plan tailored to that company.
Say "mock interview" and I'll run you through a live practice round with feedback.
For user research interview prep, use /interview-guide instead.
When to Use This Skill
- 1-2 weeks before PM job interviews
- Preparing for specific interview rounds (Product Sense, Execution, Design, etc.)
- Before take-home assignments or case studies
- Mock interview preparation
Interview Types Overview
1. Product Sense Interview
What they test: Creativity, data acumen, user understanding, prioritization
Question types:
- Product improvement ("How would you improve Instagram Stories?")
- Product growth ("How would you grow Spotify users?")
- Product launch ("Should Netflix launch gaming?")
- Product design ("Design a product for X")
- Product pricing ("How would you price YouTube Premium?")
2. Product Execution Interview
What they test: Metrics definition, goal-setting, analytical rigor, execution depth
Core skills:
- Define north star metrics
- Set measurable success criteria
- Analyze root causes of metric changes
- Think at scale (especially for Meta/Google)
3. Behavioral/Leadership Interview
What they test: Past experience, collaboration, decision-making, conflict resolution
Common frameworks:
- STAR method (Situation, Task, Action, Result)
- Leadership principles (Amazon's 16, etc.)
4. Technical Interview
What they test: System design, API knowledge, data structures, SQL
5. Product Design Interview
What they test: User-centric thinking, wireframing, usability, design critique
Workflow
Step 1: Research the Company (2-3 hours, 1 week before)
Company Research Checklist:
**Product Usage:**
- [ ] Use their product daily for 1+ week
- [ ] Note friction points, delights, questions
- [ ] Track which features you use most/least
- [ ] Screenshot bugs or UX issues
**Business Model:**
- [ ] How do they make money? (ads, subs, marketplace take rate, etc.)
- [ ] Who are their customers? (B2C, B2B, B2B2C?)
- [ ] Revenue: [Estimate from earnings reports or TechCrunch]
- [ ] Growth stage: Early/Growth/Mature/Declining?
**Competitive Landscape:**
- [ ] Top 3 competitors
- [ ] What's their competitive moat? (Network effects, switching costs, etc.)
- [ ] Recent competitive threats or wins
**Recent News:**
- [ ] Last 3 product launches (TechCrunch, company blog)
- [ ] Recent controversies or challenges
- [ ] Earnings call highlights (if public company)
**Company Culture:**
- [ ] Read Glassdoor reviews (especially PM reviews)
- [ ] LinkedIn: Connect with current PMs, ask for coffee chat
- [ ] Company blog: What do they value? (Move fast, user-first, data-driven, etc.)
Pro tip: Create a one-pager summary of all this research to review 30 min before your interview.
Step 2: Prep by Interview Type
Product Sense Prep (3-4 hours)
Framework Practice:
The 5-Step Product Sense Framework:
1. **Clarify** (2 min)
- Understand the question
- Ask clarifying questions
- Define success criteria
2. **User Segments** (3 min)
- Who are the users?
- Pick your target segment
- Explain why (size, pain, willingness to pay)
3. **Pain Points** (5 min)
- What problems does this segment face?
- Prioritize by severity + frequency
- Pick top 1-2 to solve
4. **Solutions** (10 min)
- Brainstorm 3-5 solutions
- Evaluate pros/cons
- Prioritize (impact vs. effort)
5. **Success Metrics** (5 min)
- Define north star metric
- Add 2-3 supporting metrics
- Set success criteria
Practice Questions:
- Pick 5 products you use daily
- For each, practice: "How would you improve [Product]?"
- Time yourself: 25 minutes per answer
- Record yourself (audio/video) and review
Company-Specific Twists:
- Meta/Facebook: Think at global scale, diverse user bases
- Google: Data-driven, A/B testing mindset
- Amazon: Customer obsession, work backwards from user
- Apple: Design elegance, simplicity, ecosystem thinking
CIRCLES Framework (for Product Design Questions)
The CIRCLES Method - 7 Steps for Product Design:
1. **Comprehend** the situation
- What is the product? Who is the user?
- Restate the question to confirm understanding
- Ask: "Am I designing for mobile, web, or both?"
2. **Identify** the customer
- List 2-3 user segments
- Pick one to focus on (explain why)
- Describe their demographics, behaviors, goals
3. **Report** customer needs
- List 5-7 needs/pain points for your chosen segment
- Prioritize by severity and frequency
- Pick top 2-3 to solve
4. **Cut** through prioritization
- Use a 2x2 matrix: Impact vs. Effort
- Evaluate each need against business goals
- Select the #1 need to address
5. **List** solutions
- Brainstorm 3-5 solutions for the top need
- Be creative -- don't just copy competitors
- Include at least one "bold" solution
6. **Evaluate** trade-offs
- Pros/cons for each solution
- Technical feasibility, time to build, scalability
- Pick the winning solution with clear rationale
7. **Summarize** your recommendation
- Restate: user, need, solution
- Success metrics for the solution
- Risks and how to mitigate them
When to use CIRCLES: "Design a product for...", "How would you build...", "Create a new feature for..."
Product Execution Prep (2-3 hours)
Core Skills to Master:
1. Metrics Definition
For any feature, define:
**North Star Metric:**
- The one metric that best captures value delivered
- Example: DAU/MAU for engagement, GMV for marketplace
**Supporting Metrics:**
- Metric 1: [Leading indicator]
- Metric 2: [Usage depth]
- Metric 3: [Business impact]
**Guardrail Metrics:**
- What you WON'T sacrifice
- Example: User satisfaction > 4.0, Latency < 200ms
2. Root Cause Analysis
"Metric X dropped by Y%. Why?"
Step 1: Clarify the data
- Which user segments affected?
- Which platforms/geographies?
- Time period?
Step 2: Hypotheses
- Internal changes (product, bug, experiment)
- External factors (seasonality, competition, news)
- Data issues (tracking broken, definition changed)
Step 3: Investigate
- Check recent launches
- Segment the data
- Compare to historical patterns
Step 4: Recommend action
- If bug: Fix immediately
- If experiment: Kill or iterate
- If external: Monitor or adapt strategy
3. AARM Framework (for Metrics Questions)
AARM - 4 Steps for Any Metrics Question:
1. **Acquire** - How do users find the product?
- Acquisition channels (organic, paid, referral, viral)
- Top-of-funnel metrics: impressions, clicks, signups
- Cost per acquisition by channel
2. **Activate** - How do users get to the "aha moment"?
- Activation funnel: signup → onboarding → first value
- Time to value, completion rates at each step
- What defines an "activated" user?
3. **Retain** - How do users keep coming back?
- Retention curves: D1, D7, D30
- Engagement frequency and depth
- Churn signals and re-engagement triggers
4. **Monetize** - How does the product make money?
- Revenue per user (ARPU), lifetime value (LTV)
- Conversion to paid, expansion revenue
- LTV:CAC ratio, payback period
When to use AARM: "What metrics would you track for...", "How would you measure success for...", "A metric dropped X%, diagnose it"
Practice:
- Find 3 recent product launches (TechCrunch, Product Hunt)
- For each, define: North star + 3 supporting metrics + 2 guardrails
- Practice explaining your reasoning out loud
Company-Type Specific Prep
Tailor preparation based on the company category:
AI/ML Companies (OpenAI, Anthropic, Midjourney):
- Emphasize: AI product trade-offs (accuracy vs latency, safety vs capability), evaluation methodology, prompt engineering as product design
- Study: Their model capabilities, API pricing, developer ecosystem
- Unique angles: "How would you measure if an AI feature is actually helping users vs just impressive?"
Marketplaces (Airbnb, Uber, DoorDash):
- Emphasize: Supply vs demand balancing, chicken-and-egg problems, trust & safety, unit economics
- Study: Their take rate, geographic expansion strategy, supply acquisition
- Unique angles: "How would you design for the supply side without hurting demand experience?"
Enterprise SaaS (Salesforce, Slack, Notion):
- Emphasize: Multi-persona buying (end user vs buyer vs admin), seat expansion, enterprise security/compliance
- Study: Their pricing tiers, integration ecosystem, competitive positioning
- Unique angles: "How would you balance individual user experience with admin control needs?"
Consumer Social (Meta, TikTok, Snap):
- Emphasize: Engagement loops, creator vs consumer dynamics, content ranking, growth mechanics
- Study: Their DAU/MAU ratios, monetization model, content moderation approach
- Unique angles: "How would you measure healthy engagement vs addictive patterns?"
Fintech (Stripe, Square, Plaid):
- Emphasize: Trust, compliance, fraud prevention, API-first thinking, developer experience
- Study: Their regulatory environment, payment flow, risk management
- Unique angles: "How would you design for both the merchant and the end consumer?"
Behavioral Prep (2 hours)
STAR Method Template:
Situation: [Set context in 1-2 sentences]
Task: [What needed to be done?]
Action: [What YOU specifically did - use "I" not "we"]
Result: [Quantified outcome + learning]
Top 10 Behavioral Questions:
- Tell me about a time you failed
- Describe a conflict with a teammate and how you resolved it
- Tell me about your most impactful product
- How do you prioritize features?
- Describe a time you influenced without authority
- Tell me about a time you disagreed with your manager
- How do you handle ambiguity?
- Describe a time you had to make a decision with incomplete data
- Tell me about a time you had to say no to a stakeholder
- Why product management? Why this company?
Prep Strategy:
- Write out 5-7 stories from your experience
- Each story should be usable for 2-3 different questions
- Focus on recent work (last 2 years)
- Include failures and learnings (not just wins)
- Quantify results wherever possible
Specific Guidance: "Tell me about a time you used data to make a decision"
This is one of the most common behavioral questions. Structure your answer:
1. **Set the stage** (15 sec)
- "We were deciding whether to [build X / launch Y / kill Z]"
- Mention the stakes: revenue, users, team resources
2. **Describe the data you gathered** (30 sec)
- What data sources? (analytics, surveys, A/B tests, user interviews)
- What was the key metric or insight?
- Use exact numbers: "Conversion was 3.2%, below our 5% threshold"
3. **Show the analysis** (30 sec)
- How did you interpret the data?
- What did the data suggest vs. what your gut said?
- Any conflicting signals? How did you resolve them?
4. **The decision and outcome** (30 sec)
- What did you decide? Why?
- Quantified result: "This led to a 15% increase in retention"
- What did you learn about using data?
Red flags to avoid:
- Vague data: "We looked at some metrics" (which ones?)
- No conflict: The best stories involve data surprising you
- No learning: Always end with what you'd do differently
Mock Interview Mode
If the PM says "mock interview", enter this mode:
-
Ask for setup:
- "What interview type? (Product Sense / Execution / Behavioral / Design)"
- "What company? (I'll tailor the question)"
- "Timer on or off? (Real interviews are 25-35 min)"
-
Present a question:
- Pick a realistic question for the company and interview type
- Say: "Your time starts now. Take a moment to structure your thoughts, then walk me through your answer."
-
Wait for their full answer. Do not interrupt. Let them finish.
-
Provide structured feedback:
## Mock Interview Feedback
**Question:** [The question asked]
**Time taken:** [Estimate]
### Scores (1-5)
| Dimension | Score | Notes |
|-----------|-------|-------|
| Framework Usage | X/5 | Did they use a clear structure? |
| Specificity | X/5 | Real examples, data, concrete details? |
| Creativity | X/5 | Did their answer stand out? Unique insights? |
| Communication Clarity | X/5 | Concise, easy to follow, no rambling? |
| Product Sense | X/5 | User empathy, business understanding? |
### What Went Well
- [Specific strength 1]
- [Specific strength 2]
### What to Improve
- [Specific improvement 1 with how to fix it]
- [Specific improvement 2 with how to fix it]
### Model Answer Outline
Here's how a strong candidate might structure this:
- [Key point 1]
- [Key point 2]
- [Key point 3]
- Ask: "Want to try another question, or work on one of the weak areas?"
Step 3: Mock Interviews (1 week before)
Mock Interview Checklist:
**Find a partner:**
- [ ] PM friend or mentor
- [ ] Career coach or interviewer
- [ ] Pramp, Exponent, or IGotAnOffer platforms
**Structure the mock:**
- [ ] Pick interview type (Product Sense, Execution, etc.)
- [ ] Set a timer (25-30 min)
- [ ] Ask partner to interrupt/probe like real interviewer
- [ ] Record the session
**Post-mock debrief:**
- [ ] What went well?
- [ ] What felt rushed or unclear?
- [ ] Did I clarify assumptions?
- [ ] Were my metrics specific enough?
- [ ] Did I structure my answer before diving in?
**Iterate:**
- [ ] Do 3-5 mocks minimum
- [ ] Focus on weak areas each time
- [ ] Get faster at structuring answers
Step 4: Day-Before Prep (1 hour)
Final Prep Checklist:
**Review your research:**
- [ ] Re-read company one-pager
- [ ] Review recent product launches
- [ ] Refresh on company metrics (if public)
**Prepare questions to ask:**
- [ ] About the role: "What does success look like in the first 90 days?"
- [ ] About the team: "What's the biggest challenge the team is facing?"
- [ ] About the product: "What's the product vision for the next year?"
- [ ] About culture: "How does the team balance speed vs. quality?"
**Logistics:**
- [ ] Test Zoom/tech setup
- [ ] Prepare quiet space (close door, mute phone)
- [ ] Have water nearby
- [ ] Pen + paper ready for notes/sketching
- [ ] Resume printed (if in-person)
**Mindset:**
- [ ] Get good sleep (8+ hours)
- [ ] Light exercise (walk, yoga)
- [ ] Review framework cheat sheet (15 min)
- [ ] Don't cram new content day-of
Step 5: Interview Day (30 min before)
Pre-Interview Routine:
**T-30 min:**
- [ ] Review company one-pager (5 min)
- [ ] Review framework cheat sheets (5 min)
- [ ] Do 1 quick practice question out loud (10 min)
- [ ] Breathe, center yourself (5 min)
- [ ] Use bathroom, get water (5 min)
**T-5 min:**
- [ ] Join call early
- [ ] Check audio/video
- [ ] Have pen + paper ready
- [ ] Smile - set positive energy
**During interview:**
- [ ] Take notes on question
- [ ] Ask clarifying questions
- [ ] Structure before diving in
- [ ] Check time midway through
- [ ] Leave 2-3 min for questions
Output Format
# Interview Prep: [Company Name] - [Role]
**Interview Date:** [Date]
**Interview Type:** [Product Sense / Execution / Behavioral / etc.]
---
## Company Research Summary
**Product:** [1-sentence description]
**Business Model:** [How they make money]
**Recent News:** [3 bullet points]
**Competitors:** [Top 3]
**My Usage Notes:** [Friction points, delights, questions]
---
## Key Metrics to Know
- North Star: [Metric]
- Revenue: [Estimate]
- Growth Stage: [Early/Growth/Mature]
- User Base: [Size + segments]
---
## Interview Type Prep
### [Product Sense / Execution / Behavioral]
**Framework to use:** [5-step Product Sense, STAR, etc.]
**Practice questions completed:**
1. [Question 1] - [Time: X min] - [Rating: Good/Needs work]
2. [Question 2] - [Time: X min] - [Rating: Good/Needs work]
3. [Question 3] - [Time: X min] - [Rating: Good/Needs work]
**Weak areas to focus on:**
- [Area 1: e.g., "Need to be more specific on metrics"]
- [Area 2: e.g., "Clarify assumptions upfront"]
---
## Questions to Ask Interviewer
1. [Question about role]
2. [Question about team]
3. [Question about product]
4. [Question about culture]
---
## Day-Of Checklist
- [ ] Reviewed company one-pager
- [ ] Practiced 1 question out loud
- [ ] Tech setup tested
- [ ] Water + pen + paper ready
- [ ] Mindset: confident and curious
Pro Tips
- Structure before you speak: Take 30-60 seconds to outline your answer
- Think out loud: Let interviewer follow your thought process
- Ask clarifying questions: Shows you don't make assumptions
- Be specific on metrics: "Increase engagement" → "Increase DAU/MAU from 40% to 50%"
- Show trade-offs: "Option A is faster to build, but Option B has more long-term value"
- Use real data: "Based on Instagram having 2B users..." not "I assume they have a lot of users"
- Time management: Spend 5 min on problem definition, not 20 min on solutions
- Connect to company: "This aligns with Meta's mission of bringing people together"
Common Mistakes to Avoid
❌ Jumping to solutions without understanding the problem ✅ Spend time on user segments and pain points first
❌ Vague metrics ("improve engagement") ✅ Specific metrics with targets ("increase 7-day retention from 30% to 40%")
❌ Only considering one solution ✅ Generate 3+ options and evaluate trade-offs
❌ Ignoring the business ✅ Connect user value to business value (revenue, retention, virality)
❌ Not asking clarifying questions ✅ Ask about constraints, success criteria, user segments
❌ Going overtime ✅ Check time at 50% mark, wrap up with 2-3 min buffer
Resources
Practice Platforms:
- Exponent: Mock interviews + courses
- Pramp: Free peer mock interviews
- IGotAnOffer: Company-specific prep
- Product Alliance: Video courses
Reading:
- Cracking the PM Interview (Gayle McDowell)
- Decode and Conquer (Lewis Lin)
- Company blogs: Meta, Google, Amazon PM blogs
Aakash Gupta's Guides:
Improvement Loop: Connect to /interview-feedback
After each real interview, run /interview-feedback to debrief. Over time, this creates a feedback loop:
/interview-prepidentifies areas to practice -- you prepare- You do the interview
/interview-feedbackscores your performance on 5 dimensions- Scores inform what to focus on in
/interview-prepfor the next round
After 3+ debriefs, /interview-feedback shows trend data. Use this to target your prep: if "Specificity" is consistently low, /interview-prep should emphasize researching company metrics and practicing with numbers.
Output Quality Self-Check
Before delivering the prep plan, verify:
| Check | Criteria | Pass? |
|---|---|---|
| Company-specific | Research and questions are tailored to the target company, not generic | [ ] |
| Framework included | At least one relevant framework provided (5-Step, CIRCLES, AARM, STAR) | [ ] |
| Practice questions | At least 3 practice questions with timing guidance | [ ] |
| Metrics are specific | Example metrics use real numbers, not vague ("increase engagement") | [ ] |
| Checklist provided | Day-before and day-of checklists included | [ ] |
| Questions to ask | At least 3 thoughtful questions for the PM to ask the interviewer | [ ] |
| Weak areas identified | Specific areas to focus practice on, based on interview type | [ ] |
| Time-boxed | Prep plan is scoped to the available time before the interview | [ ] |
If any check fails, address it before delivering the output.
Remember: Great preparation beats natural talent. Put in the 10-15 hours of structured prep, and you'll walk in confident and ready to nail the interview.
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