opportunity-solution-tree
by deanpetersopportunity-solution-tree skill for Product Management helps you turn a vague request into an Opportunity Solution Tree with outcomes, opportunities, solutions, and tests. Use this opportunity-solution-tree guide to frame discovery, compare options, and avoid premature feature-first decisions.
This skill scores 84/100, which means it is a solid directory listing for users who want a structured Opportunity Solution Tree workflow rather than a generic brainstorming prompt. The repository gives enough trigger guidance, template structure, and example-driven execution to help an agent move from stakeholder request to outcomes, opportunities, solutions, and experiment/POC selection with less guesswork.
- Explicit trigger and intent: use when a stakeholder request needs problem framing before deciding what to build.
- Strong operational structure: the skill defines an OST workflow from desired outcomes to opportunities, solutions, experiments, and POC selection.
- High practical leverage: includes a reusable template and sample walkthroughs that show how an agent should execute the process.
- No install command or support files are provided, so adoption depends on reading the SKILL.md and template directly.
- The repository is labeled with experimental/test signals, so users should verify fit against their own product discovery workflow before standardizing on it.
Overview of opportunity-solution-tree skill
What opportunity-solution-tree does
The opportunity-solution-tree skill helps product managers turn a vague stakeholder ask into an Opportunity Solution Tree: a clear chain from outcome, to opportunities, to solutions, to experiments. It is most useful when you need to slow down “build this feature” requests and reframe the work around the real problem.
Who it fits best
This opportunity-solution-tree skill is a strong fit for Product Management workflows, especially discovery-led teams, product ops, and AI agents assisting PMs. Use it when you need structure for ambiguous requests, retention problems, activation gaps, or prioritization discussions where solution-first thinking is creating noise.
Why people install it
The main value of opportunity-solution-tree install is decision quality: it helps you avoid premature convergence, compare multiple opportunities before choosing a solution, and keep experiments tied to a measurable outcome. It is not a roadmap generator; it is a discovery framing tool.
How to Use opportunity-solution-tree skill
Install and inspect the skill
Install the skill with npx skills add deanpeters/Product-Manager-Skills --skill opportunity-solution-tree. Then open SKILL.md first, followed by template.md and examples/sample.md to understand the expected output shape before you adapt it to your own product context.
Turn a rough request into a usable prompt
For the best opportunity-solution-tree usage, do not paste a one-line feature request and stop there. Add: the target user segment, the business metric or outcome, the observed problem, any constraints, and what evidence you already have. A stronger prompt looks like: “Reduce 3-month churn for self-serve trial users from 18% to 12%; identify likely opportunities, propose 2-3 solutions per opportunity, and recommend one experimentable POC with rationale.”
Follow the workflow in order
A practical opportunity-solution-tree guide is: define the outcome, list candidate opportunities, map solutions to each opportunity, then choose the best proof-of-concept based on feasibility, impact, and market fit. If your input skips the outcome, the tree tends to become a feature brainstorm instead of a discovery tool.
Read the repository files that matter
Start with SKILL.md for the process, template.md for the output structure, and examples/sample.md to see how the tree is populated from real product language. Because the repo is intentionally compact and has no extra rules/, resources/, or helper scripts, those three files are the real installation surface.
opportunity-solution-tree skill FAQ
Is this only for Product Management?
Yes, the opportunity-solution-tree skill is primarily for Product Management and adjacent discovery work. It is less useful for implementation planning, sprint tasking, or pure UX writing because it focuses on problem framing and testable solution choice.
How is it different from a normal prompt?
A normal prompt often jumps straight from request to answer. This skill adds a disciplined sequence: outcome first, then opportunities, then solutions, then experiment selection. That structure makes opportunity-solution-tree usage better when multiple plausible problems exist and you need evidence-based prioritization.
Is it beginner friendly?
Yes, if you can describe a product problem in plain language. Beginners usually get the most value from the template and the example file, because they show how to convert messy stakeholder language into a usable tree without overcomplicating the first pass.
When should I not use it?
Do not use opportunity-solution-tree when the goal is already clear and the task is execution detail, not discovery. If you only need a feature spec, a release checklist, or a solution write-up with no outcome uncertainty, this skill will add ceremony without much gain.
How to Improve opportunity-solution-tree skill
Give the skill a real outcome
The biggest quality lever in opportunity-solution-tree is the outcome statement. Include a metric, time frame, and audience if possible: “Increase trial-to-paid conversion from 9% to 13% for SMB admins in Q3” is much stronger than “improve onboarding.”
Add evidence, not opinions
The skill works best when you supply signals like churn reasons, funnel drop-off, support themes, or stakeholder quotes. That helps it generate better opportunities and reduces generic solution lists that sound plausible but do not match the actual problem.
Force tradeoffs in the output
If you want a better opportunity-solution-tree skill result, ask for 2-3 opportunities, a small set of solutions per opportunity, and one recommended POC with explicit rationale. This keeps the tree readable and makes the recommendation actionable instead of encyclopedic.
Iterate after the first tree
Use the first pass to expose weak assumptions, then refine the prompt with constraints such as tech limits, release timing, research findings, or market segment differences. The opportunity-solution-tree guide becomes much more useful on the second iteration, when you can narrow to the most credible opportunities and tighten the experiment design.