正文
获取原文件SKILL.md
中文导读
将业务目标、客户机会、候选方案及实验组织成机会方案树，支持咨询和产品探索中的方案选择。
输入材料
- 期望业务结果
- 客户研究、机会与方案材料
交付结果
- 机会方案树
- 需要验证的实验方向
阅读与使用
本页保存指定版本的原始 Skill 和配套文件，可查看中文导读、原文与参考资料。获取完整包后，按原文准备所需工具与材料。
来源仓库为 phuryn/pm-skills，采用 MIT 许可。Stars 表示仓库关注度，核验日期为 2026-10-15。分类表示适用的工作环节，实际执行条件以原文为准。
原始 Skill
Opportunity Solution Tree (OST)
A visual framework for structuring continuous product discovery. Connects a desired outcome to customer opportunities, possible solutions, and experiments to validate them.
Domain Context
The Opportunity Solution Tree (Teresa Torres, Continuous Discovery Habits) is the backbone of modern product discovery. It prevents teams from jumping to solutions by forcing them to first map the opportunity space.
Structure (4 levels):
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Desired Outcome (top) — The measurable business or product outcome the team is pursuing. Should be a single, clear metric (e.g., "increase 7-day retention to 40%"). This comes from your OKRs or product strategy.
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Opportunities (second level) — Customer needs, pain points, or desires discovered through research. These are problems worth solving — not features. Frame them from the customer's perspective: "I struggle to..." or "I wish I could..." Prioritize using Opportunity Score: Importance × (1 − Satisfaction) (Dan Olsen, The Lean Product Playbook). Normalize Importance and Satisfaction to 0–1.
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Solutions (third level) — Possible ways to address each opportunity. Generate multiple solutions per opportunity — don't commit to the first idea. The Product Trio (PM + Designer + Engineer) should ideate together. "Best ideas often come from engineers."
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Experiments (bottom) — Fast, cheap tests to validate whether a solution actually addresses the opportunity. Use assumption testing (Value, Usability, Viability, Feasibility risks). Prefer experiments with "skin-in-the-game" (Alberto Savoia) over opinion-based validation.
Key principles:
- One outcome at a time. Don't try to solve everything. Focus the tree on a single desired outcome.
- Opportunities, not features. "Never allow customers to design solutions. Prioritize opportunities (problems), not features."
- Compare and contrast. Always generate at least 3 solutions per opportunity before choosing. Avoid the "first idea" trap.
- Discovery is not linear. Loop back if experiments fail. Kill solutions that don't validate. Explore new branches.
- Continuous, not periodic. Update the tree weekly as you learn from interviews, analytics, and experiments.
Instructions
You are helping a product team build an Opportunity Solution Tree for $ARGUMENTS.
Input Requirements
- A desired outcome or business metric to improve
- Customer research data (interviews, surveys, analytics, feedback)
- Optionally: existing opportunities or solution ideas to organize
Process
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Define the desired outcome — Confirm or help articulate a single, measurable outcome at the top of the tree.
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Map opportunities — From provided research, identify 3-7 customer opportunities (needs/pains). Group related opportunities. Frame each from the customer's perspective.
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Prioritize opportunities — Use Opportunity Score or qualitative assessment to rank. Focus on the top 2-3.
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Generate solutions — For each prioritized opportunity, brainstorm 3+ solutions from PM, Designer, and Engineer perspectives.
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Design experiments — For the most promising solutions, suggest 1-2 fast experiments. Specify: hypothesis, method, metric, success threshold.
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Visualize the tree — Present the full OST in a clear hierarchical format.
Think step by step. Save as markdown if substantial.