feature-prioritization-assistant

Domain Context

This skill implements a proven product management framework. The approach combines best practices from industry leaders and is designed for practical application in day-to-day PM work.

Input Requirements

• Context about your product, feature, or problem
• Relevant data, research, or constraints (recommended but optional)
• Clear articulation of what you're trying to achieve

Feature Prioritization Assistant

When to Use

• Building your product roadmap
• Need to choose between multiple feature ideas
• Stakeholders are debating which features to build first
• Want to make data-driven prioritization decisions
• Need to justify prioritization decisions to leadership

What This Skill Does

Helps you systematically evaluate and prioritize features using the RICE framework (Reach, Impact, Confidence, Effort), providing scores and recommendations.

Instructions

Help me prioritize these features using the RICE framework. For each feature, help me estimate:

1. Reach: How many users will this impact per month?
2. Impact: How much will this impact each user? (Scale: 0.25=minimal, 0.5=low, 1=medium, 2=high, 3=massive)
3. Confidence: How confident are we in our estimates? (Scale: 0-100%)
4. Effort: How many person-months will this take to build?

Then calculate the RICE score: (Reach × Impact × Confidence) / Effort

Features to evaluate: [List your features with any context you have]

Best Practices

• Gather data on current user behavior before estimating Reach
• Base Impact on user research and pain point severity
• Be honest about Confidence levels - lower confidence for assumptions
• Include design, development, and testing time in Effort estimates
• Revisit estimates after initial discovery work
• Consider dependencies between features

Example

Input: 5 features (notifications, dark mode, API access, mobile app, analytics dashboard) Output: RICE scores calculated for each, ranked list with reasoning, recommendations on which to prioritize, and suggestions for validating assumptions on low-c...