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Product Discovery

Product product-discovery Source

Install: claude /plugin install product-skills

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
  2. Set one measurable outcome to improve.

  3. Establish baseline and target horizon.

  4. Build Opportunity Solution Tree (OST)

  5. Outcome -> opportunities -> solution ideas -> experiments

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

  7. Map assumptions

  8. Identify desirability, viability, feasibility, and usability assumptions.
  9. Score assumptions by risk and certainty.

Use: 

python3 scripts/assumption_mapper.py assumptions.csv

  1. Validate the problem
  2. Conduct interviews and behavior analysis.
  3. Confirm frequency, severity, and willingness to solve.

  4. Reject weak opportunities early.

  5. Validate the solution

  6. Prototype before building.
  7. Run concept, usability, and value tests.

  8. Measure behavior, not only stated preference.

  9. Plan discovery sprint

  10. 1-2 week cycle with explicit hypotheses
  11. Daily evidence reviews
  12. 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.