skills/product-sense-interview-answer/SKILL.md
Structure a spoken PM product-sense answer with assumptions, segmentation, pain-point prioritization, and MVP tradeoffs. Use when practicing design, improve, or build-next interview questions.
npx skillsauth add deanpeters/Product-Manager-Skills product-sense-interview-answerInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Help PM candidates and interview coaches structure product-sense answers that sound strong out loud, not just on paper. Use this when practicing prompts like "How would you improve X?", "Design a product for Y", or "What would you build next for Z?"
This is not a memorize-and-recite script. It is a reasoning scaffold that prevents solution-jumping, forces real prioritization, and leaves the interviewer with a clean story they can follow.
Works best with: The interview prompt you're practicing (e.g., 'How would you improve X?', 'Design a product for Y'). Also useful: The company/role you're interviewing for and how much time the answer gets.
Anything supplied with the invocation itself — text after the skill name, a pasted context dump, or an appended ARGUMENTS: line — counts as answers already given. Use it and skip whatever it covers; don't re-ask.
Arriving empty-handed? That works too. The skill asks for the prompt, then walks the reasoning scaffold with you out loud.
Example invocation: Practice this: 'How would you improve Google Maps for commuters?' — 25-minute answer, L5 PM loop.
Strong product-sense answers do more than generate ideas. Interviewers are usually testing whether you can:
The order matters. If you skip from prompt to feature ideas, your answer sounds clever but ungrounded. If you establish the user, goal, and pain first, your solution feels earned.
Use template.md as the working structure.
Start by surfacing the two ambiguities that change the answer most. Good clarifiers usually narrow:
If the interviewer does not answer, state your assumptions and move on. The goal is to unblock the rest of the answer, not to turn the interview into requirements gathering.
Quality bar: Ask questions that materially change the solution. "Are we talking mobile or desktop?" matters less than "Are we optimizing for viewers, creators, or advertisers?"
Explain why the space matters now.
For the market view, cover:
If a company is named, then add:
End this section with a one-line thesis. That thesis should make the rest of the answer feel inevitable.
Quality bar: Use qualitative signals unless you know the numbers cold. Fake precision is worse than grounded judgment.
Write one sentence in this format:
Help [user] [achieve outcome], so that [broader impact].
Then describe what success looks like for the user in observable terms.
Good: "Help beginner YouTube learners find content they are glad they watched, so that the platform becomes an intentional learning destination."
Bad: "Build a personalized AI learning path feature." That is a solution disguised as a goal.
Do not jump straight to persona. First identify the ecosystem players, then choose the player you want to serve. After that, choose two segmentation dimensions that actually change needs.
Good segmentation dimensions usually change:
Weak dimensions are often demographic cuts that do not change the product meaningfully.
After choosing your target segment:
Break the user journey into 4-6 stages. Then list the frictions across that journey.
Prioritize the top pain point using:
This is the fulcrum of the entire answer. If the pain point is vague or weak, the solution section becomes generic.
Quality bar: Pain points should describe user friction, not missing features. "No structured progression after each video" is a pain. "No AI learning path" is already a solution.
List three distinct solutions. They should solve the same pain in different ways, not represent three feature line-items inside one idea.
Evaluate each option on:
Then choose one MVP and specify:
Close with a one-sentence recap that names:
That final sentence is what the interviewer should remember.
See examples/improve-youtube.md for a full worked example.
What makes it strong:
A strong answer to this prompt would explicitly state a startup assumption if no company is named, prioritize people who live alone, and choose the wake-up problem before discussing dispatch or smart-home integrations.
What makes this example useful:
"I would improve YouTube by adding AI summaries, better recommendations, creator analytics, and a study mode."
Why this fails:
This kind of answer can sound energetic in the moment, but it signals weak PM judgment.
template.mdexamples/improve-youtube.mdskills/problem-statement/SKILL.mdskills/proto-persona/SKILL.mdskills/customer-journey-map/SKILL.mdskills/opportunity-solution-tree/SKILL.mddata-ai
Mine public reviews, app stores, and forums for unmet needs, competitor weaknesses, and switching triggers — with quoted evidence. Use when you want customer voice without waiting on interviews.
development
Build an evidence-cited SWOT of one company — yours or a competitor's — from public sources, ending with the S-O/W-T crossings. Use for strategy reviews, board prep, or competitor depth.
testing
Track competitor pricing and packaging as a diffable time series. Use when monitoring tiers, gates, limits, and price moves on a monthly or quarterly cadence.
documentation
Read an industry's structure through Porter's Five Forces with documented signals per rating, ending at the profit pool. Use when weighing market entry or when margins erode and nobody can say why.