prioritize-assumptions

Topics in the source

• Prioritize Assumptions
• Context
• Domain Context
• Instructions
• Further Reading

Triage assumptions using an Impact × Risk matrix and suggest targeted experiments. You are helping prioritize assumptions for $ARGUMENTS. If the user provides files with assumptions or research data, read them first. 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. The user will provide a list of assumptions to prioritize. Apply the following framework: 1. 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 2. 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

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