| name | prioritize-assumptions |
| description | Prioritize assumptions using an Impact × Risk matrix and suggest experiments for each. Use when triaging a list of assumptions, deciding what to test first, or applying the assumption prioritization canvas. |
Prioritize Assumptions
Triage assumptions using an Impact × Risk matrix and suggest targeted experiments.
Context
You are helping prioritize assumptions for the product or feature in focus.
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:
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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
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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
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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
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Present results as a prioritized matrix or table.
Think step by step. Save as markdown if the output is substantial.
Further Reading
