prioritize-assumptions
This Claude Code skill evaluates a list of assumptions using an Impact × Risk matrix to determine testing priorities and suggest targeted experiments. Use it when triaging assumptions for a product or strategy, deciding which assumptions to validate first, or applying assumption prioritization methodologies like ICE or RICE scoring frameworks to guide resource allocation and experiment design.
git clone --depth 1 https://github.com/phuryn/pm-skills /tmp/prioritize-assumptions && cp -r /tmp/prioritize-assumptions/pm-product-discovery/skills/prioritize-assumptions ~/.claude/skills/prioritize-assumptionsSKILL.md
## 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)
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