Build an Opportunity Solution Tree from outcomes to opportunities, solutions, and tests. Use when a stakeholder request needs problem framing before you decide what to build.
Install
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add deanpeters/Product-Manager-Skills --skill opportunity-solution-tree -a cursor -y
Download skills/opportunity-solution-tree/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does opportunity-solution-tree live?
The publisher keeps it at https://github.com/deanpeters/Product-Manager-Skills.
Which agents can use opportunity-solution-tree?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.
Build an Opportunity Solution Tree from outcomes to opportunities, solutions, and tests. Use when a stakeholder request needs problem framing before you decide what to build.
name: opportunity-solution-tree
argument-hint: "[stakeholder request or outcome]"
description: Build an Opportunity Solution Tree from outcomes to opportunities, solutions, and tests. Use when a stakeholder request needs problem framing before you decide what to build.
intent: >-
Guide product managers through creating an Opportunity Solution Tree (OST) by extracting target outcomes from stakeholder requests, generating opportunity options (problems to solve), mapping potential solutions, and selecting the best proof-of-concept (POC) based on feasibility, impact, and market fit. Use this to move from vague product requests to structured discovery, ensuring teams solve the right problems before jumping to solutions—avoiding "feature factory" syndrome and premature convergence on ideas.
type: interactive
theme: discovery-research
best_for:
"Turning a stakeholder feature request back into a problem worth solving"
"Connecting a desired outcome to opportunities, solutions, and tests"
"Showing why one solution was chosen over the alternatives"
scenarios:
"A stakeholder asked for a specific feature and I want to reframe it as a problem first"
"I need to show leadership why we picked this solution over the three alternatives"
estimated_time: "30-45 min"
Purpose
Guide product managers through creating an Opportunity Solution Tree (OST) by extracting target outcomes from stakeholder requests, generating opportunity options (problems to solve), mapping potential solutions, and selecting the best proof-of-concept (POC) based on feasibility, impact, and market fit. Use this to move from vague product requests to structured discovery, ensuring teams solve the right problems before jumping to solutions—avoiding "feature factory" syndrome and premature convergence on ideas.
This is not a roadmap generator—it's a structured discovery process that outputs validated opportunities with testable solution hypotheses.
Input
Works best with: The stakeholder request or the target outcome you're starting from.
Also useful: Customer evidence you already have, constraints, and solutions already being pushed.
Anything supplied with the invocation itself — text after the skill name, a pasted context dump, or an appended ARGUMENTS: line — counts as answers already given. Use it and skip whatever it covers; don't re-ask.
Arriving empty-handed? That works too. The skill opens by asking for the request or desired outcome, then extracts the measurable target outcome from it.
Example invocation:Build an OST from this request: 'Sales says we need a mobile app because competitors have one.'
Key Concepts
What is an Opportunity Solution Tree (OST)?
An OST is a visual framework (Teresa Torres, Continuous Discovery Habits) that connects:
Desired Outcome (business goal or product metric)
Opportunities (customer problems, needs, pain points, or desires that could drive the outcome)
Competitor materials, customer reviews (G2, Capterra), community discussions
Usage data, support tickets, churn reasons
You can paste this content directly, or describe the request briefly.
Phase 1: Generate Opportunity Solution Tree
Question 1: Extract Desired Outcome
Agent asks:
"What's the desired outcome for this initiative? (What business or product metric are you trying to move?)"
Offer 4 enumerated options:
Revenue growth — "Increase ARR, expand revenue from existing customers, new revenue streams" (Common for scaling products)
Customer retention — "Reduce churn, increase activation, improve engagement/stickiness" (Common for established products with retention issues)
Customer acquisition — "Increase sign-ups, trial conversions, new user growth" (Common for early-stage or growth products)
Product efficiency — "Reduce support costs, decrease time-to-value, improve operational metrics" (Common for mature products optimizing operations)
Or describe your specific desired outcome (be measurable: e.g., "Increase trial-to-paid conversion from 15% to 25%").
User response: [Selection or custom]
Agent extracts and confirms:
Desired Outcome: [Specific, measurable outcome]
Why it matters: [Rationale from stakeholder request or context]
Question 2: Identify Opportunities (Problems to Solve)
Agent generates 3 opportunities based on the desired outcome and context provided.
Agent says:
"Based on your desired outcome ([from Q1]) and the context you provided, here are 3 opportunities (customer problems or needs) that could drive this outcome:"
Example (if Outcome = Increase trial-to-paid conversion):
Opportunity 1: Users don't experience value during trial — "New users sign up but don't complete onboarding, never reach 'aha moment,' abandon before seeing core value"
Evidence: [From context: onboarding analytics, support tickets, exit surveys]
Opportunity 2: Pricing is unclear or misaligned — "Users unsure if paid plan is worth it; don't understand what they get for the price; pricing page confusing"
Opportunity 3: Free plan is 'good enough' — "Users stay on free tier indefinitely because it meets their needs; no compelling reason to upgrade"
Evidence: [From context: freemium user retention data, support tickets asking for workarounds]
Agent asks:
"Which opportunity feels most critical to explore first, or would you like to modify/add opportunities?"
User response: [Selection or custom]
Question 3: Generate Solutions for Selected Opportunity
Agent generates 3 solution ideas for the selected opportunity.
Agent says:
"For Opportunity [X]: [Name], here are 3 potential solutions to test:"
Example (if Opportunity 1 selected: Users don't experience value during trial):
Solution 1: Guided onboarding checklist — "Interactive checklist that walks users through core workflows step-by-step (e.g., 'Create your first project,' 'Invite a teammate,' 'Complete a task')"
Experiment: A/B test checklist vs. no checklist, measure activation rate
Solution 2: Time-to-value triggers — "Automatically prompt users when they're stuck (e.g., 'You haven't created a project yet—here's a template to get started')"
Hypothesis: Proactive nudges prevent drop-off
Experiment: Track engagement with prompts, measure trial-to-paid lift
Solution 3: Human-assisted onboarding — "Offer 15-min onboarding call with CSM for high-value trial users (enterprise, team plans)"
Hypothesis: Personal touch increases conversion for high-intent users
Experiment: Offer to 50 trial users, measure conversion vs. control group
Agent asks:
"Would you like to explore solutions for another opportunity, or move to POC selection?"
User response: [Selection or custom]
Phase 2: Select Proof-of-Concept (POC)
After generating solutions, the agent helps select which solution to test first.
Agent says:
"Let's evaluate your solutions to pick the best starting point. I'll score each solution on Feasibility (how hard to build), Impact (how much it could move the outcome), and Market Fit (how well it aligns with customer needs)."
Agent generates evaluation table:
Solution
Feasibility (1-5)
Impact (1-5)
Market Fit (1-5)
Total Score
Rationale
Solution 1: Guided onboarding checklist
4
4
5
13
High feasibility (UI pattern), proven impact (industry standard), strong market fit (users expect onboarding)
Solution 2: Time-to-value triggers
3
3
4
10
Medium feasibility (requires analytics integration), moderate impact (depends on trigger quality), good market fit
Solution 3: Human-assisted onboarding
5
5
3
13
High feasibility (no dev work), high impact (personal touch), lower market fit (doesn't scale, high-touch only)
Manual concierge test — "Run the solution manually with 20 users (e.g., personally walk them through onboarding), measure outcomes" (Best for: Learning fast, no dev work)
Or describe your experiment approach.
User response: [Selection or custom]
Output: Opportunity Solution Tree + POC Plan
After completing the flow, the agent outputs:
# Opportunity Solution Tree + POC Plan
## Desired Outcome
**Outcome:** [From Q1]
**Target Metric:** [Specific, measurable goal]
**Why it matters:** [Rationale]
---
## Opportunity Map
### Opportunity 1: [Name]
**Problem:** [Description]
**Evidence:** [From context]
**Solutions:**
1. [Solution A]
2. [Solution B]
3. [Solution C]
---
### Opportunity 2: [Name]
**Problem:** [Description]
**Evidence:** [From context]
**Solutions:**
1. [Solution A]
2. [Solution B]
3. [Solution C]
---
### Opportunity 3: [Name]
**Problem:** [Description]
**Evidence:** [From context]
**Solutions:**
1. [Solution A]
2. [Solution B]
3. [Solution C]
---
## Selected POC
**Opportunity:** [Selected opportunity]
**Solution:** [Selected solution]
**Hypothesis:**
- "If we [implement solution], then [outcome metric] will [increase/decrease] from [X] to [Y] because [rationale]."
**Experiment:**
- **Type:** [A/B test / Prototype test / Concierge test]
- **Participants:** [Number of users, segment]
- **Duration:** [Timeline]
- **Success criteria:** [What validates the hypothesis]
**Feasibility Score:** [1-5]
**Impact Score:** [1-5]
**Market Fit Score:** [1-5]
**Total:** [Sum]
**Why this POC:**
- [Rationale 1]
- [Rationale 2]
- [Rationale 3]
---
## Next Steps
1. **Build experiment:** [Specific action, e.g., "Create onboarding checklist wireframes"]
2. **Run experiment:** [Specific action, e.g., "Deploy to 50% of trial users for 2 weeks"]
3. **Measure results:** [Specific metric, e.g., "Compare activation rate: checklist vs. control"]
4. **Decide:** [If successful → scale; if failed → try next solution]
---
**Ready to build the experiment? Let me know if you'd like to refine the hypothesis or explore alternative solutions.**
Examples
See examples/sample.md for full OST examples.
Mini example excerpt:
**Desired Outcome:** Increase trial-to-paid conversion from 15% to 25%
**Opportunity:** Users don’t reach "aha" moment during trial
**Solution:** Guided onboarding checklist
Common Pitfalls
Pitfall 1: Opportunities Disguised as Solutions
Symptom: "Opportunity: We need a mobile app"
Consequence: You've already converged on a solution without exploring the problem.
Fix: Reframe opportunities as customer problems: "Mobile-first users can't access product on the go."
Pitfall 2: Skipping Divergence (Jumping to One Solution)
Symptom: "We know the solution is [X], just need to build it"
Consequence: Miss better alternatives, no learning.
Fix: Generate at least 3 solutions per opportunity. Force divergence before convergence.
Pitfall 3: Outcome is Too Vague
Symptom: "Desired Outcome: Improve user experience"
Download skills/opportunity-solution-tree/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does opportunity-solution-tree live?
The publisher keeps it at https://github.com/deanpeters/Product-Manager-Skills.
Which agents can use opportunity-solution-tree?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.