dvcrn Product Discovery
Run structured discovery to identify high-value opportunities and de-risk product bets.
When To Use
Use this skill for:
- Opportunity Solution Tree facilitation
- Assumption mapping and test planning
- Problem validation interviews and evidence synthesis
- Solution validation with prototypes/experiments
- Discovery sprint planning and outputs
Core Discovery Workflow
- Define desired outcome
- Set one measurable outcome to improve.
- Establish baseline and target horizon.
- Build Opportunity Solution Tree (OST)
- Outcome -> opportunities -> solution ideas -> experiments
- Keep opportunities grounded in user evidence, not internal opinions.
- Map assumptions
- Identify desirability, viability, feasibility, and usability assumptions.
- Score assumptions by risk and certainty.
Use:
python3 scripts/assumption_mapper.py assumptions.csv
- Validate the problem
- Conduct interviews and behavior analysis.
- Confirm frequency, severity, and willingness to solve.
- Reject weak opportunities early.
- Validate the solution
- Prototype before building.
- Run concept, usability, and value tests.
- Measure behavior, not only stated preference.
- Plan discovery sprint
- 1-2 week cycle with explicit hypotheses
- Daily evidence reviews
- End with decision: proceed, pivot, or stop
Opportunity Solution Tree (Teresa Torres)
Structure:
- Outcome: metric you want to move
- Opportunities: unmet customer needs/pains
- Solutions: candidate interventions
- Experiments: fastest learning actions
Quality checks:
- At least 3 distinct opportunities before converging.
- At least 2 experiments per top opportunity.
- Tie every branch to evidence source.
Assumption Mapping
Assumption categories:
- Desirability: users want this
- Viability: business value exists
- Feasibility: team can build/operate it
- Usability: users can successfully use it
Prioritization rule:
- High risk + low certainty assumptions are tested first.
Problem Validation Techniques
- Problem interviews focused on current behavior
- Journey friction mapping
- Support ticket and sales-call synthesis
- Behavioral analytics triangulation
Evidence threshold examples:
- Same pain repeated across multiple target users
- Observable workaround behavior
- Measurable cost of current pain
Solution Validation Techniques
- Concept tests (value proposition comprehension)
- Prototype usability tests (task success/time-to-complete)
- Fake door or concierge tests (demand signal)
- Limited beta cohorts (retention/activation signals)
Discovery Sprint Planning
Suggested 10-day structure:
- Day 1-2: Outcome + opportunity framing
- Day 3-4: Assumption mapping + test design
- Day 5-7: Problem and solution tests
- Day 8-9: Evidence synthesis + decision options
- Day 10: Stakeholder decision review
Tooling
scripts/assumption_mapper.py
CLI utility that:
- reads assumptions from CSV or inline input
- scores risk/certainty priority
- emits prioritized test plan with suggested test types
See references/discovery-frameworks.md for framework details.