prioritize-assumptions

prioritize-assumptions helps product teams triage a list of assumptions using an Impact x Risk matrix and suggests a targeted experiment for each assumption worth testing. It addresses the common problem of not knowing what to test first: rather than validating assumptions in arbitrary order, it sorts them by the value of validating them against the risk of leaving them unproven, so effort goes where learning matters most. If the user supplies files with assumptions or research data, the skill reads those first before applying the framework.

The skill evaluates each assumption on two dimensions. Impact is the value created by validating the assumption plus the number of customers affected (in ICE terms, Impact = Opportunity Score x number of Customers). Risk is defined as (1 - Confidence) x Effort. Each assumption is then categorized in a four-quadrant matrix: Low Impact/Low Risk means defer testing; High Impact/Low Risk means proceed to implementation; Low Impact/High Risk means reject the idea; and High Impact/High Risk means design an experiment to test it. For assumptions requiring testing, it suggests experiments that maximize validated learning with minimal effort, measure actual behavior rather than opinions, and have a clear success metric and threshold. Results are presented as a prioritized matrix or table. The skill also references ICE (Impact x Confidence x Ease, with Opportunity Score = Importance x (1 - Satisfaction) normalized to 0-1, per Dan Olsen) and RICE (Reach x Impact x Confidence / Effort), pointing to the prioritization-frameworks skill for full formulas.

It is aimed at product managers and discovery teams triaging assumptions, deciding what to test first, or applying the assumption prioritization canvas. The skill suggests saving substantial output as markdown and links to further reading including the Assumption Prioritization Canvas and the Continuous Product Discovery Masterclass.