Generate Product Discovery Templates

This skill covers product-discovery methodologies, user research, and validation frameworks - structured templates that help teams systematically explore problems, validate assumptions, and discover viable solutions before committing to development. Core principles: problem-first approach (understand the problem before solutions, validate it's worth solving, quantify severity and frequency, map to user segments), assumption-driven discovery (document all assumptions, prioritize by risk and impact, design targeted experiments, use the smallest viable experiment), and evidence-based decision making (define success criteria upfront, collect qualitative and quantitative evidence, triangulate across sources, document insights for the future).

The discovery framework runs in two phases. Phase 1, Problem Definition, uses a template:

### Problem Statement
**Problem Hypothesis**: [Clear, specific problem statement]
**Target User**: [Specific user segment or persona]
**Context**: [When/where does this problem occur]

### Problem Validation Metrics
- Problem frequency: [How often users encounter this]
- Problem severity: [Pain level on 1-10 scale]
- Current workarounds: [What users do today]
- Willingness to pay: [Economic validation]

### Research Questions
1. How do users currently [relevant behavior]?
2. What triggers [problem situation]?
3. What have users tried before?
4. What would success look like?

Phase 2, Solution Exploration, covers a primary solution plus 2-3 alternatives, a key-assumptions table scoring risk level, validation method, and success criteria per assumption, and a validation-experiment template with hypothesis, method, duration, success criteria, and resources needed. Research-method templates cover a 45-minute user-interview guide (opening, a 20-minute problem-discovery segment, a 15-minute solution-exploration segment, wrap-up, with specific example questions) and a problem-validation survey template (screening questions, problem-assessment questions on a 1-10 scale, and solution-interest ranking questions).

Experiment-design patterns cover a landing-page test (goal, setup steps, metrics - traffic, conversion, bounce rate, qualitative feedback - and success criteria of over 5% conversion and under 60% bounce rate) and prototype testing (prototype type, testing method, test scenarios, and metrics like task completion rate, time-to-complete, satisfaction score, and usability issues found). A discovery-synthesis template captures key insights (problem-validation status, most and least-interested segments, solution preferences, adoption barriers), validated and invalidated assumptions, new questions surfaced, and a final recommendation - proceed, pivot, or stop - with rationale, next steps, and remaining risks.

Discovery-planning best practices cover time-boxing (2-6 week boxes with specific learning goals per phase and checkpoint reviews), balancing research methods (qualitative depth with quantitative scale, starting broad then narrowing, triangulating data sources, involving cross-functional team members), and documentation and communication (shared discovery artifacts, regular stakeholder updates, documented decisions and rationale, cross-team learning sharing). Common pitfalls flagged: confirmation bias in question design, over-researching obvious problems, ignoring negative feedback, analysis paralysis, and skipping synthesis to jump straight to solutions.

Use it when running product discovery - defining and validating a problem, designing experiments to test key assumptions, running user interviews or surveys, or synthesizing discovery findings into a proceed, pivot, or stop decision. It is not a full product-delivery or sprint-execution framework - it is scoped to the discovery phase before development commitment.

Given a problem hypothesis and target user segment, it produces a structured problem-definition document, a prioritized assumptions table with validation experiments, interview and survey instruments, and a discovery-synthesis summary with a go or no-go recommendation.

Product managers, UX researchers, and product teams running structured discovery before committing to development.