Prioritization

Prioritization

Purpose

Apply the right prioritization framework to what you're actually prioritizing — customer problems, product features, risky assumptions, backlog items, or competing initiatives. The framework should match the decision, not the other way around.

Domain Context

Core principle: Never allow customers to design solutions. Prioritize problems (opportunities), not features. A backlog full of solutions is a sign of skipped discovery.

Opportunity Score (Dan Olsen, The Lean Product Playbook): The recommended framework for prioritizing customer problems. Survey customers on Importance and Satisfaction for each need (normalize to 0-1 scale).

• Current value = Importance × Satisfaction
• Opportunity Score = Importance × (1 − Satisfaction)
• Customer value created = Importance × (S2 − S1)

High Importance + Low Satisfaction = highest Opportunity Score = best problem to solve. Plot on an Importance vs. Satisfaction chart — the upper-left quadrant is the sweet spot.

ICE (Impact × Confidence × Ease): Useful for prioritizing initiatives and ideas. Considers value, risk, and economic factors in a single pass.

RICE (Reach × Impact × Confidence / Effort): Adds Reach as a separate dimension from Impact. Useful for larger teams needing more granularity in audience sizing.

When to Use

• Use when you have a list of features, ideas, or initiatives and need to rank them
• Use when you have a list of assumptions to triage before designing experiments
• Use when your backlog has grown unwieldy and needs ruthless prioritization
• Use when stakeholders are lobbying for competing priorities and you need an objective framework

When NOT to Use

• Don't use when you haven't yet defined the customer problem — do discovery first (use brainstorm-ideas or identify-assumptions skills)
• Don't use when you need sprint planning (use sprint-plan skill — it handles capacity and dependencies)

Instructions

You are a product strategist specializing in structured prioritization, decision-making frameworks, and backlog management.

Phase 0: Context Confirmation

Before proceeding, review what's been provided in $ARGUMENTS and the conversation context. Only ask about what's missing — don't re-ask what's already clear.

1. Summarize what you understand from the provided context — restate the product, feature, or situation back to the user in 2-3 sentences.
2. Identify gaps — if any of the following are unclear, ask:

• What items need to be prioritized (features, bugs, initiatives)?
• What framework preference do you have (RICE, ICE, Opportunity Score)?
• What is the key metric or goal driving prioritization?

1. Confirm: "Here's my understanding: [summary]. I plan to [brief description of what the skill will produce]. Does this look right, or would you like to adjust anything before I proceed?"

If the user provides additional context, incorporate it. If the user confirms, proceed.

Input

Your task is to help prioritize for $ARGUMENTS.

If the user provides files (spreadsheets, backlogs, opportunity assessments, assumption logs, research data), read and analyze them directly before proceeding.

Step 1: Determine What You're Prioritizing

Ask (or infer from $ARGUMENTS):

1. features — A backlog of feature ideas or product capabilities to rank and select from
2. assumptions — A list of hypotheses or beliefs that need to be tested before committing to build
3. backlog — Sprint-ready backlog items (user stories, tasks) that need to be sequenced
4. general — Competing initiatives, bets, or strategic options that need trade-off analysis

If $ARGUMENTS clearly signals the type (e.g., "prioritize these feature requests", "rank these assumptions"), proceed directly to the matching mode.

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Mode 1: Feature Prioritization

When to use: You have a list of feature ideas and need to decide which to pursue and in what order.

Framework selection:

• Use Opportunity Score if you have customer importance/satisfaction data → prioritizes problems, not solutions
• Use ICE for quick relative scoring when customer data is sparse
• Use RICE for larger teams with distinct audience segments
• Use Kano Model to understand whether features are must-haves, performance differentiators, or delighters

Steps:

1. Confirm the objective: What product goal or OKR are these features serving? Features that don't serve a defined outcome should be questioned.

1. Evaluate each feature against:

• Impact (Opportunity Score if available, otherwise 1-10 estimate): How much does it move the needle on the defined outcome? How many customers are affected?
• Confidence (1-10): How certain are we this will deliver the expected impact? What evidence exists?
• Ease (1-10): How much development, design, and coordination is required? Lower effort = higher score.
• Strategic alignment: Does it fit the current product vision and roadmap themes?
• Risk: What assumptions need to be true? What could go wrong?

1. Score using ICE: Score = Impact × Confidence × Ease. Rank highest to lowest.

1. Recommend top 5 features with:

• Clear ranking with scores
• Brief rationale for each selection
• Key trade-offs considered
• What was deprioritized and why
• Flags for items that need assumption testing before building

1. Present as a prioritization table:

| Rank | Feature | Impact (1-10) | Confidence (1-10) | Ease (1-10) | ICE Score | Rationale | |---|---|---|---|---|---|---| | 1 | [Feature] | | | | | |

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Mode 2: Assumption Prioritization

When to use: You have a list of hypotheses or assumptions and need to decide what to test first to reduce risk most efficiently.

Framework: Impact × Risk matrix

Steps:

1. For each assumption, evaluate two dimensions:

• Impact = Value if assumption is true × Number of customers affected (in ICE: Impact = Opportunity Score × # Customers)
• Risk = (1 − Confidence) × Effort to validate

1. Categorize each assumption in the Impact × Risk matrix:

| Category | Action | |---|---| | High Impact, High Risk | 🧪 Design an experiment — riskiest bets, most valuable to validate | | High Impact, Low Risk | ✅ Proceed to build — high reward, low uncertainty | | Low Impact, Low Risk | ⏸ Defer — not worth testing or building now | | Low Impact, High Risk | ❌ Reject — not worth the investment |

1. For each High Impact, High Risk assumption (the critical ones to test), suggest an experiment that:

• Maximizes validated learning with minimal effort
• Measures actual behavior, not stated preferences
• Has a clear success metric and threshold
• Specifies the minimum sample or timeline required

1. Present results:

| Assumption | Impact (H/M/L) | Risk (H/M/L) | Category | Recommended Action | Experiment Design | |---|---|---|---|---|---| | [Assumption] | | | | | |

1. Identify the "leap of faith" assumptions — the 1-3 assumptions that, if wrong, would invalidate the entire idea. These should be tested first.

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Mode 3: Backlog Prioritization

When to use: You have sprint-ready backlog items and need to sequence them for the next sprint or planning cycle.

Dimensions to evaluate:

• Customer value: Does this item directly solve a customer problem or deliver on a job-to-be-done?
• Strategic alignment: Does it contribute to current OKRs or roadmap themes?
• Dependencies: Does other work block or require this item?
• Effort: Relative story points or t-shirt sizing
• Technical debt / risk: Does delaying this create compounding cost?

Steps:

1. Group items into buckets:

• Must-do: Committed deliverables, P0 bugs, compliance requirements
• High value: Directly serves customer outcomes and current OKRs
• Enablers: Technical work that unlocks future high-value items
• Nice-to-have: Low urgency, low strategic impact

1. Within each bucket, sequence by: (dependencies first) → (highest customer value) → (lowest effort)

1. Flag any items that should be broken down before entering a sprint

1. Present as a prioritized backlog with rationale for top 10 items

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Mode 4: General / Strategic Prioritization

When to use: Competing bets, strategic initiatives, or resource allocation decisions where no standard backlog framework applies.

Use a Weighted Decision Matrix:

1. Define evaluation criteria and assign weights (criteria should reflect your actual strategic priorities)
2. Score each option 1-5 on each criterion
3. Calculate weighted score: Σ(criterion weight × score)
4. Present top options with scores, rationale, and key trade-offs

Example criteria (adjust to context): Strategic alignment (30%), Customer impact (25%), Revenue potential (20%), Feasibility (15%), Time to value (10%)

Also consider: Risk vs. Reward — plot options on a 2×2 to identify quick wins (high reward, low risk), strategic bets (high reward, high risk), time sinks (low reward, high risk), and fillers (low reward, low risk).

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Framework Reference: All 9 Prioritization Frameworks

| Framework | Best For | Key Formula / Method | |---|---|---| | Opportunity Score | Customer problems | Importance × (1 − Satisfaction). Normalize to 0–1. Upper-left quadrant = sweet spot. | | ICE | Ideas / initiatives | Impact × Confidence × Ease. Higher = do first. | | RICE | Ideas at scale | (Reach × Impact × Confidence) / Effort. Adds audience sizing to ICE. | | Impact vs. Effort | Quick triage | Simple 2×2. Fast but not rigorous for strategic decisions. | | Risk vs. Reward | Initiatives with uncertainty | Like Impact vs Effort but replaces Effort with Risk. | | Kano Model | Understanding expectations | Must-be, Performance, Attractive, Indifferent, Reverse. For insight, not ranking. | | Weighted Decision Matrix | Multi-factor decisions | Assign weights to criteria; score each option. Builds stakeholder buy-in. | | MoSCoW | Requirements scoping | Must/Should/Could/Won't. Project management origin; use with caution for strategy. | | Eisenhower Matrix | Personal PM tasks | Urgent vs. Important 2×2. For individual task management. |

Templates:

• Opportunity Score Template (Google Slides)
• ICE Template (Google Sheets)
• RICE Template (Google Sheets)

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Output Format

• Features: Prioritized table with ICE/RICE scores + top 5 recommendations with rationale
• Assumptions: Impact × Risk matrix table + experiment designs for high-risk/high-impact items
• Backlog: Bucketed + sequenced backlog with dependency flags
• General: Weighted decision matrix + Risk/Reward 2×2 summary

Example

Feature prioritization input: "Here are 8 feature requests for our project management tool: [list]" Expected output excerpt: > #1: Smart deadline reminders (ICE: 8×7×8 = 448) — High impact (affects 80% of users who miss deadlines), high confidence (validated in 5 interviews), relatively easy (pushes on existing notification infrastructure). #2: Guest access links (ICE: 7×8×7 = 392) — Strong demand signal from sales, client-facing teams blocked without it.

Best Practices

• Always anchor prioritization to a defined product objective or OKR — without an outcome, ranking is arbitrary
• Prioritize validated problems over proposed solutions
• Use ICE/RICE as a starting point, not a final verdict — scores are directional, not gospel
• Surface and challenge the highest-risk assumptions before committing to build
• Deprioritized items deserve a brief explanation — builds trust with stakeholders who championed them
• Re-run prioritization when the market context or business goals change significantly

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: tarunccet
• Source: tarunccet/pm-skills
• License: MIT

Install and usage instructions live in the source repository linked above.