Prioritize Assumptions
Triage assumptions using an Impact × Risk matrix and suggest targeted experiments.
Context
You are helping prioritize assumptions for $ARGUMENTS.
If the user provides files with assumptions or research data, read them first.
Domain Context
ICE works well for assumption prioritization: Impact (Opportunity Score × # Customers) × Confidence (1–10) × Ease (1–10). Opportunity Score = Importance × (1 − Satisfaction), normalized to 0–1 (Dan Olsen). RICE splits Impact into Reach × Impact separately: (R × I × C) / E. See the prioritization-frameworks skill for full formulas and templates.
Instructions
The user will provide a list of assumptions to prioritize. Apply the following framework:
For each assumption, evaluate two dimensions:
- Impact: The value created by validating this assumption AND the number of customers affected (in ICE: Impact = Opportunity Score × # Customers)
- Risk: Defined as (1 - Confidence) × Effort
Categorize each assumption using the Impact × Risk matrix:
- Low Impact, Low Risk → Defer testing until higher-priority assumptions are addressed
- High Impact, Low Risk → Proceed to implementation (low risk, high reward)
- Low Impact, High Risk → Reject the idea (not worth the investment)
- High Impact, High Risk → Design an experiment to test it
For each assumption requiring testing, suggest an experiment that:
- Maximizes validated learning with minimal effort
- Measures actual behavior, not opinions
- Has a clear success metric and threshold
Present results as a prioritized matrix or table.
Think step by step. Save as markdown if the output is substantial.
Further Reading
---
name: prioritize-assumptions
description: Prioritize assumptions using an Impact × Risk matrix and suggest targeted experiments for each.
---
## Prioritize Assumptions
Triage assumptions using an Impact × Risk matrix and suggest targeted experiments.
### Context
You are helping prioritize assumptions for **$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 learning with minimal effort
- Measures actual behavior, not opinions
- Has a clear success metric and threshold
4. **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](https://www.productcompass.pm/p/assumption-prioritization-canvas)
- [Continuous Product Discovery Masterclass (CPDM)](https://www.productcompass.pm/p/cpdm) (video course)
