plugins/faos-pm/skills/interview-script/SKILL.md
<!-- AUTO-GENERATED by export-plugins.py — DO NOT EDIT --> --- name: interview-script description: Create structured customer interview scripts following Mom Test principles and JTBD probing. Use when preparing for customer discovery interviews, user research sessions, or validating product assumptions. tags: [discovery, customer-research, interviews, jtbd] --- # Customer Interview Script Create structured, bias-free customer interview scripts that uncover real needs — not just what people say
npx skillsauth add frank-luongt/faos-skills-marketplace plugins/faos-pm/skills/interview-scriptInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Create structured, bias-free customer interview scripts that uncover real needs — not just what people say they want.
Most customer interviews produce unusable data because they ask leading questions, talk about the future ("Would you use X?"), or let the interviewer pitch instead of listen. This skill produces scripts grounded in The Mom Test principles: talk about their life, not your idea.
Set the stage and build rapport.
"Thanks for taking the time to chat with me today. I'm [name] from [company].
We're researching [broad topic area] to understand how people like you
handle [relevant activity]. There are no right or wrong answers — I'm
genuinely interested in your experience.
Is it okay if I take notes? [And record, if applicable]
Before we dive in, could you tell me a bit about your role and what
a typical day/week looks like for you?"
Rules:
Understand their context and establish baseline.
Template questions:
Purpose: Build rapport, understand their world, identify context that shapes their needs.
This is where you dig into past behavior and real experiences. Follow Mom Test rules.
Close gracefully and leave the door open.
"This has been really helpful. A couple of quick closing questions:
- Is there anything about [topic] that I should have asked but didn't?
- Is there anyone else you'd recommend I talk to about this?
Thank you so much for your time. I may follow up with a quick question
or two — would that be okay?"
Use these when an answer is vague or surface-level:
| Technique | Example | | --- | --- | | "Tell me more" | "That's interesting — can you tell me more about that?" | | Laddering (Why x3) | "Why was that frustrating?" → "Why does that matter?" → "Why is that important to your work?" | | Specific example | "Can you give me a specific example of when that happened?" | | Contrast | "How is that different from how you did it before?" | | Quantify | "How often does that happen? How much time does it take?" | | Emotional | "How did that make you feel?" |
After each interview, capture:
## Interview Notes
**Date**: [date]
**Participant**: [name/role/company]
**Interviewer**: [name]
### Key Jobs (What they're trying to accomplish)
1. [job]
2. [job]
### Current Solution
- [How they solve it today]
### Biggest Pain Points
1. [pain] — Severity: High/Medium/Low
2. [pain] — Severity: High/Medium/Low
### Desired Outcome
- [What "success" looks like to them]
### Willingness to Pay / Priority
- [Evidence of how much they care]
### Surprise Findings
- [Anything unexpected]
### Verbatim Quotes (Most Important)
- "[exact quote]"
- "[exact quote]"
### Follow-Up Actions
- [ ] [action]
| Avoid | Why | Instead | | --- | --- | --- | | "Would you use X?" | Hypothetical = unreliable | "Tell me about the last time you tried to solve this" | | Pitching your solution | Biases all subsequent answers | Save pitching for after the interview | | Leading questions | Confirms your bias, not their reality | Ask open-ended, neutral questions | | Multiple questions at once | Overwhelms, they answer the easiest one | One question at a time | | Nodding/agreeing too much | Signals what you want to hear | Stay neutral, curious | | Only interviewing fans | Selection bias | Include churned users, non-users, competitors' users |
development
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: databricks-mlflow-evaluation --- # MLflow 3 GenAI Evaluation ## Before Writing Any Code 1. **Read GOTCHAS.md** - 15+ common mistakes that cause failures 2. **Read CRITICAL-interfaces.md** - Exact API signatures and data schemas ## End-to-End Workflows Follow these workflows based on your goal. Each step indicates which reference files to read. ### Workflow 1: First-Time Evaluation Setup For users new to MLflow GenAI evalu
development
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: databricks-lakebase-provisioned --- # Lakebase Provisioned Patterns and best practices for using Lakebase Provisioned (Databricks managed PostgreSQL) for OLTP workloads. ## When to Use Use this skill when: - Building applications that need a PostgreSQL database for transactional workloads - Adding persistent state to Databricks Apps - Implementing reverse ETL from Delta Lake to an operational database - Storing chat/agent m
tools
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development
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: databricks-genie --- # Databricks Genie Create and query Databricks Genie Spaces - natural language interfaces for SQL-based data exploration. ## Overview Genie Spaces allow users to ask natural language questions about structured data in Unity Catalog. The system translates questions into SQL queries, executes them on a SQL warehouse, and presents results conversationally. ## When to Use This Skill Use this skill when: -