opportunity-solution-tree
by phurynThe opportunity-solution-tree skill helps Product Management teams build an Opportunity Solution Tree for product discovery: map a desired outcome to opportunities, solutions, and experiments. Use it to structure discovery work, compare options, and decide what to build next with less solution bias.
This skill scores 74/100 and is worth listing: it gives agents a clear product-discovery workflow for building an Opportunity Solution Tree, with enough structure to reduce guesswork versus a generic prompt. Directory users should expect a solid but somewhat self-contained guide rather than a deeply operational package.
- Explicit trigger and use case: building an Opportunity Solution Tree for discovery, solution mapping, or deciding what to build next.
- Good operational structure: defines the 4 OST levels and explains how to frame and prioritize opportunities.
- Strong domain leverage: ties the method to Teresa Torres and adds prioritization guidance like Opportunity Score.
- No companion scripts, references, or resources, so the skill is mostly instruction-driven and may require manual interpretation.
- Limited progressive disclosure around edge cases or execution examples, so agents may still need prompting for nonstandard discovery contexts.
Overview of opportunity-solution-tree skill
The opportunity-solution-tree skill helps you build an Opportunity Solution Tree for product discovery: a structured path from a desired outcome to customer opportunities, candidate solutions, and experiments. It is most useful for Product Management teams that want a clearer way to decide what to build next without jumping straight to features.
What this skill is for
Use the opportunity-solution-tree skill when you need to turn strategy, research, or OKRs into a decision-ready discovery map. It is a good fit if you are defining a product outcome, clustering customer pains, comparing solution ideas, or planning tests before committing to delivery.
Who benefits most
This skill is best for product managers, discovery leads, designers, and cross-functional teams working on a shared problem space. It is especially helpful when stakeholders already have feature ideas, but you need a more disciplined way to connect them to evidence.
Why it differs from a generic prompt
A generic prompt can produce an OST-shaped answer, but the opportunity-solution-tree skill gives you a repeatable structure: outcome first, opportunities second, solutions third, experiments last. That sequence matters because it reduces solution bias and makes tradeoffs easier to explain to teammates.
How to Use opportunity-solution-tree skill
Install and inspect the skill
For opportunity-solution-tree install, use the directory install flow in your skills environment, then open SKILL.md first. If your agent setup supports repository-based installation, point it at phuryn/pm-skills and the pm-product-discovery/skills/opportunity-solution-tree path.
Give the skill decision-ready input
The skill works best when you provide four things up front: a target outcome, the user segment, what evidence you already have, and any known constraints. For example, instead of “make me an OST,” use: “Build an Opportunity Solution Tree for improving 7-day retention in self-serve trial users. We have interview notes, churn reasons, and one existing onboarding flow. Prioritize opportunities before solutions.”
Read the right files first
Start with SKILL.md to understand the workflow and required inputs. If your local installation exposes only one file, treat that as the source of truth. If your environment mirrors the broader repository, also check nearby package metadata or discovery guidance so you can align the tree with your team’s product vocabulary.
Run the workflow in the right order
A practical opportunity-solution-tree usage flow is: define the outcome, collect opportunities from research or user feedback, group and rank those opportunities, brainstorm multiple solutions per opportunity, then attach experiments that can test the riskiest assumptions. Keep the tree focused on one outcome so it stays useful instead of becoming a backlog dump.
opportunity-solution-tree skill FAQ
Is this just a brainstorming template?
No. The opportunity-solution-tree skill is meant to structure product discovery, not to generate random ideas. Its value is in forcing a clearer chain from outcome to evidence-backed opportunities to testable solutions.
When should I not use it?
Do not use an OST when the problem is already fully specified and the work is execution-heavy, or when you do not have enough customer evidence to identify real opportunities. In those cases, a simpler requirement brief or delivery plan is usually better.
Is it beginner-friendly?
Yes, if you already know the product goal. The main challenge is not the format; it is choosing a single outcome and phrasing opportunities from the customer perspective instead of as hidden features.
How is it useful for Product Management?
For Product Management, the opportunity-solution-tree skill improves prioritization conversations, makes discovery work visible, and helps teams explain why one opportunity or experiment is more valuable than another. It is especially useful before roadmap commitments are made.
How to Improve opportunity-solution-tree skill
Start with a narrower outcome
Better OSTs come from tighter prompts. “Increase activation” is too broad; “Increase activation for new team admins within 7 days of signup” gives the skill a concrete target, a user segment, and a usable decision boundary.
Bring real opportunity data
The quality of the tree depends on the quality of the opportunities. Feed the skill interview quotes, support themes, session notes, win/loss patterns, or usage data so it can separate real customer pain from internal assumptions.
Ask for ranked tradeoffs, not just branches
To get stronger opportunity-solution-tree usage results, ask the skill to rank opportunities, explain why they matter, and propose the smallest experiment that would disconfirm each solution. That makes the output more actionable for discovery reviews and stakeholder alignment.
Iterate after the first tree
Use the first version to surface gaps, then refine it with better evidence and sharper language. If the tree feels solution-heavy, strip features back into opportunity language; if it feels vague, add segment detail, outcome metrics, and explicit constraints such as timeline, tech limits, or research confidence.