MCP HubMCP Hub
스킬 목록으로 돌아가기

discovery-process

deanpeters
업데이트됨 Yesterday
3 조회
4,511
575
4,511
GitHub에서 보기
기타general

정보

이 스킬은 체계적인 문제 정의, 고객 인터뷰, 실험을 통해 팀이 문제 가설에서 검증된 솔루션까지 완전한 제품 발견 워크플로를 진행하도록 구성합니다. 실제 고객 문제를 해결할 수 있도록 개발 전에 문제 영역을 체계적으로 탐색하고 가정을 검증하기 위해 설계되었습니다. 유지율 문제를 조사하거나 잘못된 제품을 구축하는 것을 피하기 위한 체계적인 접근이 필요할 때 사용하세요.

빠른 설치

Claude Code

추천
기본
npx skills add deanpeters/Product-Manager-Skills -a claude-code
플러그인 명령대체
/plugin add https://github.com/deanpeters/Product-Manager-Skills
Git 클론대체
git clone https://github.com/deanpeters/Product-Manager-Skills.git ~/.claude/skills/discovery-process

Claude Code에서 이 명령을 복사하여 붙여넣어 스킬을 설치하세요

문서

Purpose

Guide product managers through a complete discovery cycle—from initial problem hypothesis to validated solution—by orchestrating problem framing, customer interviews, synthesis, and experimentation skills into a structured process. Use this to systematically explore problem spaces, validate assumptions, and build confidence before committing to full development—avoiding "build it and they will come" syndrome and ensuring you're solving real customer problems.

This is not a one-time research project—it's a continuous discovery practice that runs in parallel with delivery, typically 1-2 discovery cycles per quarter.

Key Concepts

What is the Discovery Process?

The discovery process (Teresa Torres, Marty Cagan) is a structured approach to exploring problem spaces and validating solutions before building. It consists of:

  1. Frame the Problem — Define what you're investigating and why
  2. Conduct Research — Gather qualitative and quantitative evidence
  3. Synthesize Insights — Identify patterns, pain points, and opportunities
  4. Generate Solutions — Explore multiple solution options
  5. Validate Solutions — Test assumptions through experiments
  6. Decide & Document — Commit to build, pivot, or kill

Why This Works

  • De-risks product decisions: Tests assumptions before expensive builds
  • Customer-centric: Grounds decisions in real customer problems, not internal opinions
  • Iterative: Builds confidence progressively through small experiments
  • Fast learning: Discovers "no-go" signals early, saves wasted effort

Anti-Patterns (What This Is NOT)

  • Not waterfall research: Discovery runs continuously, not once before dev
  • Not user testing: Discovery validates problems; testing validates solutions
  • Not a substitute for shipping: Discovery informs delivery, doesn't replace it

When to Use This

  • Exploring new product/feature areas
  • Investigating retention or churn problems
  • Validating strategic initiatives before roadmap commitment
  • Continuous discovery (weekly customer touchpoints)

When NOT to Use This

  • For well-understood problems (move to execution)
  • When stakeholders have already committed to a solution (address alignment first)
  • For tactical bug fixes or technical debt (no discovery needed)

Facilitation Source of Truth

When running this workflow as a guided conversation, use workshop-facilitation as the interaction protocol.

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 workflow sequence and domain-specific outputs. If there is a conflict, follow this file's workflow logic.

Application

Use template.md for the full fill-in structure.

This workflow orchestrates 6 phases over 2-4 weeks, using multiple component and interactive skills.


Phase 1: Frame the Problem (Day 1-2)

Goal: Define what you're investigating, who's affected, and success criteria.

Activities

1. Run Problem Framing Canvas

  • Use: skills/problem-framing-canvas/SKILL.md (interactive - MITRE)
  • Participants: PM, design, engineering lead
  • Duration: 120 minutes
  • Output: Problem statement + "How Might We" question

2. Create Formal Problem Statement

  • Use: skills/problem-statement/SKILL.md (component)
  • Participants: PM
  • Duration: 30 minutes
  • Output: Structured problem statement with hypothesis

3. Define Proto-Personas (If Needed)

  • Use: skills/proto-persona/SKILL.md (component)
  • When: If target customer segment is unclear
  • Duration: 60 minutes
  • Output: Hypothesis-driven personas

4. Map Jobs-to-be-Done (If Needed)

  • Use: skills/jobs-to-be-done/SKILL.md (component)
  • When: If customer motivations are unclear
  • Duration: 60 minutes
  • Output: JTBD statements

Outputs from Phase 1

  • Problem hypothesis: "We believe [persona] struggles with [problem] because [root cause], leading to [consequence]."
  • Research questions: 3-5 questions to answer through discovery
  • Success criteria: What would validate/invalidate the problem?

Decision Point 1: Do we have enough context to start research?

If YES: Proceed to Phase 2 (Research Planning)

If NO: Gather existing data first:

  • Review support tickets, churn surveys, NPS feedback
  • Analyze product analytics (drop-off points, usage patterns)
  • Review competitor research, market trends
  • Time impact: +2-3 days

Phase 2: Research Planning (Day 3)

Goal: Design research approach, recruit participants, prepare interview guide.

Activities

1. Prep Discovery Interviews

  • Use: skills/discovery-interview-prep/SKILL.md (interactive)
  • Participants: PM, design
  • Duration: 90 minutes
  • Output: Interview plan with methodology, questions, biases to avoid

2. Recruit Participants

  • Target: 5-10 customers per discovery cycle (Teresa Torres: continuous discovery = 1 interview/week)
  • Segment: Focus on personas from Phase 1
  • Recruitment channels:
    • Existing customers (email, in-app prompts)
    • Churned customers (exit interviews)
    • Cold outreach (LinkedIn, communities)
  • Incentive: $50-100 gift card or product credit
  • Duration: 2-3 days (parallel with Phase 1)

3. Schedule Interviews

  • Format: 45-60 min per interview (30-40 min conversation + buffer)
  • Timeline: Spread across 1-2 weeks
  • Recording: Get consent, record for synthesis

Outputs from Phase 2

  • Interview guide: 5-7 open-ended questions (Mom Test style)
  • Participant roster: 5-10 scheduled interviews
  • Synthesis plan: How you'll capture and analyze insights

Phase 3: Conduct Research (Week 1-2)

Goal: Gather qualitative evidence through customer interviews.

Activities

1. Conduct Discovery Interviews

  • Methodology: From skills/discovery-interview-prep/SKILL.md (Problem validation, JTBD, switch interviews, etc.)
  • Participants: PM + optional observer (design, eng)
  • Duration: 5-10 interviews over 1-2 weeks
  • Focus areas:
    • Past behavior (not hypotheticals): "Tell me about the last time you [experienced this problem]"
    • Workarounds: "How do you currently handle this?"
    • Alternatives tried: "Have you tried other solutions? Why did you stop?"
    • Pain intensity: "How much time/money does this cost you?"

2. Take Structured Notes

  • Template:
    • Participant: [Name, role, company size]
    • Context: [When/where they experience problem]
    • Actions: [What they do, step-by-step]
    • Pain points: [Frustrations, blockers]
    • Workarounds: [Current solutions]
    • Quotes: [Verbatim customer language]
    • Insights: [Patterns, surprises]

3. Review Support Tickets & Analytics (Parallel)

  • Support tickets: Tag by theme (onboarding, feature confusion, bugs)
  • Analytics: Identify drop-off points, feature usage, cohort behavior
  • Surveys: Review NPS comments, exit surveys, feature requests

Outputs from Phase 3

  • Interview transcripts: Recorded sessions + detailed notes
  • Support ticket themes: Top 10 issues by frequency
  • Analytics insights: Quantitative data on behavior (e.g., "60% abandon onboarding at step 3")

Decision Point 2: Have we reached saturation?

Saturation = same pain points emerge across 3+ interviews, no new insights

If YES (saturated after 5-7 interviews): Proceed to Phase 4 (Synthesis)

If NO (still learning new things): Schedule 3-5 more interviews

  • Time impact: +1 week

Phase 4: Synthesize Insights (End of Week 2)

Goal: Identify patterns, prioritize pain points, map opportunities.

Activities

1. Affinity Mapping (Thematic Analysis)

  • Method:
    • Write each insight/quote on sticky note
    • Group by theme (e.g., "onboarding confusion," "pricing objections," "mobile access")
    • Count frequency (how many customers mentioned each theme)
  • Participants: PM, design, optional eng
  • Duration: 90-120 minutes
  • Output: Themed clusters with frequency counts

2. Create Customer Journey Map (Optional)

  • Use: skills/customer-journey-mapping-workshop/SKILL.md (interactive)
  • When: If pain points span multiple phases (discover, try, buy, use, support)
  • Duration: 90 minutes
  • Output: Journey map with opportunities ranked by impact

3. Prioritize Pain Points

  • Criteria:
    • Frequency: How many customers mentioned this?
    • Intensity: How painful is it? (time wasted, money lost, emotional frustration)
    • Strategic fit: Does solving this align with business goals?
  • Method: Score each pain point (1-5) on frequency, intensity, strategic fit
  • Output: Ranked list of top 3-5 pain points to address

4. Update Problem Statement

  • Use: skills/problem-statement/SKILL.md (component)
  • Refine based on research: Did initial hypothesis hold? Adjust if needed.
  • Output: Validated problem statement

Outputs from Phase 4

  • Affinity map: Themes with frequency counts
  • Top 3-5 pain points: Prioritized by frequency × intensity × strategic fit
  • Customer quotes: 3-5 verbatim quotes per pain point
  • Validated problem statement: Refined based on evidence

Phase 5: Generate & Validate Solutions (Week 3)

Goal: Explore solution options, design experiments, validate assumptions.

Activities

1. Generate Opportunity Solution Tree

  • Use: skills/opportunity-solution-tree/SKILL.md (interactive)
  • Input: Top 3 pain points from Phase 4
  • Participants: PM, design, engineering lead
  • Duration: 90 minutes
  • Output: 3 opportunities, 3 solutions per opportunity, POC recommendation

Alternative: Use Lean UX Canvas

  • Use: skills/lean-ux-canvas/SKILL.md (interactive)
  • When: Prefer hypothesis-driven approach over OST
  • Output: Hypotheses to test, minimal experiments

2. Design Experiments

  • For each solution: Define "What's the least work to learn the next most important thing?"
  • Experiment types:
    • Concierge test: Manually deliver solution to 10 customers, observe
    • Prototype test: Clickable mockup, usability test with 10 users
    • Landing page test: Fake door test (show feature, measure interest)
    • A/B test: Build minimal version, test with 50% of users
  • Success criteria: What metric/behavior validates hypothesis?

3. Run Experiments

  • Timeline: 1-2 weeks per experiment
  • Participants: PM + design (for prototypes), eng (for A/B tests)
  • Output: Quantitative and qualitative validation data

Outputs from Phase 5

  • Solution options: 3-9 solutions (3 per opportunity)
  • Experiment results: Did hypothesis validate or invalidate?
  • Customer feedback: Qualitative reactions to prototypes/concepts

Decision Point 3: Did experiments validate solution?

If YES (validated): Proceed to Phase 6 (Decide & Document)

If NO (invalidated):

  • Pivot to next solution option
  • Re-run experiments with adjusted approach
  • Time impact: +1-2 weeks

Phase 6: Decide & Document (End of Week 3-4)

Goal: Commit to build, document decision, communicate to stakeholders.

Activities

1. Make Go/No-Go Decision

  • Criteria:
    • Problem validated? (Phase 3-4)
    • Solution validated? (Phase 5)
    • Strategic fit? (aligns with business goals)
    • Feasible? (engineering capacity, technical complexity)
  • Decision:
    • GO: Move to roadmap, write epics/stories
    • PIVOT: Explore alternative solution
    • KILL: De-prioritize, not worth solving now

2. Define Epic Hypotheses (If GO)

  • Use: skills/epic-hypothesis/SKILL.md (component)
  • Participants: PM
  • Duration: 60 minutes per epic
  • Output: Epic hypothesis statement with success criteria

3. Write PRD (If GO)

  • Use: skills/prd-development/SKILL.md (workflow)
  • Participants: PM
  • Duration: 1-2 days
  • Output: Structured PRD with problem, solution, success metrics

4. Communicate Findings

  • Format: 30-min readout covering:
    • Problem validation (Phase 3-4 insights)
    • Solution validation (Phase 5 experiments)
    • Recommendation (GO/PIVOT/KILL)
  • Participants: Execs, product leadership, key stakeholders
  • Output: Alignment on next steps

Outputs from Phase 6

  • Decision: GO, PIVOT, or KILL
  • Epic hypotheses: (if GO) Testable epic statements
  • PRD: (if GO) Formal product requirements document
  • Stakeholder alignment: Exec buy-in on recommendation

Complete Workflow: End-to-End Summary

Week 1:
├─ Day 1-2: Frame the Problem
│  ├─ skills/problem-framing-canvas/SKILL.md (120 min)
│  ├─ skills/problem-statement/SKILL.md (30 min)
│  └─ [Optional] skills/proto-persona/SKILL.md, skills/jobs-to-be-done/SKILL.md
│
├─ Day 3: Research Planning
│  ├─ skills/discovery-interview-prep/SKILL.md (90 min)
│  ├─ Recruit participants (2-3 days)
│  └─ Schedule 5-10 interviews
│
└─ Day 4-5: Conduct Research (Start)
   └─ First 2-3 customer interviews

Week 2:
├─ Day 1-3: Conduct Research (Continue)
│  └─ Remaining customer interviews (3-7 more)
│
├─ Day 4-5: Synthesize Insights
│  ├─ Affinity mapping (120 min)
│  ├─ [Optional] skills/customer-journey-mapping-workshop/SKILL.md (90 min)
│  ├─ Prioritize pain points
│  └─ Update problem statement
│
└─ Decision: Reached saturation? (if NO, +1 week more interviews)

Week 3:
├─ Day 1-2: Generate & Validate Solutions
│  ├─ skills/opportunity-solution-tree/SKILL.md (90 min)
│  └─ Design experiments
│
├─ Day 3-5: Run Experiments
│  ├─ Concierge tests, prototypes, or A/B tests
│  └─ Gather validation data
│
└─ Decision: Validated? (if NO, pivot to next solution, +1-2 weeks)

Week 4:
└─ Decide & Document
   ├─ Make GO/NO-GO decision
   ├─ [If GO] skills/epic-hypothesis/SKILL.md (60 min per epic)
   ├─ [If GO] skills/prd-development/SKILL.md (1-2 days)
   └─ Communicate findings (30 min readout)

Total Time Investment:

  • Fast track: 3 weeks (5 interviews, 1 experiment)
  • Typical: 4 weeks (7-10 interviews, 1-2 experiments)
  • Thorough: 6-8 weeks (10+ interviews, multiple experiment rounds)

Examples

See examples/sample.md for a full discovery process example.

Mini example excerpt:

**Problem:** Onboarding drop-off due to jargon
**Insight:** 6/10 users quit at step 3
**Decision:** Go with guided checklist experiment

Common Pitfalls

Pitfall 1: Skipping Customer Interviews

Symptom: Rely only on analytics and support tickets, no qualitative research

Consequence: Miss "why" behind behavior, build wrong solutions

Fix: Always interview 5-10 customers per discovery cycle (even if you have data)


Pitfall 2: Asking Leading Questions

Symptom: "Would you use [feature X] if we built it?"

Consequence: Confirmation bias, customers say "yes" to be polite

Fix: Use Mom Test questions from skills/discovery-interview-prep/SKILL.md (focus on past behavior)


Pitfall 3: Not Reaching Saturation

Symptom: Interview 2-3 customers, declare discovery complete

Consequence: Small sample, not representative

Fix: Continue interviews until same patterns emerge across 3+ customers (typically 5-7 interviews minimum)


Pitfall 4: Analysis Paralysis

Symptom: Spend 6 weeks synthesizing insights, never move to solutions

Consequence: No delivery, team loses momentum

Fix: Time-box discovery to 3-4 weeks; after Phase 6, move to execution


Pitfall 5: Discovery as One-Time Activity

Symptom: Run discovery once before building, then stop

Consequence: Miss evolving customer needs, market changes

Fix: Continuous discovery (Teresa Torres): 1 customer interview per week, ongoing


References

Related Skills (Orchestrated by This Workflow)

Phase 1:

  • skills/problem-framing-canvas/SKILL.md (interactive)
  • skills/problem-statement/SKILL.md (component)
  • skills/proto-persona/SKILL.md (component, optional)
  • skills/jobs-to-be-done/SKILL.md (component, optional)

Phase 2:

  • skills/discovery-interview-prep/SKILL.md (interactive)

Phase 4:

  • skills/customer-journey-mapping-workshop/SKILL.md (interactive, optional)

Phase 5:

  • skills/opportunity-solution-tree/SKILL.md (interactive)
  • skills/lean-ux-canvas/SKILL.md (interactive, alternative)

Phase 6:

  • skills/epic-hypothesis/SKILL.md (component)
  • skills/prd-development/SKILL.md (workflow)

External Frameworks

  • Teresa Torres, Continuous Discovery Habits (2021) — Weekly customer touchpoints, OST framework
  • Rob Fitzpatrick, The Mom Test (2013) — How to ask good interview questions
  • Marty Cagan, Inspired (2017) — Product discovery principles

Dean's Work

  • Productside Blueprint — Strategic discovery process
  • [If Dean has discovery resources, link here]

Skill type: Workflow Suggested filename: discovery-process.md Suggested placement: /skills/workflows/ Dependencies: Orchestrates 10+ component and interactive skills across 6 phases

GitHub 저장소

deanpeters/Product-Manager-Skills
경로: skills/discovery-process
0
ai-agentsai-product-managementclaude-skillspm-frameworksproduct-management

연관 스킬

llamaguard

기타

LlamaGuard는 폭력 및 혐오 발언 등 6가지 안전 범주에서 LLM 입력과 출력을 조정하기 위한 Meta의 70-80억 파라미터 모델입니다. 94-95% 정확도를 제공하며 vLLM, Hugging Face 또는 Amazon SageMaker를 사용해 배포할 수 있습니다. 이 기술을 사용하여 AI 애플리케이션에 콘텐츠 필터링 및 안전 가드레일을 손쉽게 통합하세요.

스킬 보기

cost-optimization

기타

이 Claude Skill은 리소스 적정화, 태깅 전략, 지출 분석을 통해 개발자들이 클라우드 비용을 최적화할 수 있도록 지원합니다. AWS, Azure, GCP에서 클라우드 비용을 절감하고 비용 거버넌스를 구현하기 위한 프레임워크를 제공합니다. 인프라 비용을 분석하거나, 리소스를 적정화하거나, 예산 제약을 충족해야 할 때 사용하세요.

스킬 보기

quantizing-models-bitsandbytes

기타

이 스킬은 bitsandbytes를 사용하여 LLM을 8비트 또는 4비트 정밀도로 양자화하며, 최소한의 정확도 손실로 50-75%의 메모리 감소를 달성합니다. 제한된 GPU 메모리에서 더 큰 모델을 실행하거나 추론을 가속화하는 데 이상적이며, INT8, NF4, FP4와 같은 형식을 지원합니다. 이 스킬은 HuggingFace Transformers와 통합되어 QLoRA 학습 및 8비트 옵티마이저를 가능하게 합니다.

스킬 보기

dispatching-parallel-agents

기타

이 Claude Skill은 3개 이상의 독립적인 문제를 동시에 조사하고 해결하기 위해 다중 에이전트를 배치합니다. 공유 상태나 의존성 없이 해결 가능한 무관련 장애 시나리오에 맞게 설계되었습니다. 핵심 기능은 병렬 문제 해결로, 각 독립 문제 영역마다 하나의 에이전트를 할당하여 효율성을 극대화합니다.

스킬 보기