agent product skill risk: low
Opportunity Solution Tree Product Discovery
Guides building an Opportunity Solution Tree by defining a desired outcome, mapping and prioritizing customer opportunities from research data, generating multiple solutions, desig…
SKILL 1 file
SKILL.md
--- 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):** 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. --- ### Further Reading - [The Extended Opportunity Solution Tree](https://www.productcompass.pm/p/the-extended-opportunity-solution-tree) - [What Is Product Discovery? The Ultimate Guide Step-by-Step](https://www.productcompass.pm/p/what-exactly-is-product-discovery) - [Product Trio: Beyond the Obvious](https://www.productcompass.pm/p/product-trio) - [Continuous Product Discovery Masterclass (CPDM)](https://www.productcompass.pm/p/cpdm) (video course)
INPUTS
- $ARGUMENTS REQUIRED
topic or focus area for the OST
REQUIRED CONTEXT
- desired outcome or business metric
- customer research data
OPTIONAL CONTEXT
- existing opportunities or solution ideas
ROLES & RULES
Role assignments
- You are helping a product team build an Opportunity Solution Tree for **$ARGUMENTS**.
- One outcome at a time.
- Opportunities, not features.
- Compare and contrast.
- Discovery is not linear.
- Continuous, not periodic.
EXPECTED OUTPUT
- Format
- markdown
- Schema
- markdown_sections · Desired Outcome, Opportunities, Solutions, Experiments
- Constraints
- hierarchical tree format with 4 levels
- include prioritized opportunities, multiple solutions, and experiments
SUCCESS CRITERIA
- Define the desired outcome
- Map opportunities
- Prioritize opportunities
- Generate solutions
- Design experiments
- Visualize the tree
CAVEATS
- Dependencies
- A desired outcome or business metric to improve
- Customer research data (interviews, surveys, analytics, feedback)
- Optionally: existing opportunities or solution ideas to organize
- Missing context
- Exact input format expected for research data and $ARGUMENTS
- Preferred visualization syntax or tool for the tree
- Ambiguities
- 'Save as markdown if substantial.' does not define criteria for substantial.
QUALITY
- OVERALL
- 0.80
- CLARITY
- 0.85
- SPECIFICITY
- 0.78
- REUSABILITY
- 0.88
- COMPLETENESS
- 0.72
IMPROVEMENT SUGGESTIONS
- Replace 'Save as markdown if substantial.' with a clear rule: 'Always output the final OST as a single Markdown file using nested bullet points and headings.'
- Add an 'Output Format' section that shows an example tree skeleton with placeholders.
USAGE
Copy the prompt above and paste it into your AI of choice — Claude, ChatGPT, Gemini, or anywhere else you're working. Replace any placeholder sections with your own context, then ask for the output.
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