Product Discovery

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name: product-discovery description: Use when validating product opportunities, mapping assumptions, planning discovery sprints, or testing problem-solution fit before committing delivery resources.

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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.