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product-discovery

Expert product discovery guidance for user research and problem validation. Use when conducting user interviews, validating problems, applying jobs-to-be-done framework, sizing opportunities, customer segmentation, competitive analysis, prototype testing, usability testing, designing surveys, or synthesizing research insights. Covers discovery sprints, continuous discovery, and research operations.

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含まれるファイル(12)

  • SKILL.md7.6 KB
  • rules/_sections.md733 B
  • rules/analysis-competitive.md11.7 KB
  • rules/analysis-opportunity.md8.3 KB
  • rules/analysis-segmentation.md8.9 KB
  • rules/analysis-synthesis.md10.5 KB
  • rules/discovery-jtbd.md10.8 KB
  • rules/discovery-problems.md7.9 KB
  • rules/research-interviews.md5.1 KB
  • rules/research-surveys.md6.3 KB
  • rules/testing-prototype.md8.0 KB
  • rules/testing-usability.md9.6 KB

SKILL.md(原文)

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Product Discovery

Strategic user research and problem validation expertise — from interview techniques and JTBD to opportunity sizing and insight synthesis.

Philosophy

Great products start with great problems. Discovery is how you find problems worth solving for people who will pay.

The best product discovery:

  1. Talk to users, not stakeholders — Customers know their problems, not solutions
  2. Validate problems before solutions — Build the right thing, then build it right
  3. Quantify and qualify — Numbers tell you what, conversations tell you why
  4. Continuous over batched — Weekly habits beat quarterly projects

How This Skill Works

When invoked, apply the guidelines in rules/ organized by:

  • research-* — User interview techniques, survey design, research ops
  • discovery-* — Problem discovery, JTBD framework, validation
  • analysis-* — Synthesis, segmentation, competitive analysis
  • testing-* — Prototype testing, usability testing

Core Frameworks

Discovery Process

PhaseActivitiesOutputs
ExploreInterviews, observation, data miningProblem space map
ValidateProblem interviews, surveys, experimentsValidated problems
PrioritizeOpportunity scoring, segmentationPrioritized roadmap
TestPrototype testing, usability studiesSolution validation

Jobs-to-be-Done Framework

                    ┌─────────────────────┐
                    │    FUNCTIONAL JOB   │
                    │   (What they do)    │
                    └──────────┬──────────┘
                               │
              ┌────────────────┼────────────────┐
              │                │                │
              ▼                ▼                ▼
       ┌──────────┐     ┌──────────┐     ┌──────────┐
       │ EMOTIONAL│     │  SOCIAL  │     │ CONTEXT  │
       │   JOB    │     │   JOB    │     │ (When/   │
       │ (Feel)   │     │ (Appear) │     │  Where)  │
       └──────────┘     └──────────┘     └──────────┘

Opportunity Scoring (OST)

FactorWeightDescription
Importance40%How important is this job to the customer?
Satisfaction30%How satisfied are they with current solutions?
Frequency20%How often do they encounter this problem?
Willingness to Pay10%Will they pay to solve this?

Opportunity Score = Importance + max(Importance - Satisfaction, 0)

Research Method Selection

┌─────────────────────────────────────────────────────────────┐
│                   GENERATIVE RESEARCH                       │
│              (Discover unknown unknowns)                    │
│  ┌───────────┐  ┌───────────┐  ┌───────────┐               │
│  │Contextual │  │ Discovery │  │ Diary    │               │
│  │ Inquiry   │  │ Interviews│  │ Studies  │               │
│  └───────────┘  └───────────┘  └───────────┘               │
├─────────────────────────────────────────────────────────────┤
│                   EVALUATIVE RESEARCH                       │
│              (Validate known hypotheses)                    │
│  ┌───────────┐  ┌───────────┐  ┌───────────┐               │
│  │ Usability │  │  A/B      │  │ Prototype │               │
│  │ Testing   │  │  Testing  │  │ Testing   │               │
│  └───────────┘  └───────────┘  └───────────┘               │
├─────────────────────────────────────────────────────────────┤
│                   QUANTITATIVE RESEARCH                     │
│              (Measure and prioritize)                       │
│  ┌───────────┐  ┌───────────┐  ┌───────────┐               │
│  │  Surveys  │  │ Analytics │  │ Card      │               │
│  │           │  │  Review   │  │ Sorting   │               │
│  └───────────┘  └───────────┘  └───────────┘               │
└─────────────────────────────────────────────────────────────┘

Customer Segmentation Matrix

DimensionConsumer (B2C)Business (B2B)
DemographicsAge, income, locationCompany size, industry, revenue
BehaviorUsage patterns, purchase historyBuying process, tech stack
PsychographicsValues, lifestyle, attitudesCompany culture, risk tolerance
NeedsProblems, goals, aspirationsBusiness outcomes, KPIs

Continuous Discovery Cadence

Weekly:
├── 2-3 customer interviews
├── Review analytics/feedback
└── Update opportunity backlog

Monthly:
├── Synthesis session
├── Prioritization review
└── Stakeholder alignment

Quarterly:
├── Deep-dive research sprint
├── Competitive analysis refresh
└── Segment review

Interview Quick Reference

Interview TypeWhen to UseKey Questions
DiscoveryExploring problem space"Tell me about the last time..."
ProblemValidating specific pain"How painful is this 1-10? Why?"
SolutionTesting concepts"Would this solve your problem?"
JTBDUnderstanding motivation"What were you trying to accomplish?"
UsabilityTesting interfaces"What do you expect to happen?"

Anti-Patterns

  • Solution-first discovery — Falling in love with solutions before validating problems
  • Leading the witness — Asking questions that suggest desired answers
  • Confirmation bias — Only hearing what supports your hypothesis
  • Sample of one — Making decisions from a single interview
  • Proxy research — Asking salespeople instead of customers
  • Feature requests as research — Users ask for features, not problems
  • Analysis paralysis — Researching forever, never deciding
  • HiPPO-driven — Highest Paid Person's Opinion overriding data

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

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