Opportunity Solution Tree

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:

1. Desired Outcome (business goal or product metric)
2. Opportunities (customer problems, needs, pain points, or desires that could drive the outcome)
3. Solutions (ways to address each opportunity)
4. Experiments (tests to validate solutions)

Structure:

         Desired Outcome (1)
                |
    +-----------+-----------+
    |           |           |
Opportunity  Opportunity  Opportunity (3)
    |           |           |
  +-+-+       +-+-+       +-+-+
  | | |       | | |       | | |
 S1 S2 S3    S1 S2 S3    S1 S2 S3 (9 total solutions)

Why This Works

• Outcome-driven: Starts with business goal, not feature requests
• Divergent before convergent: Explores multiple opportunities before picking solutions
• Problem-focused: Opportunities are problems, not solutions disguised as problems
• Testable: Each solution maps to experiments, not just "build it and ship"
• POC selection: Evaluates feasibility, impact, market fit before committing resources

Anti-Patterns (What This Is NOT)

• Not a feature list: Opportunities are problems customers face, not "we need dark mode"
• Not solution-first: Don't start with "we should build X"—start with "customers struggle with Y"
• Not waterfall planning: OST is a discovery tool, not a project plan
• Not a one-time exercise: OSTs evolve as you learn from experiments

When to Use This

• Stakeholder requests a feature or product initiative
• Starting discovery for a new product area
• Clarifying vague OKRs or strategic goals
• Prioritizing which problems to solve first
• Aligning team on what outcomes you're driving

When NOT to Use This

• When the problem is already validated (move to solution testing)
• For tactical bug fixes or technical debt (no discovery needed)
• When stakeholders demand a specific solution (address alignment issues first)

Facilitation Source of Truth

Use `workshop-facilitation` as the default interaction protocol for this skill.

It defines:

• session heads-up + entry mode (Guided, Context dump, Best guess)
• one-question turns with plain-language prompts
• progress labels (for example, Context Qx/8 and Scoring Qx/5)
• interruption handling and pause/resume behavior
• numbered recommendations at decision points
• quick-select numbered response options for regular questions (include Other (specify) when useful)

This file defines the domain-specific assessment content. If there is a conflict, follow this file's domain logic.

Application

Use template.md for the full fill-in structure.

This interactive skill follows a two-phase process:

Phase 1: Generate OST (extract outcome, identify opportunities, map solutions) Phase 2: Select POC (evaluate solutions, recommend best starting point)

Step 0: Gather Context (Before Questions)

Agent suggests:

Before we create your Opportunity Solution Tree, let's gather context:

Stakeholder Request or Product Initiative:

• What did the stakeholder ask for? (Feature request, product idea, strategic goal)
• Any existing materials: PRD drafts, OKR documents, strategy memos, meeting notes
• Problem statements, customer complaints, or research findings

Product Context (if available):

• Website copy, positioning statements, product descriptions
• 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:

1. Revenue growth — "Increase ARR, expand revenue from existing customers, new revenue streams" (Common for scaling products)
2. Customer retention — "Reduce churn, increase activation, improve engagement/stickiness" (Common for established products with retention issues)
3. Customer acquisition — "Increase sign-ups, trial conversions, new user growth" (Common for early-stage or growth products)
4. 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):

1. 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]

1. 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"

• Evidence: [From context: conversion funnel drop-off at pricing page, sales objections]

1. 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):