Opportunity Solution Tree
- 2k installs
- 24.9k repo stars
- Updated July 3, 2026
- phuryn/pm-skills
A structured visual framework with four hierarchical levels (outcome, opportunities, solutions, experiments) that grounds product discovery in customer research and enables teams to systematically explore solution spaces
About
The Opportunity Solution Tree (OST) is a visual framework from Teresa Torres' Continuous Discovery Habits that structures product discovery by connecting measurable outcomes to customer opportunities, multiple solution approaches, and fast experiments. It prevents premature commitment to solutions by forcing teams to first map the opportunity space using customer research. The OST has four levels: desired outcome (top), opportunities (customer needs prioritized by Importance × (1-Satisfaction)), solutions (3+ options per opportunity generated by PM-Designer-Engineer trios), and experiments (assumption-testing validations). Key practices include maintaining one outcome per tree, framing opportunities as problems not features, generating competing solutions before deciding, and updating the tree weekly as learning accumulates from interviews and experiments.
- Four-level hierarchy: outcome, opportunities, solutions, experiments
- Opportunity Score formula: Importance × (1-Satisfaction) for prioritization
- Product Trio ideation: PM, Designer, Engineer collaborate on 3+ solutions per opportunity
- Assumption testing: validate Value, Usability, Viability, Feasibility risks with experiments
- Continuous discovery loop: update weekly, kill failing solutions, explore new branches
Opportunity Solution Tree by the numbers
- 1,998 all-time installs (skills.sh)
- +84 installs in the week ending Aug 5, 2026 (Skillselion tracking)
- Ranked #238 of 3,282 Productivity & Planning skills by installs in the Skillselion catalog
- Security screen: LOW risk (skills.sh audit)
- Data as of Aug 5, 2026 (Skillselion catalog sync)
opportunity-solution-tree capabilities & compatibility
- Capabilities
- structure unstructured customer research into op · prioritize competing opportunities using quantit · generate multiple solution alternatives to avoid · design lean experiments to validate assumptions · facilitate pm designer engineer ideation and ali · visualize discovery work in repeatable format · track learning loops and iterate on tree based o
- Works with
- notion · confluence · jira · slack
- Use cases
- planning · project management · research · api development
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| Installs | 2k |
|---|---|
| repo stars | ★ 24.9k |
| Security audit | 3 / 3 scanners passed |
| Last updated | July 3, 2026 |
| Repository | phuryn/pm-skills ↗ |
What it does
Structure product discovery by mapping desired outcomes to opportunities, solutions, and experiments for continuous validation.
Who is it for?
Product managers and cross-functional product teams (designers, engineers) running continuous discovery, scoping complex features, prioritizing opportunities from customer research, and structuring assumption validation.
Skip if: Solo founders without research data, teams with fixed scope/roadmaps that don't iterate, organizations practicing batch-mode discovery only, or builders needing implementation patterns rather than discovery frameworks.
When should I use this skill?
Starting a discovery cycle, facing multiple prioritization choices, integrating new customer research into planning, preparing for a design sprint or feature kickoff, or reviewing product strategy weekly.
What you get
Teams establish a single measurable outcome, discover and prioritize customer opportunities using research, generate multiple competing solutions via Product Trio collaboration, and design fast experiments to validate as
- Desired outcome statement (single measurable metric)
- Prioritized opportunities list with Opportunity Score calculations
- 3+ solutions per opportunity mapped to Product Trio perspectives
By the numbers
- Four-level hierarchy enforced: outcome, opportunities, solutions, experiments
- Minimum 3 solutions per opportunity recommended
- Opportunity Score formula: Importance (0-1) × (1-Satisfaction (0-1))
Files
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):
1. 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.
2. 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.
3. 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."
4. 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
1. Define the desired outcome — Confirm or help articulate a single, measurable outcome at the top of the tree.
2. Map opportunities — From provided research, identify 3-7 customer opportunities (needs/pains). Group related opportunities. Frame each from the customer's perspective.
3. Prioritize opportunities — Use Opportunity Score or qualitative assessment to rank. Focus on the top 2-3.
4. Generate solutions — For each prioritized opportunity, brainstorm 3+ solutions from PM, Designer, and Engineer perspectives.
5. Design experiments — For the most promising solutions, suggest 1-2 fast experiments. Specify: hypothesis, method, metric, success threshold.
6. Visualize the tree — Present the full OST in a clear hierarchical format.
Think step by step. Save as markdown if substantial.
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Further Reading
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Choose opportunity-solution-tree over generic roadmap templates when discovery needs a visual link from outcomes through opportunities to experiments.
FAQ
How do I prioritize opportunities when I have limited research data?
Use Opportunity Score (Importance × (1-Satisfaction)) with conservative estimates normalized 0-1, then validate rankings through quick follow-up interviews. Start with 3-5 top opportunities rather than attempting exhaustive prioritization. Group related opportunities to surface t
Should every solution be tested with a full experiment, or are there shortcuts?
Not every solution needs formal experiments. Quick experiments (landing page, prototype walkthrough, concierge MVP) validate before expensive builds. Reserve full experiments for solutions that passed assumption testing. 'Skin-in-the-game' (user spending time/effort) beats opinio
How often should I rebuild or refresh the OST?
Update the tree weekly as you learn from interviews, analytics, and experiment results. Kill solutions that fail validation. Explore new branches as opportunities emerge. One outcome per tree for focus, but rotate outcomes every 1-3 months as product strategy evolves.
Is Opportunity Solution Tree safe to install?
skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.