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mom-test

wondelai/skills

Talk to customers without leading them using Mom Test rules: focus on their life, ask about specifics in the past, and listen more.

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What is mom-test?

The Mom Test is a framework for conducting customer interviews that extract truth instead of false validation. Use it when validating ideas, designing customer discovery, preparing interview scripts, or interpreting feedback to avoid confirmation bias and false positives.

Score conversations against seven diagnostic criteria (past behavior, concrete facts, real commitments, listening ratio, belief updates) to identify leading questions and false validation
Distinguish good questions anchored in observable past behavior from bad questions that ask people to predict the future or evaluate hypotheticals
Deflect compliments, fluff, and feature requests in real time to extract signal from noise instead of accepting fool's gold validation
Extract commitment and advancement from each conversation—securing time, reputation, or money stakes rather than polite zombie leads
Apply domain-specific question patterns for idea validation, feature prioritization, pricing research, problem validation, market sizing, and competitive analysis

How to install mom-test

npx skills add https://github.com/wondelai/skills --skill mom-test

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How to use mom-test

1.Before an interview, draft your questions and score them: do they ask about past behavior or future hypotheticals? Do they mention your idea or focus on their life?
2.During the conversation, apply the three core rules: talk about their life not your idea, ask about specifics in the past, and talk less (aim for 80% them speaking)
3.When you hear a compliment or feature request, deflect immediately and return to concrete facts: 'Thanks—what does your current process look like?' or 'When did you last need that?'
4.At the end, secure a real commitment (time, reputation, or money) or a clear rejection—avoid polite zombie leads with no next step
5.After the interview, score the conversation 1-7 using the Quick Diagnostic: name the failing rows and apply the specific fix for each

Use cases

Good for

Validate a new product idea by asking about the last time someone faced the problem, rather than pitching the solution and asking 'would you buy this?'
Prioritize features by discovering what people actually do versus what they say, through specific past-behavior walkthroughs
Design a customer-discovery interview script that avoids leading questions and anchors in concrete facts instead of hypotheticals
Interpret ambiguous customer feedback by deflecting compliments and digging into the underlying job or motivation behind feature requests
Prepare user-research questions that reveal real budgets, existing alternatives, and decision-making behavior instead of opinions

Who it's for

Product managers validating ideas and prioritizing features
Founders and entrepreneurs conducting customer discovery
UX researchers designing interview scripts and interpreting feedback
Anyone conducting customer interviews who wants to avoid confirmation bias and false validation

mom-test FAQ

Why does the Mom Test focus on past behavior instead of asking what people would do in the future?

People are unreliable predictors of their own future behavior. Opinions and hypotheticals are worthless; only past behavior can genuinely inform product decisions because it can't be rationalized away.

What's the difference between a good question and a bad question?

Bad questions ask people to predict the future ('Would you buy this?'), evaluate hypothetical products, or confirm your assumptions—they always get a yes. Good questions anchor in observable past behavior: 'How are you dealing with this problem today?' or 'What have you tried before and why did you stop?'

How do I handle compliments and feature requests in a customer conversation?

Deflect immediately and return to concrete facts. When someone says 'That's a great idea!' respond with 'Thanks—what part of your current workflow would this replace?' When they request a feature, dig into motivation: 'Why do you want that? Can you show me the last time you needed it?'

What does a 'zombie lead' mean and how do I avoid it?

A zombie lead is someone who is polite and encouraging but never commits to anything—no time, reputation, or money stake. End every conversation with a clear advance (a specific next step) or a clear rejection. Politeness without commitment is a wasted conversation.

How do I score a customer conversation?

Use the Quick Diagnostic: 1 point per row that passes (past behavior, concrete facts, real commitment, 80%+ listening, belief updates). Score 6-7 = strong signal; 4-5 = some signal but leaking into hypotheticals; ≤3 = a pitch in disguise with no learning.

Full instructions (SKILL.md)

Source of truth, from wondelai/skills.

name: mom-test description: 'Talk to customers without leading them using Mom Test rules: discuss their life not your idea, ask about specifics in the past, and talk less. Use when the user mentions "customer interviews", "validate my idea", "users say they want it but dont buy", "leading questions", "The Mom Test", "customer feedback bias", or "interview script". Also trigger when preparing user-research questions, interpreting ambiguous feedback, or designing customer-discovery that avoids false positives. Covers commitment and advancement, avoiding compliments, and extracting signal from noise. For product-market fit, see jobs-to-be-done. For rapid prototype testing, see design-sprint.' license: MIT metadata: author: wondelai version: "1.4.0"

The Mom Test Framework

Framework for customer conversations that won't lead you astray, based on a fundamental truth: everyone is lying to you -- not maliciously, but because you're asking the wrong questions. The Mom Test provides rules for asking questions so good that even your mom can't lie to you.

Core Principle

Good customer conversations are about their life, not your idea. The moment you mention what you're building, people switch from sharing truth to performing politeness. Talk about their problems, their lives, and their existing behavior instead of pitching, and ask about specifics in the past, not hypotheticals about the future. Above all, talk less and listen more.

Scoring

Goal: 7/7. Score a conversation (or interview plan) by the seven-row Quick Diagnostic below: 1 point per row that passes.

6-7 = focused on their life and past behavior, concrete facts captured, a real commitment (time/reputation/money) secured, they talked 80%+, and beliefs got updated.
4-5 = some past-behavior facts but leaking into hypotheticals, compliments accepted as signal, or ending without an ask.
<=3 = a pitch in disguise: leading questions, opinions and fluff, a polite zombie lead, no learning.

Always state the current score out of 7, name the failing diagnostic rows, and give the specific fix for each.

Framework Sections

1. The Mom Test Rules

Core concept: Three rules that make it impossible for even your most supportive loved ones to give you false validation, shifting conversations from opinion-gathering to fact-finding.

Why it works: People are unreliable predictors of their own future behavior, so opinions are worthless. Past behavior is the only reliable data and can genuinely inform product decisions.

Key insights:

Rule 1: Talk about their life, not your idea -- never mention your solution until the end, if at all
Rule 2: Ask about specifics in the past, not generics or hypotheticals about the future
Rule 3: Talk less, listen more -- aim for them to speak 80% of the time
A question fails the Mom Test if the answer is always "yes" regardless of whether the business will succeed
Good questions could potentially destroy your currently imagined business

Product applications:

Context	Application	Example
Idea validation	Ask about the problem, never the solution	"Tell me about the last time you tried to [problem area]" not "Would you use an app that does X?"
Feature prioritization	Discover what people do vs. what they say	"Walk me through how you handled this last week"
Pricing research	Anchor to existing spending behavior	"What are you currently paying to solve this?" not "Would you pay $X?"

Copy patterns:

"Tell me about the last time you..."
"What else have you tried?"
"Why does that bother you?"

See: references/question-patterns.md when drafting an interview script -- a 5-tier question hierarchy, domain-specific question banks (SaaS/consumer/marketplace), and four formulation exercises.

2. Good vs Bad Questions

Core concept: Most interview questions are broken because they ask people to predict the future, evaluate hypothetical products, or confirm your assumptions. Good questions anchor in observable past behavior and extract concrete facts.

Why it works: Asking "would you buy this?" is like asking "will you go to the gym next week?" -- the answer is always yes, the follow-through rarely there. Behavior that already happened can't be rationalized away.

Key insights:

Bad: "Do you think it's a good idea?" -- always gets a yes
Bad: "Would you buy a product that does X?" / "How much would you pay?" -- hypothetical, anchored to please you
Good: "How are you dealing with this problem today?" -- reveals actual behavior
Good: "What have you tried before and why did you stop?" -- reveals past decisions
Good: "Where does the money come from for solutions like this?" -- reveals real budgets
The scariest questions -- ones with the power to change what you're building -- produce the most useful data

Product applications:

Context	Application	Example
Problem validation	Confirm the problem exists and matters	"When did this last come up? What did you do? What didn't work?"
Market sizing	Check if enough people share the problem	"Who else in your industry deals with this? How do they handle it?"
Competitive analysis	Find real alternatives already in use	"What tools/processes do you currently use for this?"

Copy patterns:

"What's the hardest part about [doing this thing]?"
"How often does this come up?"
"Walk me through what happened the last time this came up"

3. Avoiding Compliments and Opinions

Core concept: Three types of bad data feel like progress but actively mislead: compliments ("That's a great idea!"), fluff (hypotheticals, maybes, future promises), and ideas (feature requests disconnected from real problems). Deflecting these and digging for truth is the core skill.

Why it works: Compliments are the fool's gold of customer development -- they feel amazing but contain zero information about whether anyone will pay or use the product. Only specifics about real past behavior and genuine commitments provide signal.

Key insights:

Compliments: deflect immediately and return to concrete facts about how they handle the problem today
Fluff: generic claims ("I usually," "I always," "I would never") are worthless without a specific instance
Ideas: dig into the motivation behind every feature request -- what's driving it, when they last needed it
Fishing for compliments ("Don't you think this would be useful?") is unconscious validation-seeking
Symptom of a bad conversation: you walk away feeling great but with no concrete facts or commitments

Product applications:

Context	Application	Example
Post-demo feedback	Deflect "this looks awesome"	"Thanks! What part of your current workflow would this replace?"
Feature requests	Dig for the underlying job	"Why do you want that? Can you show me the last time you needed it?"
Investor conversations	Separate encouragement from interest	Ask for customer intros, not "great idea" feedback

Copy patterns:

"Thanks, but to make sure I'm not wasting your time -- what does your current process look like?"
"When you say you'd 'definitely' use this, what would you stop using?"
"That's a great feature idea -- what problem would it solve for you specifically?"

See: references/avoiding-bad-data.md when a conversation feels good but yields no facts -- how to spot and deflect the three bad-data types (compliments, fluff, ideas) in real time.

4. Commitment and Advancement

Core concept: The currency of a customer conversation is commitment, not compliments. End every conversation with a clear advance toward adoption or a clear rejection -- the worst outcome is a "zombie lead" who is polite but never commits.

Why it works: Saying "I'd definitely buy that" costs nothing; offering an intro, a deposit, or a pilot invests something real. Commitment closes the dangerous gap between what people say and what they do.

Key insights:

Commitment currencies: time (meeting, trial), reputation (intro, testimonial), money (deposit, pre-order, letter of intent)
Advancing moves the relationship toward a sale; spinning wheels produces pleasant, useless meetings
Know your "ask" before the meeting -- the minimum commitment that proves this is real
A "no" is more valuable than a "maybe" -- you can learn from it and move on
If they won't give you their time, they definitely won't give you their money

Product applications:

Context	Application	Example
Early validation	Request a commitment that tests interest	"Can I follow up with a prototype next week for 15 minutes of your time?"
B2B sales	Advance toward the decision-maker	"Could you introduce me to the person who handles the budget for this?"
Pre-launch	Collect pre-orders or letters of intent	"Launching in 8 weeks -- want to join the first cohort at 40% off?"

Copy patterns:

"Who else should I talk to about this?"
"Would you be willing to try a prototype next week?"
"If I built this, would you be willing to pilot it for 30 days?"

See: references/commitment-advancement.md when a conversation ends without a commitment -- the currency ladder (time/reputation/money) and scripts for advancing instead of spinning wheels.

5. Finding Conversations

Core concept: The best customer conversations happen casually -- warm intros, industry events, online communities, coffee. Formal "customer interview" framing triggers performance mode; casual framing produces honest data.

Why it works: "Can I interview you about your problems?" makes people polished and guarded; "I'm trying to learn about the industry -- can I buy you coffee?" makes them open up. The framing determines the quality of the data.

Key insights:

Cold outreach: keep it short, lead with their expertise, don't pitch
Warm intros are the best source -- one well-connected advisor can open dozens of doors
Go where customers already gather: industry events, meetups, online communities (participate genuinely first)
"I'm trying to learn" beats "I'm doing customer research"
Use the five-part structure for getting meetings: vision / framing / weakness / pedestal / ask

Product applications:

Context	Application	Example
Pre-idea exploration	Immerse in the target community	3 industry events and 20 casual conversations before writing code
B2B prospecting	Warm intros through advisors	"Our advisor [Name] suggested I ask how you handle [problem area]"
Consumer research	Intercept at the point of behavior	Talk to people in line at the store, the gym, the coworking space

Copy patterns:

"I'm researching how [industry] handles [problem] -- could I learn from your experience over a 15-minute coffee?"
"[Mutual contact] suggested I talk to you because you know a lot about [area]"
"I'm not trying to sell anything -- I'm just trying to understand the space"

Ethical boundary: Never disguise a sales call as a learning conversation -- if you already have a product and are selling, be transparent.

See: references/finding-conversations.md when you need to source interviews -- cold vs warm outreach templates, the five-part meeting ask (vision/framing/weakness/pedestal/ask), and how to keep it casual.

6. Processing and Learning

Core concept: Conversations are only useful if processed: distill raw notes into beliefs, update them regularly, and share with your team. Without a system you'll cherry-pick quotes that confirm your biases.

Why it works: Memory is biased toward recent and emotionally charged information, so teams selectively remember confirming data. Processing as a team prevents any one person's bias from dominating the narrative.

Key insights:

Take notes during or immediately after -- never rely on memory
Separate facts (what they said and did) from interpretations (what you think it means)
Share raw notes with your team, not filtered summaries
Update your three key beliefs after each batch: the problem, the customer segment, the solution
Stop talking and start building when conversations start repeating
Use a simple spreadsheet: who, date, key quotes, facts, commitments, belief changes

Product applications:

Context	Application	Example
Team alignment	Review notes together weekly	5 conversations per week reviewed as a team; belief board updated
Pivot decisions	Track evidence against core beliefs	8 of 10 conversations reveal a different problem than expected -- pivot
Feature validation	Count unprompted mentions	A problem named by 7 of 10 people is real; 1 of 10 might not be

Copy patterns:

"Our current belief is X -- here's what confirms it and what challenges it"
"We've heard this from N of M people -- is that enough signal?"
"Time to stop talking and build -- conversations are repeating"

See: references/processing-learning.md after a batch of interviews -- the notes-to-beliefs spreadsheet template, team-review cadence, and signals that it's time to stop talking and build.

Common Mistakes

Mistake	Why It Fails	Fix
Pitching your idea instead of asking about their life	Triggers politeness; produces compliments, not facts	Don't mention your idea until the very end, if at all
Asking "would you buy this?"	Hypothetical yeses cost nothing	Ask what they've already done: "How much are you spending on this now?"
Accepting compliments as validation	"Great idea!" carries zero information about behavior	Deflect immediately: "Thanks -- but what are you doing about this today?"
Talking too much	You learn while listening, not talking	They should talk 80%+ of the time
No clear ask at the end	Produces zombie leads that go nowhere	Know your advance before the meeting: trial, intro, pre-order
Running formal "interview" sessions	Triggers performance mode and filtered answers	Keep it casual: coffee, hallway conversations, Slack DMs
Not processing notes as a team	Individual bias filters data into confirmation	Share raw notes weekly; update shared beliefs together

Quick Diagnostic

Question	If No	Action
Did the conversation focus on their life and past behavior, not your idea?	You ran a pitch, not a Mom Test conversation	Redo with zero mention of your solution
Did you get concrete facts about what they've already done?	You collected opinions and hypotheticals	Ask about the last time the problem occurred and what they did
Did they give a commitment (time, reputation, or money)?	Likely a zombie lead -- polite but not interested	Ask for a specific next step: trial, intro, or pre-order
Did they do most of the talking?	You talked too much and learned too little	Practice silence; let awkward pauses work for you
Did you learn something that could change what you're building?	You asked safe, confirming questions	Ask the scary questions you've been avoiding
Did you update your beliefs based on the conversation?	You're collecting data but not learning	Review notes with the team; update problem/segment/solution beliefs
Can you summarize the key facts (not opinions)?	Poor notes, or opinions confused with facts	Separate facts from interpretations immediately after

See: references/case-studies.md when you want to see the rules applied end-to-end -- realistic SaaS, consumer, B2B, and marketplace interviews scored against this diagnostic.

About the Author

Rob Fitzpatrick is an entrepreneur and educator who founded multiple venture-backed startups and learned the hard way that most customer conversations produce misleading feedback. The Mom Test (2013) distills his evidence-based approach, has been translated into 20+ languages, and is required reading at accelerators including Y Combinator and Techstars. He also wrote The Workshop Survival Guide and Write Useful Books.