/prioritization-matrix

name: prioritization-matrix
description: "Score and rank features or initiatives using RICE, ICE, weighted scoring, or custom frameworks. Generates a prioritized backlog with clear rationale. Use for: sprint planning, roadmap decisions, feature trade-offs."
argument-hint: "[list of features or initiatives to prioritize]"

Prioritization Matrix Skill

Score, rank, and prioritize a set of features, initiatives, or ideas using structured scoring frameworks.

When to Use

• User has a list of features and needs to decide what to build first
• User needs to justify prioritization to stakeholders
• User says `/prioritization-matrix` followed by a list of items
• Any time competing initiatives need to be ranked

Supported Frameworks

RICE (Reach, Impact, Confidence, Effort)

Best for: Growth-focused teams with measurable reach data.

| Factor | How to Score | |--------|-------------| | **Reach** | # of users/customers affected per quarter | | **Impact** | 0.25 (minimal) / 0.5 (low) / 1 (medium) / 2 (high) / 3 (massive) | | **Confidence** | 100% (high) / 80% (medium) / 50% (low) | | **Effort** | Person-months of work |

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

ICE (Impact, Confidence, Ease)

Best for: Fast estimation when detailed reach data isn't available.

| Factor | How to Score (1-10) | |--------|-------------------| | **Impact** | How much will this move the needle? | | **Confidence** | How sure are we about impact and effort? | | **Ease** | How easy is this to implement? (10 = trivial) |

**Score = Impact x Confidence x Ease**

Weighted Scoring

Best for: Custom criteria that matter to your team.

Define 4-6 criteria with weights (must sum to 100%):

| Criteria | Weight | Description | |----------|--------|-------------| | Strategic alignment | 25% | How well does this support company goals? | | User impact | 25% | How much does this improve user experience? | | Revenue potential | 20% | Direct or indirect revenue impact | | Technical feasibility | 15% | How complex is implementation? | | Time sensitivity | 15% | Is there a window of opportunity? |

Score each item 1-5 on each criterion. **Weighted score = sum of (score x weight).**

Value vs. Effort (2x2)

Best for: Quick visual communication to stakeholders.

Plot items on a 2x2 matrix:

• **Quick Wins** (High value, Low effort) → Do first
• **Big Bets** (High value, High effort) → Plan carefully
• **Fill-ins** (Low value, Low effort) → Do if capacity allows
• **Money Pits** (Low value, High effort) → Deprioritize

Workflow

Step 1: Clarify

• What are we prioritizing? (features, bugs, initiatives, experiments)
• What timeframe? (this sprint, this quarter, this year)
• What constraints? (team size, dependencies, deadlines)
• What's the primary goal? (growth, retention, revenue, quality)

Step 2: Choose Framework

Based on the context, recommend the most appropriate framework. If the user doesn't specify, default to RICE for product features and Weighted Scoring for strategic initiatives.

Step 3: Score

For each item:

• Score on each dimension with reasoning (not just numbers)
• Flag assumptions and confidence level
• Note dependencies between items

Step 4: Rank & Recommend

• Sort by composite score
• Group into tiers: Must Do / Should Do / Could Do / Won't Do
• Highlight any items where the score conflicts with your intuition (and explain why)

Step 5: Communicate

Generate a stakeholder-ready summary:

• Top 3 priorities with one-sentence rationale each
• What we're NOT doing and why
• Key assumptions that could change the ranking

Output Format

Generate a clean markdown table with scores, plus a summary paragraph. Include the rationale for the top and bottom items. Flag any close calls where small changes in assumptions would flip the ranking.

AI/ML-Specific Considerations

When prioritizing AI features, also consider:

• **Model readiness**: Is the underlying model capable enough?
• **Eval coverage**: Do we have evals to measure success?
• **Safety review**: Does this need safety/red-team review?
• **Data requirements**: Do we have the training/eval data?
• **Cost per query**: What's the inference cost impact?