prioritize-assumptions

## 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: 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** → Design an experiment to test it 3. **For each assumption requiring testing**, suggest an experiment that: - Maximizes validated learn...