Define Opportunity Tree logo

Define Opportunity Tree

Organization
product-on-purpose
define-opportunity-tree

Creates an opportunity solution tree connecting a desired outcome to customer opportunities and candidate solutions, preventing solution-first jumps in continuous discovery. Use when structuring scattered feature ideas or communicating how the roadmap traces to outcomes. For ranking an existing flat list of candidates, use define-prioritization-framework instead; this skill discovers the list, it does not score one.

Overview

Publisherproduct-on-purpose
Repositorypm-skills
Skill namedefine-opportunity-tree
Stars
717
Forks
92
Bundled files
4
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by product-on-purpose on GitHub. Read the source before you install it.

Installation

Install the Define Opportunity Tree AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/product-on-purpose/pm-skills/tree/main/skills/define-opportunity-tree
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/product-on-purpose/pm-skills.git /tmp/pm-skills
mkdir -p .claude/skills
cp -r /tmp/pm-skills/skills/define-opportunity-tree .claude/skills/define-opportunity-tree
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Define Opportunity Tree in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Define Opportunity Tree on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Define Opportunity Tree is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Opportunity Solution Tree

An Opportunity Solution Tree (OST) is a visual framework for product discovery that connects business outcomes to customer opportunities and potential solutions. Developed by Teresa Torres, it prevents the common trap of jumping straight to solutions by ensuring every feature idea traces back to a customer need and measurable outcome.

When to Use

  • During continuous product discovery to organize learning
  • When prioritizing what opportunities to pursue
  • To communicate product strategy to stakeholders
  • When you have too many feature ideas and need structure
  • After user research to connect insights to action
  • When aligning team on what outcomes matter most

When NOT to Use

  • You need to score and rank a flat list of known candidates -> use define-prioritization-framework; the tree structures discovery, not a ranking exercise
  • You have one specific problem to frame for a team -> use define-problem-statement
  • You are ready to test a single assumption -> use define-hypothesis, then measure-experiment-design
  • The outcome you want to drive is not yet agreed -> set it first with foundation-okr-writer; a tree without an agreed outcome decorates opinions

Instructions

When asked to create an opportunity solution tree, follow these steps:

  1. Define the Desired Outcome Start at the top with a clear, measurable business or product outcome. This should be something you can influence through product changes. Express it quantitatively when possible (e.g., "Increase 30-day retention from 40% to 55%").

  2. Identify Opportunity Areas Branch out to 3-5 opportunity areas.places where customer needs or pain points could be addressed. Opportunities are not solutions; they're customer problems, needs, or desires. Phrase them from the customer's perspective.

  3. Add Supporting Evidence For each opportunity, note the evidence that supports it: user research quotes, behavioral data, support tickets, or market trends. Strong opportunities have multiple evidence sources.

  4. Brainstorm Solutions For each opportunity, generate 2-4 potential solutions. Don't self-censor at this stage. Solutions can range from quick experiments to major features. Keep them specific enough to evaluate.

  5. Define Assumption Tests For each promising solution, identify the riskiest assumption and design a lightweight experiment to test it. Good tests validate whether the solution will actually address the opportunity.

  6. Prioritize the Tree Not all branches are equal. Mark which opportunity and solution you'll pursue first based on potential impact, confidence, and effort. The tree is a living document.you'll iterate as you learn.

  7. Visualize the Structure Create a tree diagram showing the hierarchy: outcome at top, opportunities below, solutions beneath each opportunity, and experiments at the leaves.

Output Format

Use the template in references/TEMPLATE.md to structure the output. A complete tree fills every template section: Desired Outcome; Visual Tree; Opportunity Branches; Prioritization; Experiments Backlog; Learning Log; and Next Steps.

Quality Checklist

Before finalizing, verify:

  • Outcome is measurable and within product team's influence
  • Opportunities are customer-centric (needs/problems, not features)
  • Each opportunity has supporting evidence documented
  • Multiple solutions exist per opportunity (not jumping to one)
  • Assumptions are explicit and experiments designed
  • Prioritization is clear (which branch to explore first)

Examples

See references/EXAMPLE.md for a completed example.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Define Opportunity Tree AI skill do?

Creates an opportunity solution tree connecting a desired outcome to customer opportunities and candidate solutions, preventing solution-first jumps in continuous discovery. Use when structuring scattered feature ideas or communicating how the roadmap traces to outcomes. For ranking an existing flat list of candidates, use define-prioritization-framework instead; this skill discovers the list, it does not score one.

Why use Define Opportunity Tree on TypingMind?

Because you install it once and use it with any model. Define Opportunity Tree is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Define Opportunity Tree in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/product-on-purpose/pm-skills/tree/main/skills/define-opportunity-tree. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Define Opportunity Tree?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Define Opportunity Tree?

As many as you like. As long as a model supports skills, you can use Define Opportunity Tree with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Define Opportunity Tree AI skill free?

Yes. It is published on GitHub by product-on-purpose under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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