Triage assumptions using an Impact × Risk matrix and suggest targeted experiments.
Context
You are helping prioritize assumptions for $ARGUMENTS.
If the user provides files with assumptions or research data, read them first.
Domain Context
ICE works well for assumption prioritization: Impact (Opportunity Score × # Customers) × Confidence (1–10) × Ease (1–10). Opportunity Score = Importance × (1 − Satisfaction), normalized to 0–1 (Dan Olsen). RICE splits Impact into Reach × Impact separately: (R × I × C) / E. See the prioritization-frameworks skill for full formulas and templates.
Instructions
The user will provide a list of assumptions to prioritize. Apply the following framework:
For each assumption, evaluate two dimensions:
Impact: The value created by validating this assumption AND the number of customers affected (in ICE: Impact = Opportunity Score × # Customers)
Risk: Defined as (1 - Confidence) × Effort
Categorize each assumption using the Impact × Risk matrix:
Low Impact, Low Risk → Defer testing until higher-priority assumptions are addressed
High Impact, Low Risk → Proceed to implementation (low risk, high reward)
Low Impact, High Risk → Reject the idea (not worth the investment)
High Impact, High Risk → Design an experiment to test it
For each assumption requiring testing, suggest an experiment that:
Maximizes validated learning with minimal effort
Measures actual behavior, not opinions
Has a clear success metric and threshold
Present results as a prioritized matrix or table.
Think step by step. Save as markdown if the output is substantial.
1---2name: prioritize-assumptions3description: Prioritize Assumptions4---5## Prioritize Assumptions67Triage assumptions using an Impact × Risk matrix and suggest targeted experiments.89### Context1011You are helping prioritize assumptions for **$ARGUMENTS**.1213If the user provides files with assumptions or research data, read them first.1415### Domain Context1617**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.1819### Instructions2021The user will provide a list of assumptions to prioritize. Apply the following framework:22231. **For each assumption**, evaluate two dimensions:24 - **Impact**: The value created by validating this assumption AND the number of customers affected (in ICE: Impact = Opportunity Score × # Customers)25 - **Risk**: Defined as (1 - Confidence) × Effort26272. **Categorize each assumption** using the Impact × Risk matrix:28 - **Low Impact, Low Risk** → Defer testing until higher-priority assumptions are addressed29 - **High Impact, Low Risk** → Proceed to implementation (low risk, high reward)30 - **Low Impact, High Risk** → Reject the idea (not worth the investment)31 - **High Impact, High Risk** → Design an experiment to test it32333. **For each assumption requiring testing**, suggest an experiment that:34 - Maximizes validated learning with minimal effort35 - Measures actual behavior, not opinions36 - Has a clear success metric and threshold37384. **Present results** as a prioritized matrix or table.3940Think step by step. Save as markdown if the output is substantial.4142---4344### Further Reading4546- [Assumption Prioritization Canvas: How to Identify And Test The Right Assumptions](https://www.productcompass.pm/p/assumption-prioritization-canvas)47- [Continuous Product Discovery Masterclass (CPDM)](https://www.productcompass.pm/p/cpdm) (video course)
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ComeOnOliver (@comeonoliver) published this skill. Their other Agent Skills are listed on their SkillMD profile.
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