customer-discovery
Skill0 starsChanged 7 months agoMITView on GitHub
Understand user personas, pain point scenarios, and their underlying JTBD (Job-to-be-Done) through structured research interviews. Ensure evidence authenticity before defining any solution.
We split this skill into the rules it gives an agent
2 rules. 0 are common, 2 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 6 edited versions we read keep these.
2
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 28
Deep Dive Motivation (Five Whys): Ask why repeatedly until you touch on the user's emotional motivation or efficiency bottleneck.
- Distinctive: line 32
Every research output must be able to fill the following formula:
“Common” is measured: kept by at least 6 in 10 of the 6 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Customer Discovery: Deep User Discovery and Evidence Synthesis
Reject product definitions based on assumptions. Uncover real user behavioral motivations through deep interviews and observations, and transform them into quantifiable "opportunities."
Checklist
- Identify Target Segments — Who is the person in the most pain right now? Define their characteristics, scenarios, and existing alternatives.
- Design Non-Leading Interview Protocols — Use the "Past behavior predicts future behavior" principle. Write questions that focus on specific events rather than hypothetical intentions.
- Execute & Capture Raw Evidence — Save raw dialogue records or key summaries to
docs/pmpowers/discovery/. - Synthesize Evidence — Extract core pain points, unmet needs, and potential JTBDs.
- Sync Opportunity Map — Connect synthesized insights to nodes in
opportunity-mapping.
Interview Guidance: The Mom Test Protocol
1. Anti-Patterns
- Asking about Future Intent: "If you had this feature, would you use it?" (❌) -> People will lie out of politeness.
- Leading Bias: "Don't you think the current process is cumbersome?" (❌) -> Guiding the user to give the answer you want.
- Selling Solutions: Strictly forbid mentioning your product ideas during the first 90% of the interview. (❌)
2. Best Practices
- Anchor Specific Events: "When was the last time you encountered [Problem]? Please walk me through that process." (✅)
- Dig for Alternatives: "How much money or time do you currently spend to solve this? What tools have you tried?" (✅)
- Deep Dive Motivation (Five Whys): Ask why repeatedly until you touch on the user's emotional motivation or efficiency bottleneck. (✅)
Core Framework: JTBD (Job-to-be-Done)
Every research output must be able to fill the following formula:
"When I am in [Scenario], I want to [Take Some Action], so that I can [Achieve Some Progress/Outcome]."
Delivery Standards
Interview records and synthesis reports must be stored in docs/pmpowers/discovery/YYYY-MM-DD-<user-or-segment>.md.
Other versions7 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 7
- bingyue/fde-skills★ 5same name
- jpoindexter/Vanta★ 2same name
- AdeyStudios/skills★ 1same name
- ajstars1/founder-stack★ 1same name
- aya-taleb/biz-ready-skills★ 1same name
- agentdynarq/founder-skills★ 0same name
- ajstars1/startup-coach★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository34 other files it gives its agents.
- agile-scrum-masterySkill
- artifact-provenanceSkill
- brainstormingSkill
- business-acumenSkill
- cross-functional-collaborationSkill
- customer-empathySkill
- data-analysisSkill
- gtm-launch-managementSkill
- lean-experimentationSkill
- market-researchSkill
- metrics-definitionSkill
- negotiation-conflict-resolutionSkill
- north-star-metricSkill
- opportunity-mappingSkill
- outcome-ownershipSkill
- presentation-storytellingSkill
- prioritizationSkill
- problem-solvingSkill
- product-definitionSkill
- product-vision-strategySkill
- project-managementSkill
- prototypingSkill
- qa-quality-mindsetSkill
- roadmappingSkill
- spec-driven-designSkill
- stakeholder-managementSkill
- subagent-driven-discoverySkill
- technical-acumenSkill
- user-interviewingSkill
- using-pmpowersSkill
- ux-design-awarenessSkill
- validation-guardSkill
- writing-plansSkill
- written-communicationSkill
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
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Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

