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product-discovery

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

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How to use this skill

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Source SKILL.md: https://github.com/LeoYeAI/openclaw-master-skills/blob/HEAD/skills/product-discovery/SKILL.md

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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.
  1. Build Opportunity Solution Tree (OST)
  • Outcome -> opportunities -> solution ideas -> experiments
  • Keep opportunities grounded in user evidence, not internal opinions.
  1. Map assumptions
  • Identify desirability, viability, feasibility, and usability assumptions.
  • Score assumptions by risk and certainty.

Use:

python3 scripts/assumption_mapper.py assumptions.csv
  1. Validate the problem
  • Conduct interviews and behavior analysis.
  • Confirm frequency, severity, and willingness to solve.
  • Reject weak opportunities early.
  1. Validate the solution
  • Prototype before building.
  • Run concept, usability, and value tests.
  • Measure behavior, not only stated preference.
  1. 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.