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.
Further Reading
- Assumption Prioritization Canvas: How to Identify And Test The Right Assumptions
- Continuous Product Discovery Masterclass (CPDM) (video course)
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