product-discovery

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

1. Define desired outcome

• Set one measurable outcome to improve.

• Establish baseline and target horizon.

2. Build Opportunity Solution Tree (OST)

• Outcome -> opportunities -> solution ideas -> experiments

• Keep opportunities grounded in user evidence, not internal opinions.

3. Map assumptions

• Identify desirability, viability, feasibility, and usability assumptions.

• Score assumptions by risk and certainty.

Use:

python3 scripts/assumption_mapper.py assumptions.csv

4. Validate the problem

• Conduct interviews and behavior analysis.

• Confirm frequency, severity, and willingness to solve.

• Reject weak opportunities early.

5. Validate the solution

• Prototype before building.

• Run concept, usability, and value tests.

• Measure behavior, not only stated preference.

6. 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.