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)