{"id":"00061937-ccbc-437e-8171-eb15363b7976","shortId":"NLVLwX","kind":"skill","title":"prioritization-matrix","tagline":"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.","description":"# Prioritization Matrix Skill\n\nScore, rank, and prioritize a set of features, initiatives, or ideas using structured scoring frameworks.\n\n## When to Use\n- User has a list of features and needs to decide what to build first\n- User needs to justify prioritization to stakeholders\n- User says `/prioritization-matrix` followed by a list of items\n- Any time competing initiatives need to be ranked\n\n## Supported Frameworks\n\n### RICE (Reach, Impact, Confidence, Effort)\nBest for: Growth-focused teams with measurable reach data.\n\n| Factor | How to Score |\n|--------|-------------|\n| **Reach** | # of users/customers affected per quarter |\n| **Impact** | 0.25 (minimal) / 0.5 (low) / 1 (medium) / 2 (high) / 3 (massive) |\n| **Confidence** | 100% (high) / 80% (medium) / 50% (low) |\n| **Effort** | Person-months of work |\n\n**Score = (Reach x Impact x Confidence) / Effort**\n\n### ICE (Impact, Confidence, Ease)\nBest for: Fast estimation when detailed reach data isn't available.\n\n| Factor | How to Score (1-10) |\n|--------|-------------------|\n| **Impact** | How much will this move the needle? |\n| **Confidence** | How sure are we about impact and effort? |\n| **Ease** | How easy is this to implement? (10 = trivial) |\n\n**Score = Impact x Confidence x Ease**\n\n### Weighted Scoring\nBest for: Custom criteria that matter to your team.\n\nDefine 4-6 criteria with weights (must sum to 100%):\n\n| Criteria | Weight | Description |\n|----------|--------|-------------|\n| Strategic alignment | 25% | How well does this support company goals? |\n| User impact | 25% | How much does this improve user experience? |\n| Revenue potential | 20% | Direct or indirect revenue impact |\n| Technical feasibility | 15% | How complex is implementation? |\n| Time sensitivity | 15% | Is there a window of opportunity? |\n\nScore each item 1-5 on each criterion. **Weighted score = sum of (score x weight).**\n\n### Value vs. Effort (2x2)\nBest for: Quick visual communication to stakeholders.\n\nPlot items on a 2x2 matrix:\n- **Quick Wins** (High value, Low effort) → Do first\n- **Big Bets** (High value, High effort) → Plan carefully\n- **Fill-ins** (Low value, Low effort) → Do if capacity allows\n- **Money Pits** (Low value, High effort) → Deprioritize\n\n## Workflow\n\n### Step 1: Clarify\n- What are we prioritizing? (features, bugs, initiatives, experiments)\n- What timeframe? (this sprint, this quarter, this year)\n- What constraints? (team size, dependencies, deadlines)\n- What's the primary goal? (growth, retention, revenue, quality)\n\n### Step 2: Choose Framework\nBased 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.\n\n### Step 3: Score\nFor each item:\n- Score on each dimension with reasoning (not just numbers)\n- Flag assumptions and confidence level\n- Note dependencies between items\n\n### Step 4: Rank & Recommend\n- Sort by composite score\n- Group into tiers: Must Do / Should Do / Could Do / Won't Do\n- Highlight any items where the score conflicts with your intuition (and explain why)\n\n### Step 5: Communicate\nGenerate a stakeholder-ready summary:\n- Top 3 priorities with one-sentence rationale each\n- What we're NOT doing and why\n- Key assumptions that could change the ranking\n\n## Output Format\nGenerate 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.\n\n## AI/ML-Specific Considerations\nWhen prioritizing AI features, also consider:\n- **Model readiness**: Is the underlying model capable enough?\n- **Eval coverage**: Do we have evals to measure success?\n- **Safety review**: Does this need safety/red-team review?\n- **Data requirements**: Do we have the training/eval data?\n- **Cost per query**: What's the inference cost 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