◆PromptShop

Product Discovery

The Product Discovery skill guides users through structured discovery to identify high-value opportunities and de-risk product bets.

Install

npx promptshop add product-discovery

Details

What This Skill Does

The Product Discovery skill guides users through structured discovery to identify high-value opportunities and de-risk product bets. It facilitates Opportunity Solution Tree creation, assumption mapping, problem validation, and solution validation. This skill is beneficial for product managers, designers, and engineers involved in early-stage product development and innovation.

When to Use

  • Facilitating Opportunity Solution Tree workshops.
  • Mapping assumptions and planning tests.
  • Conducting problem validation interviews.
  • Validating solutions with prototypes and experiments.
  • Planning discovery sprints with clear hypotheses.
  • Defining desired outcomes and measurable targets.

Key Features

  • Builds Opportunity Solution Trees for structured discovery.
  • Maps assumptions by risk and certainty.
  • Validates problems through interviews and analysis.
  • Runs concept, usability, and value tests.
  • Plans 1-2 week discovery sprints.
  • Identifies desirability, viability, and feasibility.

Manual Installation

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. 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.