Assumption Testing Frameworks in PM Skills: 9 Methods for Product Prioritization

PM Skills provides nine assumption testing frameworks—including Opportunity Score, ICE, RICE, and the Kano Model—centralized in the prioritization-frameworks skill with formulas, usage guidance, and downloadable templates.

The phuryn/pm-skills repository offers a dedicated reference skill that consolidates quantitative methods for validating product assumptions before committing engineering resources. These frameworks are exposed through the prioritization-frameworks skill and consumed by downstream commands like prioritize-assumptions to create Impact × Risk matrices.

The Nine Assumption Testing Frameworks

According to the source code in pm-execution/skills/prioritization-frameworks/SKILL.md, PM Skills implements the following quantitative and qualitative frameworks:

Opportunity Score (Dan Olsen) Measures Importance × (1 − Satisfaction) to surface high-value customer problems. Product managers use this when prioritizing problems before any solutions are built.

ICE Framework Calculates Impact × Confidence × Ease, where Impact equals Opportunity Score × number of customers, Confidence ranges 1-10, and Ease ranges 1-10. This method enables quick ranking of ideas when time is limited.

RICE Framework Scales ICE for larger teams using Reach × Impact × Confidence ÷ Effort. Reach represents the number of customers, Impact uses the Opportunity Score, Confidence ranges 0-100%, and Effort uses person-months.

Kano Model Classifies features into Must-be, Performance, Attractive, Indifferent, and Reverse categories. This framework excels at understanding customer expectations rather than pure assumption validation.

MoSCoW Method Categorizes requirements as Must have, Should have, Could have, or Won't have. This provides simple stakeholder alignment for scope decisions.

Eisenhower Matrix Maps tasks by Urgent versus Important quadrants. This serves as a personal task management tool for product managers.

Impact vs Effort A 2×2 matrix for quick triage of many ideas during initial screening phases.

Risk vs Reward Adds uncertainty dimensions to the Impact vs Effort matrix for decision-making where risk tolerance varies significantly.

Weighted Decision Matrix Supports multi-criteria scoring with custom weights for complex trade-offs involving many dimensions.

How the Prioritize-Assumptions Skill Works

The pm-product-discovery/skills/priorize-assumptions/SKILL.md file operationalizes these frameworks by applying ICE and RICE formulas to compute Impact scores. It then maps each assumption onto an Impact × Risk matrix, where Risk is calculated as (1 − Confidence) × Effort.

This matrix identifies high-impact/high-risk assumptions requiring immediate experimentation versus low-effort items that can be deferred. The skill references the framework definitions from pm-execution/skills/prioritization-frameworks/SKILL.md for all calculations.

Practical usage:

pm-product-discovery:prioritize-assumptions \
    --assumptions assumptions.txt \
    --framework ICE

This command performs the following steps:

1. Reads assumptions from assumptions.txt
2. Computes Impact using the ICE formula (Opportunity Score × number of customers)
3. Calculates Risk as (1 − Confidence) × Effort
4. Generates an Impact × Risk matrix with experiment recommendations for high-impact/high-risk items

Accessing Framework Templates

The repository provides downloadable Google Sheets and Excel templates for quantitative frameworks. According to pm-execution/skills/prioritization-frameworks/SKILL.md, templates are available for Opportunity Score, ICE, and RICE frameworks.

To retrieve a template:

pm-execution:download-template \
    --template opportunity-score \
    --output ./opportunity_score_template.xlsx

This command fetches the Google Sheets template referenced in the frameworks skill, allowing teams to input survey data (Importance and Satisfaction) to calculate Opportunity Scores for each problem.

Integration with Discovery Workflows

The assumption testing frameworks integrate into broader discovery workflows through pm-product-discovery/commands/discover.md. This orchestration file calls the identify-assumptions-* and prioritize-assumptions skills, applying the quantitative frameworks during the validation phase.

Summary

• PM Skills consolidates nine assumption testing frameworks in pm-execution/skills/prioritization-frameworks/SKILL.md
• ICE and RICE provide quantitative scoring formulas for Impact calculations
• The prioritize-assumptions skill applies these formulas to create an Impact × Risk matrix for experiment planning
• Ready-to-use Google Sheets templates are accessible via the download-template command
• Frameworks range from quick triage methods (Impact vs Effort) to complex multi-criteria analysis (Weighted Decision Matrix)

Frequently Asked Questions

What is the difference between ICE and RICE in PM Skills?

ICE uses a 1-10 scale for Confidence and Ease, making it suitable for rapid estimation. RICE replaces Ease with Effort measured in person-months and uses percentage-based Confidence (0-100%), providing granularity for larger teams. Both formulas are defined in pm-execution/skills/prioritization-frameworks/SKILL.md and referenced by the prioritize-assumptions skill.

How does the prioritize-assumptions skill calculate risk?

The skill calculates Risk using the formula (1 − Confidence) × Effort, where Confidence represents validation certainty and Effort indicates implementation cost. It plots this against the Impact score (calculated via ICE or RICE) to categorize assumptions into the Impact × Risk matrix.

Where can I find the templates for these frameworks?

Framework templates are stored as Google Sheets and referenced in pm-execution/skills/prioritization-frameworks/SKILL.md. Access them via the pm-execution:download-template command, which supports downloading Opportunity Score, ICE, and RICE templates as Excel files for local use.

Which framework should I use for early-stage problem validation?

Use the Opportunity Score framework when validating problems before solution development. This method measures Importance × (1 − Satisfaction) to identify high-value problems where customer importance is high but current satisfaction is low, indicating prime opportunities for new products.

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