a5c-ai/prioritization-calculator

Prioritization Calculator Skill

Calculate and validate scores for multiple prioritization frameworks with weighted scoring, normalization, and confidence-adjusted rankings.

Overview

This skill provides robust calculation engines for popular prioritization frameworks used in product management. It handles score normalization, validation, custom weighting, and generates actionable priority rankings.

Capabilities

RICE Scoring

• Calculate Reach, Impact, Confidence, Effort scores
• Normalize across different scale systems
• Apply team capacity adjustments
• Generate confidence-adjusted rankings

ICE Scoring

• Impact, Confidence, Ease calculations
• Comparative scoring across features
• Quick prioritization for growth experiments
• Sensitivity analysis

MoSCoW Categorization

• Must-have, Should-have, Could-have, Won't-have classification
• Constraint-based categorization rules
• Dependency-aware sorting
• Release planning integration

Weighted Scoring

• Custom prioritization frameworks
• Multiple criteria weighting
• Stakeholder preference integration
• Dynamic weight adjustment

Score Analysis

• Normalize scores across data sources
• Generate priority rankings with intervals
• Identify score clustering and outliers
• Track prioritization changes over time

Prerequisites

Input Data Format

{
  "items": [
    {
      "id": "FEAT-001",
      "name": "Feature name",
      "scores": {
        "reach": 5000,
        "impact": 2,
        "confidence": 0.8,
        "effort": 3
      },
      "metadata": {
        "theme": "growth",
        "requestedBy": "sales"
      }
    }
  ],
  "config": {
    "framework": "rice",
    "effortUnit": "person_weeks",
    "teamCapacity": 20
  }
}

Usage Patterns

RICE Score Calculation

## RICE Formula

RICE Score = (Reach * Impact * Confidence) / Effort

### Scale Definitions

**Reach**: Number of users/customers affected per quarter
- Estimate conservatively
- Use data when available

**Impact**: Expected effect on users
| Score | Meaning |
|-------|---------|
| 3 | Massive impact |
| 2 | High impact |
| 1 | Medium impact |
| 0.5 | Low impact |
| 0.25 | Minimal impact |

**Confidence**: How certain are we?
| Score | Meaning |
|-------|---------|
| 1.0 | High confidence - solid data |
| 0.8 | Medium confidence - some data |
| 0.5 | Low confidence - educated guess |

**Effort**: Person-months or person-weeks
- Include all work: design, dev, QA, launch
- Round up for unknowns

RICE Calculation Example

# RICE Score Calculator

def calculate_rice(reach, impact, confidence, effort):
    """
    Calculate RICE score for prioritization.

    Args:
        reach: Users affected per quarter
        impact: Impact score (0.25, 0.5, 1, 2, or 3)
        confidence: Confidence level (0.5, 0.8, or 1.0)
        effort: Person-weeks of effort

    Returns:
        RICE score
    """
    if effort <= 0:
        raise ValueError("Effort must be positive")

    rice_score = (reach * impact * confidence) / effort
    return rice_score

# Example features
features = [
    {"name": "Search improvements", "reach": 10000, "impact": 2, "confidence": 0.8, "effort": 4},
    {"name": "Export to PDF", "reach": 2000, "impact": 1, "confidence": 1.0, "effort": 2},
    {"name": "AI suggestions", "reach": 5000, "impact": 3, "confidence": 0.5, "effort": 8},
]

# Calculate and rank
for feature in features:
    feature["rice_score"] = calculate_rice(
        feature["reach"],
        feature["impact"],
        feature["confidence"],
        feature["effort"]
    )

ranked = sorted(features, key=lambda x: x["rice_score"], reverse=True)

ICE Score Calculation

## ICE Formula

ICE Score = Impact * Confidence * Ease

### Scale Definitions (1-10 for each)

**Impact**: How much will this move the metric?
- 10: Massive improvement
- 5: Moderate improvement
- 1: Minimal improvement

**Confidence**: How sure are we it will work?
- 10: Very confident (tested/proven)
- 5: Somewhat confident (similar worked)
- 1: Pure hypothesis

**Ease**: How easy to implement?
- 10: Trivial (hours)
- 5: Moderate (days)
- 1: Complex (weeks+)

ICE Calculation Example

def calculate_ice(impact, confidence, ease):
    """
    Calculate ICE score for growth prioritization.

    Args:
        impact: 1-10 impact on target metric
        confidence: 1-10 confidence level
        ease: 1-10 ease of implementation

    Returns:
        ICE score (1-1000)
    """
    return impact * confidence * ease

# Example experiments
experiments = [
    {"name": "New CTA color", "impact": 3, "confidence": 5, "ease": 10},
    {"name": "Simplified checkout", "impact": 8, "confidence": 7, "ease": 4},
    {"name": "Social proof", "impact": 5, "confidence": 8, "ease": 7},
]

for exp in experiments:
    exp["ice_score"] = calculate_ice(exp["impact"], exp["confidence"], exp["ease"])

ranked = sorted(experiments, key=lambda x: x["ice_score"], reverse=True)

MoSCoW Classification

## MoSCoW Framework

### Must Have (M)
- Non-negotiable for launch
- Failure without it is unacceptable
- Core to the value proposition

### Should Have (S)
- Important but not critical
- Workarounds exist
- Next priority after Must

### Could Have (C)
- Nice to have
- Would enhance but not required
- Do if time permits

### Won't Have (W)
- Explicitly out of scope
- Deferred to future
- Agreed not to do now

## Classification Rules

```python
def classify_moscow(item, constraints):
    """
    Classify item into MoSCoW category.

    Args:
        item: Feature/requirement
        constraints: Release constraints

    Returns:
        MoSCoW category
    """
    # Must Have criteria
    if item.is_regulatory or item.blocks_launch:
        return "M"

    # Should Have criteria
    if item.impact_score >= 0.7 and item.fits_budget:
        return "S"

    # Could Have criteria
    if item.impact_score >= 0.4:
        return "C"

    # Won't Have
    return "W"

### Weighted Scoring Framework
```markdown
## Custom Weighted Scoring

### Define Criteria
| Criterion | Weight | Scale |
|-----------|--------|-------|
| Strategic Alignment | 25% | 1-5 |
| Revenue Impact | 20% | 1-5 |
| Customer Demand | 20% | 1-5 |
| Technical Feasibility | 15% | 1-5 |
| Competitive Pressure | 10% | 1-5 |
| Risk Level (inverse) | 10% | 1-5 |

### Calculation
```python
def weighted_score(item, criteria_weights):
    """
    Calculate weighted prioritization score.

    Args:
        item: Dict with scores for each criterion
        criteria_weights: Dict with weights (must sum to 1.0)

    Returns:
        Weighted score
    """
    total = 0
    for criterion, weight in criteria_weights.items():
        score = item.get(criterion, 0)
        total += score * weight
    return total

## Integration with Babysitter SDK

### Task Definition Example
```javascript
const prioritizationTask = defineTask({
  name: 'feature-prioritization',
  description: 'Calculate prioritization scores for features',

  inputs: {
    features: { type: 'array', required: true },
    framework: { type: 'string', default: 'rice' },
    customWeights: { type: 'object', default: null },
    teamCapacity: { type: 'number', default: null }
  },

  outputs: {
    rankedFeatures: { type: 'array' },
    scoreDistribution: { type: 'object' },
    recommendations: { type: 'array' }
  },

  async run(inputs, taskCtx) {
    return {
      kind: 'skill',
      title: `Calculate ${inputs.framework.toUpperCase()} scores`,
      skill: {
        name: 'prioritization-calculator',
        context: {
          operation: 'calculate_scores',
          features: inputs.features,
          framework: inputs.framework,
          customWeights: inputs.customWeights,
          teamCapacity: inputs.teamCapacity
        }
      },
      io: {
        inputJsonPath: `tasks/${taskCtx.effectId}/input.json`,
        outputJsonPath: `tasks/${taskCtx.effectId}/result.json`
      }
    };
  }
});

Output Formats

Prioritization Report

# Feature Prioritization Report

## Framework: RICE
## Date: 2026-01-24
## Team Capacity: 20 person-weeks/quarter

## Rankings

| Rank | Feature | RICE Score | Reach | Impact | Confidence | Effort |
|------|---------|------------|-------|--------|------------|--------|
| 1 | Search improvements | 4000 | 10000 | 2 | 0.8 | 4 |
| 2 | Export to PDF | 1000 | 2000 | 1 | 1.0 | 2 |
| 3 | AI suggestions | 937 | 5000 | 3 | 0.5 | 8 |

## Capacity Analysis

- Total available: 20 person-weeks
- Top 2 features: 6 person-weeks (fits)
- All 3 features: 14 person-weeks (fits)

## Recommendations

1. **Execute**: Search improvements (highest score, proven impact)
2. **Execute**: Export to PDF (high confidence, low effort)
3. **Validate**: AI suggestions (high impact but low confidence - run experiment first)

Score Distribution Analysis

{
  "framework": "rice",
  "items_scored": 15,
  "score_statistics": {
    "mean": 2450,
    "median": 1800,
    "std_dev": 1200,
    "min": 250,
    "max": 5800
  },
  "clusters": [
    {
      "name": "high_priority",
      "threshold": ">3000",
      "count": 3
    },
    {
      "name": "medium_priority",
      "threshold": "1000-3000",
      "count": 7
    },
    {
      "name": "low_priority",
      "threshold": "<1000",
      "count": 5
    }
  ]
}

Best Practices

1. Consistent Scales: Use same scale definitions across all scorers
2. Document Assumptions: Record how Reach/Impact were estimated
3. Regular Recalibration: Update scores as new data arrives
4. Avoid False Precision: RICE is for relative ranking, not absolute truth
5. Consider Dependencies: Factor in feature dependencies when ranking
6. Involve Stakeholders: Get input from multiple perspectives on scores

Common Pitfalls

1. Inflated Confidence: Default to 0.5 if unsure
2. Optimistic Effort: Always include QA, documentation, launch time
3. Double-Counting Reach: Avoid counting same users multiple times
4. Gaming Scores: Facilitate honest scoring discussions

References

• RICE Prioritizer Claude Skill
• Sprint Prioritizer Plugin
• OKR Cascade Generator
• Linear MCP Server