/prd-v10-mom-test-interview
Apply Rob Fitzpatrick's Mom Test discipline to customer interviews during PRD v1.0 Market Adoption. Triggers on requests to interview customers, run discovery calls, validate ideas with users, or when user asks "how do I interview customers?", "Mom Test", "Rob Fitzpatrick",
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Apply Rob Fitzpatrick's Mom Test discipline to customer interviews during PRD v1.0 Market Adoption. Triggers on requests to interview customers, run discovery calls, validate ideas with users, or when user asks "how do I interview customers?", "Mom Test", "Rob Fitzpatrick",
SKILL.md
prd-v10-mom-test-interview.SKILL.mdname: prd-v10-mom-test-interview
description: >
Apply Rob Fitzpatrick's Mom Test discipline to customer interviews during PRD v1.0 Market
Adoption. Triggers on requests to interview customers, run discovery calls, validate ideas
with users, or when user asks "how do I interview customers?", "Mom Test", "Rob Fitzpatrick",
"customer interview", "user research without lies", "talking to humans", "discovery
conversation". Outputs CFD-* discovery entries with Mom Test-calibrated confidence.
context: fork
allowed-tools:
- Read
- Write
- Edit
- Glob
- Grep
execution_modes:
default: standard
supports: [quick, standard, deep]
Mom Test Interview Discipline
Position in workflow: v1.0 Continuous Discovery (Torres) → **v1.0 Mom Test Interview** → v1.0 Case Study Builder, Testimonial Collector
Execution Mode
Default is **standard**. See [`.claude/rules/08-skill-execution-modes.md`](../../rules/08-skill-execution-modes.md) for selection logic.
| Mode | What this skill produces | |------|--------------------------| | **quick** | Single interview prep + question list + CFD-* discovery entry post-interview | | **standard** | Interview prep + scripted opening + 5–7 questions + CFD-* entry with confidence calibration + Bad Data flagged | | **deep** | Multi-interview cohort plan + cross-interview synthesis + pattern flagging at 3-mention threshold + assumption-test handoff |
Framework: The Mom Test
From *The Mom Test: How to Talk to Customers and Learn If Your Business is a Good Idea When Everyone is Lying to You* (Rob Fitzpatrick, 2013). Premise: People lie to spare your feelings — especially about your business idea. Learn to ask questions that get truth.
The three Mom Test rules
1. **Talk about their life, not your idea.** Ask about how they actually live and work, not whether they'd like your hypothetical feature. 2. **Ask about specifics in the past, not generics or opinions about the future.** "Last time you faced X, what did you do?" beats "Would you use a tool that does Y?" 3. **Talk less and listen more.** If you're talking more than 30% of the time, you're pitching, not learning.
Three types of Bad Data to avoid
| Bad Data type | Example | Why it's bad | What to do | |---------------|---------|--------------|------------| | **Compliments** | "That sounds awesome!" | Politeness; not predictive | Discard; don't update confidence | | **Fluff** | "I usually..." "I would probably..." | Generics, future hypotheticals | Anchor to specifics: "When did you last do that?" | | **Ideas** | "You should add X" | Solution suggestions disguised as needs | Note as IDEA; ask "what would solving X enable you to do?" — get the underlying job |
Confidence calibration
Mom Test-disciplined interviews produce **higher-confidence CFD- entries** because the data is grounded in observed past behavior, not opinion. Confidence floors per P4:
- 2/5 — Generic statement ("I sometimes have this problem")
- 3/5 — Specific past instance ("Last Tuesday, I spent 2 hours doing X")
- 4/5 — Specific past instance + paid-money-for-a-workaround / hired-a-person / built-a-script ("I'm paying $30/mo for a tool that almost solves this")
- 5/5 — Repeated specific past instances + ongoing willingness to invest in better solution (commit > talk)
Consumes
- **ADO-BEACHHEAD-\* and PER-\* personas** — Defines the interview pool; non-segment interviews are still useful but get lower weight
- **Opportunity Solution Tree** (from prd-v10-continuous-discovery-torres) — Interview goals trace to specific opportunities being tested
- **CFD-\* prior research** — Patterns to validate or contradict
- **KPI-\* outcomes** — Why we're interviewing (anchor to what we're trying to move)
Produces
- **CFD-\* discovery interview entries** — One per interview, with confidence calibrated by Mom Test rules
- **CFD-\* pattern entries** — When 3+ interviews mention the same specific behavior, promote to a pattern entry with frequency
- **Bad Data inventory** — Compliments, fluff, ideas captured for context but not data
- **Discovery Tree updates** — Each interview either strengthens an opportunity, weakens an opportunity, or surfaces a new one
Execution
Step 1: Define the goal of this interview
Not "validate the idea." Specifically: which opportunity / assumption from the Opportunity Solution Tree is this interview testing?
| Goal | Example | |------|---------| | Validate opportunity exists | "Do beachhead pragmatists actually struggle with pricing-tier selection?" | | Test assumption | "Will users engage with a pricing wizard before signup?" | | Map workaround | "How are they solving this today?" | | Quantify pain | "How much time/money does this cost them currently?" |
Write the goal at the top of the interview notes.
Step 2: Prep the question list (Mom Test-shaped)
Use specifics-in-the-past form. Avoid hypotheticals.
| Bad question | Better (Mom Test) version | |--------------|---------------------------| | "Would you use a pricing wizard?" | "Walk me through how you picked the tier when you signed up." | | "Do you think analytics is important?" | "Tell me about the last time you looked at analytics." | | "What features do you wish we had?" | "Tell me about the last time the tool didn't do what you needed." | | "How would you describe X?" | "Tell me about the last time X came up at work." |
5–7 questions is plenty for a 25–30 minute interview. Leave room to follow threads.
Step 3: Run the interview
- **Open** with a short framing: who you are, why this conversation, no sales pitch
- **Ask the past-behavior questions**, listen, follow threads
- **Probe specifics** when they generalize: "When was that specifically?" / "What was the dollar / time cost?" / "What did you do next?"
- **Flag Bad Data internally**: when they compliment, smile and move on; when they fluff, anchor to specifics; when they suggest ideas, note as IDEA and dig for the underlying job
- **Talk less t
Read more
name: prd-v10-mom-test-interview description: > Apply Rob Fitzpatrick's Mom Test discipline to customer interviews during PRD v1.0 Market Adoption. Triggers on requests to interview customers, run discovery calls, validate ideas with users, or when user asks "how do I interview customers?", "Mom Test", "Rob Fitzpatrick", "customer interview", "user research without lies", "talking to humans", "discovery conversation". Outputs CFD-* discovery entries with Mom Test-calibrated confidence. context: fork allowed-tools: - Read - Write - Edit - Glob - Grep execution_modes: default: standard supports: [quick, standard, deep]
Mom Test Interview Discipline
Position in workflow: v1.0 Continuous Discovery (Torres) → **v1.0 Mom Test Interview** → v1.0 Case Study Builder, Testimonial Collector
Execution Mode
Default is **standard**. See [`.claude/rules/08-skill-execution-modes.md`](../../rules/08-skill-execution-modes.md) for selection logic.
| Mode | What this skill produces | |------|--------------------------| | **quick** | Single interview prep + question list + CFD-* discovery entry post-interview | | **standard** | Interview prep + scripted opening + 5–7 questions + CFD-* entry with confidence calibration + Bad Data flagged | | **deep** | Multi-interview cohort plan + cross-interview synthesis + pattern flagging at 3-mention threshold + assumption-test handoff |
Framework: The Mom Test
From *The Mom Test: How to Talk to Customers and Learn If Your Business is a Good Idea When Everyone is Lying to You* (Rob Fitzpatrick, 2013). Premise: People lie to spare your feelings — especially about your business idea. Learn to ask questions that get truth.
The three Mom Test rules
1. **Talk about their life, not your idea.** Ask about how they actually live and work, not whether they'd like your hypothetical feature. 2. **Ask about specifics in the past, not generics or opinions about the future.** "Last time you faced X, what did you do?" beats "Would you use a tool that does Y?" 3. **Talk less and listen more.** If you're talking more than 30% of the time, you're pitching, not learning.
Three types of Bad Data to avoid
| Bad Data type | Example | Why it's bad | What to do | |---------------|---------|--------------|------------| | **Compliments** | "That sounds awesome!" | Politeness; not predictive | Discard; don't update confidence | | **Fluff** | "I usually..." "I would probably..." | Generics, future hypotheticals | Anchor to specifics: "When did you last do that?" | | **Ideas** | "You should add X" | Solution suggestions disguised as needs | Note as IDEA; ask "what would solving X enable you to do?" — get the underlying job |
Confidence calibration
Mom Test-disciplined interviews produce **higher-confidence CFD- entries** because the data is grounded in observed past behavior, not opinion. Confidence floors per P4:
- 2/5 — Generic statement ("I sometimes have this problem")
- 3/5 — Specific past instance ("Last Tuesday, I spent 2 hours doing X")
- 4/5 — Specific past instance + paid-money-for-a-workaround / hired-a-person / built-a-script ("I'm paying $30/mo for a tool that almost solves this")
- 5/5 — Repeated specific past instances + ongoing willingness to invest in better solution (commit > talk)
Consumes
- **ADO-BEACHHEAD-\* and PER-\* personas** — Defines the interview pool; non-segment interviews are still useful but get lower weight
- **Opportunity Solution Tree** (from prd-v10-continuous-discovery-torres) — Interview goals trace to specific opportunities being tested
- **CFD-\* prior research** — Patterns to validate or contradict
- **KPI-\* outcomes** — Why we're interviewing (anchor to what we're trying to move)
Produces
- **CFD-\* discovery interview entries** — One per interview, with confidence calibrated by Mom Test rules
- **CFD-\* pattern entries** — When 3+ interviews mention the same specific behavior, promote to a pattern entry with frequency
- **Bad Data inventory** — Compliments, fluff, ideas captured for context but not data
- **Discovery Tree updates** — Each interview either strengthens an opportunity, weakens an opportunity, or surfaces a new one
Execution
Step 1: Define the goal of this interview
Not "validate the idea." Specifically: which opportunity / assumption from the Opportunity Solution Tree is this interview testing?
| Goal | Example | |------|---------| | Validate opportunity exists | "Do beachhead pragmatists actually struggle with pricing-tier selection?" | | Test assumption | "Will users engage with a pricing wizard before signup?" | | Map workaround | "How are they solving this today?" | | Quantify pain | "How much time/money does this cost them currently?" |
Write the goal at the top of the interview notes.
Step 2: Prep the question list (Mom Test-shaped)
Use specifics-in-the-past form. Avoid hypotheticals.
| Bad question | Better (Mom Test) version | |--------------|---------------------------| | "Would you use a pricing wizard?" | "Walk me through how you picked the tier when you signed up." | | "Do you think analytics is important?" | "Tell me about the last time you looked at analytics." | | "What features do you wish we had?" | "Tell me about the last time the tool didn't do what you needed." | | "How would you describe X?" | "Tell me about the last time X came up at work." |
5–7 questions is plenty for a 25–30 minute interview. Leave room to follow threads.
Step 3: Run the interview
- **Open** with a short framing: who you are, why this conversation, no sales pitch
- **Ask the past-behavior questions**, listen, follow threads
- **Probe specifics** when they generalize: "When was that specifically?" / "What was the dollar / time cost?" / "What did you do next?"
- **Flag Bad Data internally**: when they compliment, smile and move on; when they fluff, anchor to specifics; when they suggest ideas, note as IDEA and dig for the underlying job
- **Talk less t
PRD-driven Context Engineering: A systematic approach to building AI-powered products using progressive documentation and context-aware development workflows
Repo: mattgierhart/PRD-driven-context-engineering
Other skills on prd-driven-context-engineering.
- /SKILL_TEMPLATE
[1-2 sentence description of what this skill does]. Triggers on [specific phrases/contexts that should activate this skill]. Outputs [what the skill produces].
Open skill - /ghm-gate-check
Validates gate criteria before PRD lifecycle advancement by delegating to the readiness scoring pipeline (scripts/readiness.py). Returns a graduated PASS / WARN / BLOCK verdict with top blockers and their causal chain. Triggers before advancing from v0.X to v0.Y or explicit
Open skill - /ghm-harvest
Extracts durable insights from temp/ files to SoT during EPIC Phase E. Triggers at EPIC completion or explicit `/ghm-harvest` invocation. Outputs new SoT entries and archive manifest.
Open skill - /ghm-id-register
Validates and registers new SoT IDs with cross-reference integrity. Triggers when creating BR-XXX, UJ-XXX, API-XXX, or CFD-XXX entries. Outputs formatted SoT entry with validated cross-references.
Open skill - /ghm-self-install
Install the PRD-Driven Context Engineering methodology into a fresh OR existing repository — the subscription-native alternative to forking the whole repo. Runs an interactive wizard that seeds the framework (.claude/ hooks, skills, agents, rules, scripts) without clobbering
Open skill - /ghm-sot-builder
Creates new Source of Truth (SoT) files when existing templates don't fit your needs. Triggers on requests to create a new SoT file, add a new artifact type, or when user says "I need to track [X] but there's no SoT for it", "create SoT", "new source of truth". Outputs a
Open skill

