假设优先级排序指南：使用影响×风险矩阵与ICE/RICE框架优化产品决策

prioritize-assumptions by phuryn/pm-skills

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安装命令

npx skills add https://github.com/phuryn/pm-skills --skill prioritize-assumptions

方法论项目管理产品管理

🇨🇳中文介绍

假设优先级排序

使用影响×风险矩阵对假设进行优先级排序，并建议有针对性的实验。

背景

您正在帮助为 $ARGUMENTS 确定假设的优先级。

如果用户提供了包含假设或研究数据的文件，请先阅读它们。

领域背景

ICE 框架非常适合用于假设优先级排序：影响（机会分数 × 客户数量）× 置信度（1–10）× 简易性（1–10）。机会分数 = 重要性 × （1 − 满意度），标准化到 0–1（Dan Olsen）。RICE 框架将影响拆分为覆盖范围和影响分别计算：（覆盖范围 × 影响 × 置信度）/ 简易性。完整的公式和模板请参见 prioritization-frameworks 技能。

操作指南

用户将提供一个需要确定优先级的假设列表。请应用以下框架：

针对每个假设，评估两个维度： * 影响：验证此假设所创造的价值以及受影响的客户数量（在 ICE 中：影响 = 机会分数 × 客户数量） * 风险：定义为（1 - 置信度） × 工作量
使用影响×风险矩阵对每个假设进行分类： * 低影响，低风险 → 推迟测试，直到处理完更高优先级的假设 * 高影响，低风险 → 进入实施阶段（低风险，高回报） * 低影响，高风险 → 拒绝该想法（不值得投入） * 高影响，高风险 → 设计一个实验来测试它
对于每个需要测试的假设，建议一个实验，该实验应： * 以最小的努力最大化已验证的学习成果 * 衡量实际行为，而非意见 * 具有明确的成功指标和阈值
将结果呈现为一个优先级排序的矩阵或表格。

请逐步思考。如果输出内容较多，请保存为 Markdown 格式。

🇺🇸English

Prioritize Assumptions

Triage assumptions using an Impact × Risk matrix and suggest targeted experiments.

Context

You are helping prioritize assumptions for $ARGUMENTS.

If the user provides files with assumptions or research data, read them first.

Domain Context

ICE works well for assumption prioritization: Impact (Opportunity Score × # Customers) × Confidence (1–10) × Ease (1–10). Opportunity Score = Importance × (1 − Satisfaction), normalized to 0–1 (Dan Olsen). RICE splits Impact into Reach × Impact separately: (R × I × C) / E. See the prioritization-frameworks skill for full formulas and templates.

Instructions

The user will provide a list of assumptions to prioritize. Apply the following framework:

For each assumption , evaluate two dimensions:
- Impact : The value created by validating this assumption AND the number of customers affected (in ICE: Impact = Opportunity Score × # Customers)
- Risk : Defined as (1 - Confidence) × Effort
Categorize each assumption using the Impact × Risk matrix:
- Low Impact, Low Risk → Defer testing until higher-priority assumptions are addressed
- High Impact, Low Risk → Proceed to implementation (low risk, high reward)
- Low Impact, High Risk → Reject the idea (not worth the investment)
- High Impact, High Risk → Design an experiment to test it
For each assumption requiring testing , suggest an experiment that:
- Maximizes validated learning with minimal effort
- Measures actual behavior, not opinions
- Has a clear success metric and threshold
Present results as a prioritized matrix or table.

Think step by step. Save as markdown if the output is substantial.