The Feature Prioritizer skill helps AI Product Managers make data-driven decisions about what to build next. It applies multiple prioritization frameworks (RICE, ICE, Value vs. Effort), generates comparison matrices, and provides strategic recommendations for feature roadmapping.

When to Use This Skill

Planning quarterly or annual roadmaps
Evaluating a large feature backlog
Making build vs. buy vs. partner decisions
Responding to stakeholder feature requests
Resolving conflicts about priorities
Communicating trade-offs to leadership
Balancing technical debt vs. new features
Comparing AI/ML investment opportunities

PM Frameworks Applied

RICE Scoring: Reach × Impact × Confidence / Effort
ICE Scoring: (Impact + Confidence + Ease) / 3
Value vs. Effort Matrix: 2x2 matrix for visual prioritization
Weighted Scoring: Custom weights for company priorities
Strategic Alignment: Fit with company OKRs and vision
Dependency Mapping: Understanding feature relationships
Risk-Adjusted Scoring: Accounting for uncertainty

Inputs Required

{
  "features": [
    {
      "name": "string",
      "description": "string",
      "reach": "number (users affected per quarter)",
      "impact": "number (0.25-3.0 scale)",
      "confidence": "number (0.0-1.0 scale)",
      "effort": "number (person-months)",
      "strategic_alignment": "number (1-5 scale, optional)",
      "dependencies": ["list of feature names, optional"],
      "risk_level": "string (low/medium/high, optional)"
    }
  ],
  "company_priorities": {
    "growth_weight": "number (0.0-1.0, optional)",
    "retention_weight": "number (0.0-1.0, optional)",
    "monetization_weight": "number (0.0-1.0, optional)"
  },
  "constraints": {
    "max_effort_per_quarter": "number (optional)",
    "required_features": ["list, optional"]
  }
}

Outputs Produced

Prioritized Feature List - Features ranked by RICE and ICE scores
Value vs. Effort Matrix - Visual 2x2 matrix in ASCII or data format
Strategic Recommendations - Which features to build, defer, or cut
Roadmap Scenarios - Different options based on resource availability
Risk Analysis - Highlighting high-risk features
Dependency Map - Features that should be built together
Trade-off Analysis - What you're giving up by choosing certain features

Usage Instructions

Basic Invocation

Prioritize these features using RICE: [list 3-5 features with reach, impact, confidence, effort]

Detailed Invocation

I need to prioritize my feature backlog for Q1 planning. Here are the features:

1. AI-powered search - 50K users, high impact (2.0), 80% confidence, 4 months effort
2. Mobile app dark mode - 100K users, low impact (0.5), 95% confidence, 1 month effort
3. Advanced analytics dashboard - 10K users, massive impact (3.0), 60% confidence, 6 months effort
4. Social sharing - 75K users, medium impact (1.0), 70% confidence, 2 months effort

Company priorities: 60% growth, 30% retention, 10% monetization
Constraint: Maximum 8 person-months available

Generate RICE scores, ICE scores, value vs. effort matrix, and recommend Q1 roadmap.

Best Practices

Use consistent units - Always measure reach in same timeframe (e.g., quarterly)
Calibrate impact scale - 0.25=minimal, 0.5=low, 1.0=medium, 2.0=high, 3.0=massive
Be honest about confidence - Low confidence should penalize risky bets
Include all effort - Design, engineering, testing, documentation
Consider dependencies - Some features unlock others
Validate with stakeholders - Scoring is subjective, get alignment
Revisit regularly - Priorities change, update scores quarterly
Balance frameworks - No single framework is perfect
Account for strategic value - Some features are strategic bets

Composition with Other Skills

Recommended Workflow

Before Prioritization:
- user-research-analyzer → Understand user needs
- competitive-analyzer → Know market dynamics
- metrics-dashboard-builder → Define success metrics
During Prioritization:
- Use this skill (feature-prioritizer) → Score and rank features
- Iterate on assumptions with team
After Prioritization:
- prd-generator → Create PRDs for top features
- roadmap-planner → Build visual roadmap
- stakeholder-communicator → Explain priorities and trade-offs

Common Pitfalls to Avoid

Optimism bias - Overestimating impact, underestimating effort
Recency bias - Overweighting recently requested features
HiPPO effect - Letting highest paid person's opinion dominate
Analysis paralysis - Over-analyzing instead of deciding
Ignoring technical debt - Only prioritizing new features
Missing dependencies - Building features in wrong order
Forgetting maintenance - Not accounting for support burden
Local optimization - Optimizing for team vs. company goals

Prioritization Framework Details

RICE Scoring

Formula: (Reach × Impact × Confidence) / Effort

When to use:

Large feature sets (10+ features)
Need quantitative justification
Want to compare very different features

Strengths:

Comprehensive and objective
Forces thinking about all dimensions
Easy to communicate to stakeholders

Limitations:

Requires good data/estimates
Doesn't capture strategic value well
Can be gamed if not careful

ICE Scoring

Formula: (Impact + Confidence + Ease) / 3

When to use:

Quick prioritization needed
Early-stage features with limited data
Evaluating experiments or bets

Strengths:

Simple and fast
Good for experiments
Less data-intensive than RICE

Limitations:

Doesn't account for reach
All factors weighted equally
More subjective

Value vs. Effort Matrix

Quadrants:

Quick Wins (High Value, Low Effort) → Build first
Big Bets (High Value, High Effort) → Plan carefully
Fill Ins (Low Value, Low Effort) → Build if capacity
Time Sinks (Low Value, High Effort) → Avoid

When to use:

Visual communication to executives
Simplifying complex decisions
First-pass filtering of backlog

Python Functions

`calculate_rice(reach: int, impact: float, confidence: float, effort: float) -> float`

Calculates RICE prioritization score.

`calculate_ice(impact: float, confidence: float, ease: float) -> float`

Calculates ICE prioritization score.

`generate_value_effort_matrix(features: list) -> dict`

Creates 2x2 matrix categorizing features into quadrants.

`apply_strategic_weights(features: list, weights: dict) -> list`

Adjusts scores based on company strategic priorities.

`identify_dependencies(features: list) -> dict`

Maps feature dependencies and suggests build order.

`generate_roadmap_scenarios(features: list, constraints: dict) -> list`

Creates different roadmap options based on resource constraints.

`assess_portfolio_balance(features: list) -> dict`

Analyzes if feature mix balances growth, retention, technical debt, etc.

Output Format Example

## Feature Prioritization Results

### RICE Scores (Ranked)

| Rank | Feature | Reach | Impact | Confidence | Effort | RICE Score |
|------|---------|-------|--------|------------|--------|------------|
| 1 | Feature A | 50,000 | 2.0 | 80% | 4 | 20.0 |
| 2 | Feature B | 100,000 | 0.5 | 95% | 1 | 47.5 |

### Value vs. Effort Matrix

│

High │ [Big Bets] [Quick Wins] │ Feature A Feature B VALUE│ │ [Time Sinks] [Fill Ins] Low │ Feature C Feature D │ └───────────────────────────── High Low EFFORT


### Strategic Recommendations

**Build Now (Q1)**:
1. Feature B - Quick win with massive reach
2. Feature A - Strategic bet, high impact

**Defer to Q2**:
- Feature D - Low value, wait for capacity

**Cut from Roadmap**:
- Feature C - Time sink, recommend alternative approach

feature-prioritizer

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