prioritize-assumptions

phuryn/pm-skills

Prioritize assumptions using Impact × Risk matrix and design targeted experiments.

What is prioritize-assumptions?

This skill helps you triage assumptions by evaluating their impact and risk, then categorizing them into a matrix to decide what to test first. Use it when you have a list of assumptions to validate, need to decide testing priorities, or want to apply the assumption prioritization canvas framework.

Evaluate assumptions across Impact (opportunity score × customer count) and Risk (1 - confidence × effort) dimensions
Categorize assumptions into four quadrants: defer low-impact/low-risk, implement high-impact/low-risk, reject low-impact/high-risk, and experiment on high-impact/high-risk
Design targeted experiments for assumptions requiring validation that maximize learning with minimal effort
Support both ICE and RICE prioritization frameworks for scoring
Present results as a prioritized matrix or table for decision-making

How to install prioritize-assumptions

npx skills add https://github.com/phuryn/pm-skills --skill prioritize-assumptions

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How to use prioritize-assumptions

1.Gather your list of assumptions about the product, feature, or business model you're evaluating
2.For each assumption, estimate its Impact (opportunity score × number of customers affected) and Risk (1 - confidence × effort required)
3.Plot each assumption on the Impact × Risk matrix to categorize it into one of four quadrants
4.For high-impact/high-risk assumptions, work with the skill to design an experiment that measures actual behavior with a clear success metric
5.Review the prioritized results and decide which assumptions to test, implement, or defer based on the matrix positioning

Use cases

Good for

Triaging a backlog of product assumptions to determine which to validate first
Deciding between multiple feature ideas by testing their core assumptions before building
Applying the Assumption Prioritization Canvas to a new product or feature initiative
Validating go/no-go decisions on strategic bets using experiment design
Prioritizing customer discovery efforts when resources are limited

Who it's for

Product managers evaluating feature viability
Startup founders validating business model assumptions
Product teams deciding what to build next
Innovation teams testing new market opportunities
Anyone applying lean product development or continuous discovery

prioritize-assumptions FAQ

What's the difference between ICE and RICE scoring?

ICE uses Impact (Opportunity Score × # Customers) × Confidence × Ease, while RICE separates Impact into Reach × Impact, then divides by Effort: (R × I × C) / E. Both work for assumption prioritization; choose based on your context and available data.

How do I measure 'Risk' for an assumption?

Risk is calculated as (1 - Confidence) × Effort. Confidence is your belief the assumption is true (1–10 scale), and Effort is the cost/time to test it. Higher confidence and lower effort = lower risk.

What makes a good experiment for validating an assumption?

A good experiment maximizes validated learning with minimal effort, measures actual behavior (not opinions), and has a clear success metric and threshold for deciding whether the assumption is validated.

When should I defer testing an assumption?

Defer low-impact/low-risk assumptions until higher-priority assumptions are addressed. These won't significantly affect your product or business, so they're not worth testing first.

Can I use this skill without the prioritization-frameworks skill?

Yes, this skill includes the core ICE and RICE formulas. The prioritization-frameworks skill provides additional templates and detailed guidance if you want deeper reference material.

Full instructions (SKILL.md)

Source of truth, from phuryn/pm-skills.

name: prioritize-assumptions description: "Prioritize assumptions using an Impact × Risk matrix and suggest experiments for each. Use when triaging a list of assumptions, deciding what to test first, or applying the assumption prioritization canvas."

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.