Mmcp.market

discovery-interview-prep skill

by deanpeters·deanpeters/Product-Manager-Skills·7.1k stars

Plan customer discovery interviews with the right goal, segment, constraints, and method. Use when preparing interviews for problem validation, churn research, or new product ideas.

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Install the discovery-interview-prep skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.

git clone --depth 1 https://github.com/deanpeters/Product-Manager-Skills.git /tmp/Product-Manager-Skills
mkdir -p ~/.claude/skills
cp -r /tmp/Product-Manager-Skills/skills/discovery-interview-prep ~/.claude/skills/discovery-interview-prep
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

Purpose

Guide product managers through preparing for customer discovery interviews by asking adaptive questions about research goals, customer segments, constraints, and methodologies. Use this to design effective interview plans, craft targeted questions, avoid common biases, and maximize learning from limited customer access—ensuring discovery interviews yield actionable insights rather than confirmation bias or surface-level feedback.

This is not a script generator—it's a strategic prep process that outputs a tailored interview plan with methodology, question framework, and success criteria.

Input

Works best with: Your research goal — what you need to learn from customers. Also useful: Customer segment, access constraints (how many interviews, by when), and any hypotheses you're carrying in.

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 opens by asking your primary goal for the interviews, then narrows with follow-ups.

Example invocation: Prep interviews to understand why enterprise customers churn after 6 months — I can get 5 interviews in 2 weeks.

Key Concepts

The Discovery Interview Prep Flow

An interactive process that:

  1. Gathers product/problem context (marketing materials, assumptions)
  2. Defines research goals (what you're trying to learn)
  3. Identifies target customer segment and access constraints
  4. Recommends interview methodology (Jobs-to-be-Done, problem validation, switch interviews, etc.)
  5. Generates interview framework with questions, biases to avoid, and success metrics

Why This Works

  • Goal-driven: Aligns interview approach to what you need to learn
  • Adaptive: Adjusts methodology based on product stage (idea vs. existing product) and access constraints
  • Bias-aware: Highlights common pitfalls (leading questions, confirmation bias, solution-first thinking)
  • Actionable: Outputs interview guide ready to use

Anti-Patterns (What This Is NOT)

  • Not a user testing script: Discovery = learning problems; testing = validating solutions
  • Not a sales demo: Don't pitch—listen and learn
  • Not surveys at scale: Deep qualitative interviews (5-10 people), not broad surveys (100+ people)

When to Use This

  • Starting product discovery (validating problem space)
  • Repositioning an existing product (understanding new market)
  • Investigating churn or drop-off (retention interviews)
  • Evaluating feature ideas before building
  • Preparing for customer development sprints

When NOT to Use This

  • User testing a prototype (use usability testing frameworks instead)
  • Quantitative research at scale (use surveys, analytics)
  • When you already know the problem (move to solution validation)

Facilitation Source of Truth

Use workshop-facilitation as the default interaction protocol for this skill.

It defines:

  • session heads-up + entry mode (Guided, Context dump, Best guess)
  • one-question turns with plain-language prompts
  • progress labels (for example, Context Qx/8 and Scoring Qx/5)
  • interruption handling and pause/resume behavior
  • numbered recommendations at decision points
  • quick-select numbered response options for regular questions (include Other (specify) when useful)

This file defines the domain-specific assessment content. If there is a conflict, follow this file's domain logic.

Application

This interactive skill asks up to 4 adaptive questions, offering 3-4 enumerated options at each step.

Step 0: Gather Context (Before Questions)

Agent suggests:

Before we design your interview plan, let's gather context:

For Your Own Product (Existing or Planned):

  • Problem hypothesis or product concept description
  • Target customer segment (if known)
  • Existing research (support tickets, churn data, user feedback)
  • Product website or positioning materials
  • Key assumptions you're trying to validate

For Investigating an Existing Problem:

  • Customer complaints, support tickets, or churn reasons
  • Hypotheses about why customers leave or struggle
  • Competitive alternatives customers switch to

If Exploring a New Problem Space:

  • Find similar products or adjacent solutions
  • Copy competitor materials, customer reviews (G2, Capterra), or community discussions (Reddit, forums)
  • We'll use these to frame hypotheses

You can paste this content directly, or we can proceed with a brief description.

Question 1: Research Goal

Agent asks: "What's the primary goal of these discovery interviews? (What do you need to learn?)"

Offer 4 enumerated options:

  1. Problem validation — "Confirm that a problem exists and is painful enough to solve" (for new product ideas)
  2. Jobs-to-be-Done discovery — "Understand what customers are trying to accomplish and why current solutions fail" (for product strategy)
  3. Retention/churn investigation — "Figure out why customers leave or don't activate" (for existing products with churn issues)
  4. Feature prioritization — "Validate which problems/features matter most to customers" (for roadmap planning)

Or describe your own research goal (be specific: what question are you trying to answer?).

User response: [Selection or custom]

Question 2: Target Customer Segment

Agent asks: "Who are you interviewing? (Be as specific as possible.)"

Offer 4 enumerated options (adapted based on Q1):

Example (if Q1 = Problem validation):

  1. People who experience the problem regularly — E.g., "Small business owners who manually process invoices weekly" (high pain frequency)
  2. People who've tried to solve it — E.g., "Users who've tried 2+ competing solutions and churned" (understand failures)
  3. People in the target segment (regardless of problem awareness) — E.g., "All freelancers, even if they don't realize invoicing is a problem" (uncover latent needs)
  4. People who've recently experienced the problem — E.g., "Customers who churned in the last 30 days" (fresh memory)

Or describe your specific target segment (role, company size, behaviors, demographics).

Adaptation tip: Use personas or customer segments from provided materials.

User response: [Selection or custom]

Question 3: Constraints

Agent asks: "What constraints are you working with for these interviews?"

Offer 4 enumerated options:

  1. Limited access — "Can only interview 5-10 customers, need results in 2 weeks" (common for startups or fast timelines)
  2. Existing customer base — "Have 100+ active customers, can recruit easily" (mature product advantage)
  3. Cold outreach required — "No existing customers; need to recruit from scratch via LinkedIn, ads, or communities" (new product challenge)
  4. Internal stakeholders only — "Can interview sales/support teams who talk to customers daily" (proxy research, less ideal but pragmatic)

Or describe your specific constraints (budget, time, access, team capacity).

User response: [Selection or custom]

Question 4: Interview Methodology

Agent asks: "Based on your goal ([Q1]), target segment ([Q2]), and constraints ([Q3]), here are recommended interview methodologies:"

Offer 3-4 enumerated options (context-aware based on Q1-Q3):

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