Prioritization Frameworks in the pm-skills Repository: 9 Methods for Product Managers

The prioritization-frameworks skill in the phuryn/pm-skills repository documents nine distinct prioritization methods—Eisenhower Matrix, Impact vs Effort, Risk vs Reward, Opportunity Score, Kano Model, Weighted Decision Matrix, ICE, RICE, and MoSCoW—each with specific formulas, use cases, and implementation guidance.

The prioritization-frameworks skill serves as a canonical reference guide for product managers deciding how to rank work and initiatives. Located in the phuryn/pm-skills open-source repository, this skill provides formulas, practical use cases, and template links in pm-execution/skills/prioritization-frameworks/SKILL.md.

Overview of the Prioritization-Frameworks Skill

According to the phuryn/pm-skills source code, the prioritization-frameworks skill is designed as a standalone reference that product managers can consult when determining how to prioritize tasks, features, or strategic initiatives. Unlike other skills that may execute commands, this skill functions as a documentation resource that other skills reference automatically when prioritization guidance is needed.

The complete documentation resides in pm-execution/skills/prioritization-frameworks/SKILL.md, which defines the frameworks, their mathematical formulas where applicable, and criteria for selecting the appropriate method.

The 9 Prioritization Frameworks Available

The skill documents nine distinct frameworks, each suited for different decision-making contexts:

• Eisenhower Matrix – Use for personal task management by separating tasks into four quadrants based on Urgent vs Important criteria.
• Impact vs Effort – Apply for quick triage of initiatives using a simple 2×2 plot of expected impact against required effort.
• Risk vs Reward – Select when uncertainty matters; this framework plots risk against potential reward to balance high-risk, high-reward ideas.
• Opportunity Score – Recommended for prioritizing customer problems using the formula: Opportunity Score = Importance × (1 − Satisfaction).
• Kano Model – Use for understanding customer expectations by categorizing features as Must-be, Performance, Attractive, Indifferent, or Reverse.
• Weighted Decision Matrix – Apply for multi-criteria stakeholder decisions by assigning weights to criteria, scoring each option, and computing a weighted sum.
• ICE – Use for rapid idea prioritization with the formula: Score = Impact × Confidence × Ease.
• RICE – Select for larger teams needing granular scoring: Score = (Reach × Impact × Confidence) / Effort.
• MoSCoW – Apply for requirement-level prioritization by classifying items as Must, Should, Could, or Won't have.

Calculating Priority Scores: Code Examples

For the three quantitative frameworks—Opportunity Score, ICE, and RICE—the skill provides specific formulas that you can implement directly. Below are Python implementations based on the documentation in SKILL.md (see lines 14-23 for Opportunity Score, lines 27-35 for ICE, and lines 37-46 for RICE):

def opportunity_score(importance, satisfaction):
    """
    importance: float 0-1 (how important the problem is)
    satisfaction: float 0-1 (current satisfaction level)
    Returns the opportunity score.
    """
    return importance * (1 - satisfaction)


def ice_score(impact, confidence, ease):
    """
    impact: numeric (e.g., Opportunity Score × # customers)

    confidence: 1-10
    ease: 1-10
    Returns the ICE score.
    """
    return impact * confidence * ease


def rice_score(reach, impact, confidence, effort):
    """
    reach: number of customers/users affected
    impact: numeric value per customer (e.g., Opportunity Score)
    confidence: 0-1 (percentage as a decimal)
    effort: person-months (or any effort unit)
    Returns the RICE score.
    """
    return (reach * impact * confidence) / effort


# Example usage

importance = 0.9
satisfaction = 0.2
opp = opportunity_score(importance, satisfaction)        # → 0.72

# ICE example (assume 5,000 customers affected)

impact = opp * 5000
ice = ice_score(impact, confidence=8, ease=7)           # → 0.72 × 5000 × 8 × 7

# RICE example

reach = 5000
confidence = 0.9
effort = 4   # person-months

rice = rice_score(reach, opp, confidence, effort)       # → (5000 × 0.72 × 0.9) / 4

These functions translate the exact formulas documented in the skill, allowing you to process survey data, analytics, or product backlog items to generate comparable priority scores across initiatives.

Key Files and Integration Points

The prioritization frameworks are distributed across several files in the repository:

• pm-execution/skills/prioritization-frameworks/SKILL.md – The canonical reference listing all nine frameworks, formulas, and template links (Google Sheets/Slides).
• pm-execution/README.md – Overview of the execution area mentioning the prioritization-frameworks skill as a standalone reference.
• README.md (repo root) – Explains that skills like prioritization-frameworks are consulted automatically without requiring dedicated commands.
• pm-product-discovery/skills/prioritize-features/SKILL.md – Demonstrates how other skills reference the prioritization-frameworks skill for guidance.
• pm-product-discovery/skills/prioritize-assumptions/SKILL.md – Another example of cross-skill referencing for prioritization logic.

Summary

• The prioritization-frameworks skill in phuryn/pm-skills provides nine distinct methods for ranking work, from qualitative matrices to quantitative scoring models.
• Quantitative frameworks (Opportunity Score, ICE, RICE) provide mathematical formulas for calculating priority scores based on reach, impact, confidence, and effort.
• Qualitative frameworks (Eisenhower Matrix, Kano Model, MoSCoW) offer categorical approaches for task management and requirement classification.
• Implementation code for the scoring formulas is available in Python and can be integrated directly into data processing pipelines.
• The primary documentation resides in pm-execution/skills/prioritization-frameworks/SKILL.md, with other skills referencing this file for prioritization guidance.

Frequently Asked Questions

What is the difference between ICE and RICE prioritization frameworks?

ICE stands for Impact, Confidence, and Ease, calculating priority as Impact × Confidence × Ease. RICE adds Reach to the calculation and reframes the variables as Score = (Reach × Impact × Confidence) / Effort. According to the source code, ICE works best for rapid idea prioritization, while RICE suits larger teams needing granularity across customer reach and effort investment.

How do I calculate the Opportunity Score for customer problems?

The Opportunity Score formula is Importance × (1 − Satisfaction), where both variables are typically expressed as decimals between 0 and 1. You measure how important a problem is to customers and subtract their current satisfaction level from 1. As documented in lines 14-23 of SKILL.md, this framework is specifically recommended for prioritizing customer problems.

Where can I find templates for these prioritization frameworks?

The prioritization-frameworks skill includes references to Google Sheets and Google Slides templates within pm-execution/skills/prioritization-frameworks/SKILL.md. These templates provide pre-built structures for applying the Eisenhower Matrix, Weighted Decision Matrix, and scoring calculations for ICE and RICE.

Can I use these frameworks outside of product management?

While the phuryn/pm-skills repository targets product managers, the prioritization frameworks are domain-agnostic. The Eisenhower Matrix works for personal task management, the Weighted Decision Matrix suits any multi-criteria decision, and MoSCoW applies to any project requirements. The mathematical formulas for ICE, RICE, and Opportunity Score can be adapted to any initiative requiring quantitative prioritization.

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