customer-discovery-conversations
About customer-discovery-conversations
A skill for turning customer conversations into evidence that can inform decisions. It guides teams to ask about recent behavior, constraints, costs, and workarounds rather than seek approval for an idea.
Key features
- 01Set decision goals and risky assumptions
- 02Focus on a narrow customer segment
- 03Ask about recent behavior and examples
- 04Assess evidence and capture follow-ups
Use cases
- 01Prepare a customer discovery guide
- 02Run a batch of customer conversations
- 03Review notes for decision-relevant evidence
Install
Copy the command and run it in a terminal.
npx skills add https:/ / github.com/ lvtd-llc/ skills --skill customer-discovery-conversationsAfter installing, type/customer-discovery-conversationsin Claude Code to confirm it loaded.
Bundled files
References and scripts installed along with SKILL.md.
agents/openai.yamlworkflows/recover-bad-data.mdrewrite-interview-questions.mdrun-conversation-batch.mdDetails
From the project READMECustomer Discovery Conversations
Use this skill to turn customer conversations into decision-quality evidence. The goal is not approval for an idea. The goal is to learn concrete facts about customers' lives, current behavior, constraints, costs, workarounds, and risks.
Source Traceability
Primary source: The Mom Test by Rob Fitzpatrick, especially chapters 1-4 and 8. Guidance is paraphrased for this MIT repo; authoring notes used converted EPUB lines 236-2362 and 3613-4108.
Workflow Routing
| Need | Use |
|---|---|
| Rewrite an interview script | workflows/rewrite-interview-questions.md |
| Run a batch of conversations | workflows/run-conversation-batch.md |
| Recover from compliments, hypotheticals, or ideas | workflows/recover-bad-data.md |
| Segment is too broad | customer-segment-slicing |
| Product or sales meeting needs a real next step | customer-commitment-validation |
| Raw notes need synthesis | customer-learning-notes |
Conversation Workflow
- State the decision the conversations should improve.
- Name the riskiest assumptions, including at least one uncomfortable question.
- Choose the narrow customer or stakeholder type. If the segment is fuzzy, use
customer-segment-slicingfirst. - Prepare up to three learning goals for this type of person.
- Rewrite questions toward current behavior and specific past examples.
- Keep the conversation about the customer until there is enough evidence to discuss a solution.
- During the conversation, anchor vague claims to recent examples and dig into current costs, workarounds, constraints, and goals.
- Capture notes, signals, and follow-up tasks for review.
Question Rules
- Talk about the customer's life, work, tools, constraints, and recent past.
- Ask scary questions early enough that the answer can still change the plan.
- Turn opinions, compliments, and hypotheticals into requests for examples.
- Ask what already costs them time, money, reputation, or operational pain.
- Save the pitch until the customer evidence justifies discussing a solution.
Good Evidence
Prefer recent examples, current workarounds, money/time/reputation already spent, existing tools and budgets, named stakeholders, decision paths, and strong emotion backed by a concrete story.
Treat compliments, opinions, future promises, generic claims, and feature requests without motivation as weak evidence.
Output Format
# Customer Discovery Plan
## Context
- Decision:
- Segment:
- Risk questions:
## Conversation Guide
| Goal | Ask About | Avoid | Follow-Up |
|------|-----------|-------|-----------|
## Evidence To Capture
- Current behavior, past examples, workarounds:
- Costs, budget, constraints, stakeholders:
- Strong emotion, follow-up, commitment:
## Review Criteria
- What changed in our beliefs?
- What is still unknown?
- What are the next three questions?
Quality Bar
- Do not help users collect approval or compliments.
- Do not ask customers to design the product for the team.
- Do not treat "I would buy this" as validation without commitment.
- Do not run conversations without a decision, segment, and learning goals.
- Prefer fewer high-value conversations over many generic interviews.