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User Interview Prep

Community
Mehdibargach
user-interview-prep

Generate a complete user research brief with hypotheses, interview guide, and open-ended questions. Takes a research topic or hypothesis and returns a ready-to-use interview kit.

Overview

PublisherMehdibargach
Repositoryclaude-code-pm-skills
Skill nameuser-interview-prep
Stars
97
Forks
38
Bundled files
Instructions only
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Mehdibargach on GitHub. Read the source before you install it.

Installation

Install the User Interview Prep AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Mehdibargach/claude-code-pm-skills/tree/main/skills/user-interview-prep
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Mehdibargach/claude-code-pm-skills.git /tmp/claude-code-pm-skills
mkdir -p .claude/skills
cp -r /tmp/claude-code-pm-skills/skills/user-interview-prep .claude/skills/user-interview-prep
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable User Interview Prep in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads User Interview Prep on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so User Interview Prep is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

User Interview Prep

Generate a structured research brief and interview guide that a PM can use immediately. The goal is to go into interviews with clear hypotheses, unbiased questions, and a plan to learn — not confirm.

Process

  1. Clarify the research goal — Restate the input as a single, testable research question. If the input is vague, sharpen it. A good research goal starts with "Understand..." or "Discover..." — never "Validate..." or "Confirm...".
  2. Surface assumptions — List 3-5 hypotheses the PM is likely carrying into this research. These are the things that need to be tested, not confirmed.
  3. Design the interview flow — Structure a 30-minute interview with warm-up, core exploration, and wrap-up sections. Each section has a purpose and time allocation.
  4. Write the questions — Generate open-ended, non-leading questions. Follow the "Mom Test" principles: ask about their life, not your idea. Ask about the past, not hypothetical futures.
  5. Add observation notes — Flag what to watch for beyond words: body language, hesitation, workarounds they mention, emotional signals.

Output Format

Research Goal

One sentence. Clear, specific, learnable.

Hypotheses to Test

#HypothesisHow We'll Know If It's TrueHow We'll Know If It's False
1.........
2.........
3.........

Interview Guide

Warm-up (5 min) — Build rapport, understand context.

  • Question 1
  • Question 2

Core Exploration (20 min) — Dig into the problem space.

  • Question 3
  • Question 4
  • ...

Wrap-up (5 min) — Capture anything missed, get referrals.

  • Question N
  • "Is there anything I should have asked but didn't?"

Things to Watch For

Bullet list of behavioral signals, hesitations, or workarounds to note during the interview.

Anti-Patterns to Avoid

Bullet list of common interviewer mistakes specific to this topic (leading questions, pitching solutions, etc.).

Rules

  • Never write a question that starts with "Would you..." or "Do you think...". These invite hypothetical answers. Ask about past behavior instead.
  • Every question must pass the Mom Test: would you get honest, useful data even if you asked your mom?
  • No more than 12 questions total. Quality over quantity — each question should earn its place.
  • Include at least 2 follow-up prompts per core question (e.g., "Tell me more about that", "What happened next?").
  • Write in English.
  • Keep the entire output under 2 pages. A PM should be able to print this and bring it to the interview.
  • Be opinionated: if a hypothesis seems weak or untestable, call it out and suggest a better one.
  • Do not include questions about the PM's product or solution. This is about understanding the problem.

Frequently asked questions

What does the User Interview Prep AI skill do?

Generate a complete user research brief with hypotheses, interview guide, and open-ended questions. Takes a research topic or hypothesis and returns a ready-to-use interview kit.

Why use User Interview Prep on TypingMind?

Because you install it once and use it with any model. User Interview Prep is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install User Interview Prep in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Mehdibargach/claude-code-pm-skills/tree/main/skills/user-interview-prep. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use User Interview Prep?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with User Interview Prep?

As many as you like. As long as a model supports skills, you can use User Interview Prep with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the User Interview Prep AI skill free?

It is published on GitHub by Mehdibargach. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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