opportunity-solution-tree
phuryn/pm-skills
Structure product discovery by mapping desired outcomes to opportunities, solutions, and experiments.
What is opportunity-solution-tree?
The Opportunity Solution Tree (OST) is a framework for continuous product discovery that prevents jumping to solutions by first mapping the opportunity space. Use it when structuring discovery work, prioritizing customer needs, or deciding what to build next based on validated learning.
- Define a single measurable desired outcome tied to business metrics or OKRs
- Map customer opportunities (needs and pain points) from research data using the customer perspective
- Prioritize opportunities using Opportunity Score (Importance × (1 − Satisfaction)) or qualitative assessment
- Generate multiple solutions per opportunity through cross-functional ideation (PM, Designer, Engineer)
- Design fast, cheap experiments to validate solutions against key assumptions (Value, Usability, Viability, Feasibility)
- Visualize the full tree hierarchy and iterate weekly as new research and experiment results emerge
How to install opportunity-solution-tree
npx skills add https://github.com/phuryn/pm-skills --skill opportunity-solution-treeHow to use opportunity-solution-tree
- 1.Define a single, measurable desired outcome (e.g., a metric from your OKRs or product strategy)
- 2.Gather customer research data (interviews, surveys, analytics, user feedback)
- 3.Map 3-7 customer opportunities from the research, framed from the customer's perspective (e.g., 'I struggle to...')
- 4.Prioritize opportunities using Opportunity Score or qualitative assessment; focus on the top 2-3
- 5.Brainstorm 3+ solutions per prioritized opportunity with input from PM, Designer, and Engineer
- 6.Design 1-2 fast experiments per solution with clear hypotheses, methods, metrics, and success thresholds
- 7.Visualize the tree in a hierarchical format and review/update weekly as you learn from interviews and experiments
Use cases
- Structuring discovery work around a single business metric (e.g., increase 7-day retention to 40%)
- Organizing customer research (interviews, surveys, feedback) into a prioritized opportunity map
- Deciding between competing solution ideas by testing assumptions before committing resources
- Preventing premature feature development by validating that opportunities are real before building
- Facilitating cross-functional alignment between product, design, and engineering on discovery priorities
- Product managers structuring discovery and validation workflows
- Product teams (PM, Designer, Engineer) collaborating on opportunity prioritization
- Organizations practicing continuous discovery habits
- Teams moving from opinion-based to evidence-based product decisions
opportunity-solution-tree FAQ
An opportunity is a customer need, pain point, or desire (e.g., 'I struggle to find relevant content'). A feature is a proposed solution (e.g., 'add a recommendation algorithm'). The OST prioritizes opportunities (problems) first, not features, to avoid building the wrong solution.
Use the Opportunity Score formula: Importance × (1 − Satisfaction), where both are normalized to 0–1. This weights high-importance, low-satisfaction opportunities highest. Alternatively, use qualitative assessment based on research frequency and business impact.
Generate at least 3 solutions per opportunity before committing to one. Involve PM, Designer, and Engineer perspectives. Avoid the 'first idea' trap; the best ideas often come from engineers or emerge through diverse ideation.
A good experiment is fast and cheap, tests a specific assumption (Value, Usability, Viability, or Feasibility), and has clear success criteria. Prefer experiments with 'skin-in-the-game' (real user commitment) over opinion-based validation.
Update the tree weekly as you learn from customer interviews, analytics, and experiment results. The OST is a living artifact, not a one-time plan. Loop back, kill solutions that don't validate, and explore new branches based on learning.
Full instructions (SKILL.md)
Source of truth, from phuryn/pm-skills.
name: opportunity-solution-tree description: "Build an Opportunity Solution Tree (OST) to structure product discovery — map a desired outcome to opportunities, solutions, and experiments. Based on Teresa Torres' Continuous Discovery Habits. Use when structuring discovery work, mapping opportunities to solutions, or deciding what to build next."
Opportunity Solution Tree (OST)
A visual framework for structuring continuous product discovery. Connects a desired outcome to customer opportunities, possible solutions, and experiments to validate them.
Domain Context
The Opportunity Solution Tree (Teresa Torres, Continuous Discovery Habits) is the backbone of modern product discovery. It prevents teams from jumping to solutions by forcing them to first map the opportunity space.
Structure (4 levels):
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Desired Outcome (top) — The measurable business or product outcome the team is pursuing. Should be a single, clear metric (e.g., "increase 7-day retention to 40%"). This comes from your OKRs or product strategy.
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Opportunities (second level) — Customer needs, pain points, or desires discovered through research. These are problems worth solving — not features. Frame them from the customer's perspective: "I struggle to..." or "I wish I could..." Prioritize using Opportunity Score: Importance × (1 − Satisfaction) (Dan Olsen, The Lean Product Playbook). Normalize Importance and Satisfaction to 0–1.
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Solutions (third level) — Possible ways to address each opportunity. Generate multiple solutions per opportunity — don't commit to the first idea. The Product Trio (PM + Designer + Engineer) should ideate together. "Best ideas often come from engineers."
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Experiments (bottom) — Fast, cheap tests to validate whether a solution actually addresses the opportunity. Use assumption testing (Value, Usability, Viability, Feasibility risks). Prefer experiments with "skin-in-the-game" (Alberto Savoia) over opinion-based validation.
Key principles:
- One outcome at a time. Don't try to solve everything. Focus the tree on a single desired outcome.
- Opportunities, not features. "Never allow customers to design solutions. Prioritize opportunities (problems), not features."
- Compare and contrast. Always generate at least 3 solutions per opportunity before choosing. Avoid the "first idea" trap.
- Discovery is not linear. Loop back if experiments fail. Kill solutions that don't validate. Explore new branches.
- Continuous, not periodic. Update the tree weekly as you learn from interviews, analytics, and experiments.
Instructions
You are helping a product team build an Opportunity Solution Tree for $ARGUMENTS.
Input Requirements
- A desired outcome or business metric to improve
- Customer research data (interviews, surveys, analytics, feedback)
- Optionally: existing opportunities or solution ideas to organize
Process
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Define the desired outcome — Confirm or help articulate a single, measurable outcome at the top of the tree.
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Map opportunities — From provided research, identify 3-7 customer opportunities (needs/pains). Group related opportunities. Frame each from the customer's perspective.
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Prioritize opportunities — Use Opportunity Score or qualitative assessment to rank. Focus on the top 2-3.
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Generate solutions — For each prioritized opportunity, brainstorm 3+ solutions from PM, Designer, and Engineer perspectives.
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Design experiments — For the most promising solutions, suggest 1-2 fast experiments. Specify: hypothesis, method, metric, success threshold.
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Visualize the tree — Present the full OST in a clear hierarchical format.
Think step by step. Save as markdown if substantial.
Further Reading
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