discovery-process▌
deanpeters/product-manager-skills · updated Apr 8, 2026
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Structured discovery cycle from problem hypothesis to validated solution, orchestrating framing, interviews, synthesis, and experiments.
- ›Guides product managers through six phases over 3–4 weeks: frame the problem, plan research, conduct customer interviews, synthesize insights, generate and validate solutions, and make go/no-go decisions
- ›Emphasizes continuous discovery practice (1 interview per week) rather than one-time research projects, with decision points between phases to pivot o
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:
- Frame the Problem — Define what you're investigating and why
- Conduct Research — Gather qualitative and quantitative evidence
- Synthesize Insights — Identify patterns, pain points, and opportunities
- Generate Solutions — Explore multiple solution options
- Validate Solutions — Test assumptions through experiments
- 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(com
How to use discovery-process on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add discovery-process
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches discovery-process from GitHub repository deanpeters/product-manager-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate discovery-process. Access the skill through slash commands (e.g., /discovery-process) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
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Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★48 reviews- ★★★★★Yuki Desai· Dec 28, 2024
discovery-process has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Kofi Kim· Dec 24, 2024
Useful defaults in discovery-process — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Aditi Torres· Dec 24, 2024
Registry listing for discovery-process matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Mia Jain· Dec 12, 2024
We added discovery-process from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Dhruvi Jain· Dec 8, 2024
discovery-process fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Oshnikdeep· Nov 27, 2024
Registry listing for discovery-process matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Anika Iyer· Nov 27, 2024
discovery-process reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Layla Abbas· Nov 15, 2024
discovery-process is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Nikhil Jackson· Nov 15, 2024
discovery-process fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Ganesh Mohane· Oct 18, 2024
discovery-process reduced setup friction for our internal harness; good balance of opinion and flexibility.
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