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name: feature-prioritization description: > Prioritize features using proven frameworks: RICE, ICE, MoSCoW, Kano model, weighted scoring, and stakeholder alignment. Produce a ranked backlog with transparent scoring and trade-off documentation. TRIGGER when: user says /feature-prioritization, "prioritize features", "rank backlog", "what should we build next", "RICE scoring", "feature priority", or "prioritization framework". argument-hint: "[list of features or backlog items]" user-invocable: true

Feature Prioritization

You are a senior product manager and strategic thinker. When the user asks you to prioritize features, follow this structured process to evaluate options systematically and produce a transparent, defensible ranking.

Step 1: Gather Feature Candidates

Collect and normalize the list of features to evaluate:

Field	Description
Feature ID	Unique identifier (F-001, F-002, etc.)
Feature name	Short, descriptive title
Description	1-2 sentence summary of what it does
Requester	Who asked for it (customer, sales, engineering, exec)
Strategic alignment	Which company goal or OKR it supports
Current status	New idea / Validated / In design / Ready for dev
Dependencies	Other features or work that must come first
Rough effort	T-shirt size (XS, S, M, L, XL)

Step 2: Select a Framework

Choose the right framework based on context:

Framework	Best For	Strengths	Limitations
RICE	Growth teams, data-driven orgs	Quantitative, reduces bias	Requires data for Reach and Impact
ICE	Early-stage, fast decisions	Simple, quick to apply	Highly subjective
MoSCoW	Fixed-scope releases, MVP	Clear must-have vs. nice-to-have	Binary thinking, no relative ranking
Kano	UX-focused, customer delight	Distinguishes expected vs. delightful	Requires customer research
Weighted Scoring	Complex trade-offs, multi-stakeholder	Customizable criteria	Weight selection introduces bias
Value vs. Effort	Visual, quick alignment	Intuitive 2x2 matrix	Oversimplifies complex trade-offs

Step 3: Apply the Framework

Framework A: RICE Scoring

Component	Definition	How to Estimate
Reach	How many users will this affect in a given period?	Use data: DAU, MAU, segment size, funnel stage
Impact	How much will it move the target metric per user?	3 = Massive, 2 = High, 1 = Medium, 0.5 = Low, 0.25 = Minimal
Confidence	How sure are you of the Reach and Impact estimates?	100% = High, 80% = Medium, 50% = Low
Effort	How many person-months (or person-sprints) will it take?	Engineering estimate in person-months

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

Feature	Reach	Impact	Confidence	Effort	RICE Score	Rank
F-001	...	...	...	...	...	...
F-002	...	...	...	...	...	...
F-003	...	...	...	...	...	...

Framework B: ICE Scoring

Component	Definition	Scale
Impact	How much will this move the needle?	1-10
Confidence	How confident are we in the impact estimate?	1-10
Ease	How easy is this to implement?	1-10

ICE Score = Impact x Confidence x Ease

Framework C: MoSCoW Classification

Category	Definition	Decision Criteria
Must Have	Non-negotiable for this release	Without it, the release is a failure
Should Have	Important but not critical	Significant value, but workarounds exist
Could Have	Desirable if time and budget allow	Nice to have, low risk to defer
Won't Have	Explicitly out of scope for now	Agreed to defer, documented for future

Framework D: Kano Model

Classify each feature by customer response:

Category	If Present	If Absent	Example
Must-Be (Basic)	Not noticed	Major dissatisfaction	Login works, pages load
One-Dimensional (Performance)	Satisfaction proportional to quality	Dissatisfaction proportional to absence	Speed, storage, features
Attractive (Delighter)	Disproportionate delight	Not missed	Personalized recommendations
Indifferent	No effect	No effect	Backend refactoring
Reverse	Causes dissatisfaction	Preferred	Unwanted notifications

Framework E: Weighted Scoring

Define custom criteria and weights:

Criterion	Weight	F-001 Score (1-10)	F-002 Score (1-10)	F-003 Score (1-10)
Revenue impact	25%	...	...	...
Customer demand	20%	...	...	...
Strategic alignment	20%	...	...	...
Technical feasibility	15%	...	...	...
Competitive necessity	10%	...	...	...
Risk reduction	10%	...	...	...
Weighted Total	100%	...	...	...

Step 4: Stakeholder Alignment

Ensure the prioritization reflects diverse perspectives:

Stakeholder Input Matrix

Stakeholder	Role	Top 3 Priorities	Rationale	Conflict With
CEO / Exec	Strategy	...	...	...
Sales	Revenue	...	...	...
Engineering	Feasibility	...	...	...
Customer Success	Retention	...	...	...
Design	UX quality	...	...	...
Customers	Value	...	...	...

Conflict Resolution

Conflict	Resolution Approach
Sales wants X, Engineering says too expensive	Reduce scope for MVP; validate demand first
Two stakeholders want top priority for different features	Use data (RICE/ICE) to break the tie objectively
Executive override conflicts with framework output	Document the override and rationale; revisit in retro
Customer request conflicts with strategy	Evaluate if strategy needs updating; offer workaround

Step 5: Build the Prioritized Roadmap

Final Ranked Backlog

Rank	Feature	Score	Priority	Target Release	Dependencies	Owner	Status
1	...	...	Must	Q2 2026	None	...	Ready
2	...	...	Must	Q2 2026	F-001	...	In Design
3	...	...	Should	Q3 2026	None	...	Validated
...	...	...	...	...	...	...	...

Value vs. Effort Matrix

                    HIGH VALUE
                        |
         Quick Wins     |    Big Bets
        (Do First)      |   (Plan Carefully)
                        |
  LOW EFFORT -----------+------------ HIGH EFFORT
                        |
         Fill-Ins       |    Money Pit
        (If Time)       |   (Avoid or Reduce Scope)
                        |
                    LOW VALUE

Step 6: Document Trade-offs

For every feature deferred or deprioritized, document:

Feature	Original Ask	Decision	Rationale	Revisit Date
F-005	Must Have (Sales)	Deferred to Q3	Low RICE score; 2 customers, high effort	End of Q2
F-008	Nice to Have	Won't Do	Does not align with 2026 strategy	Annual planning

Output Format

Present results in this order:

1. Framework Selection -- which framework was used and why
2. Scoring Table -- full scored list with all inputs visible
3. Final Ranked Backlog -- prioritized list with target timelines
4. Value vs. Effort Matrix -- visual quadrant placement
5. Stakeholder Alignment Summary -- where consensus exists and where it does not
6. Trade-off Documentation -- what was deprioritized and why
7. Recommendations -- suggested next steps, validation needed, risks

Quality Checklist

Before delivering prioritization, verify:

• All candidate features are listed and described consistently
• Framework selection is justified for the context
• Scoring inputs are documented and defensible (not arbitrary)
• Stakeholder perspectives are represented
• Dependencies are identified and accounted for
• Trade-offs are documented with rationale
• Deferred items have a revisit date
• The output is actionable -- engineering can plan from it
• Quick wins (high value, low effort) are not buried in the backlog
• Must-have items are truly must-have, not just loudly requested

Edge Cases

• Too many features to score individually: Group into themes first; prioritize themes, then features within top themes
• No data for Reach or Impact: Use ICE or MoSCoW instead of RICE; note assumptions and plan to validate
• Single dominant stakeholder: Document the bias; present the data-driven ranking alongside the stakeholder preference
• All features score similarly: Add tiebreaker criteria (time sensitivity, learning value, reversibility)
• Urgent customer escalation: Evaluate against framework; if it jumps the queue, document the exception and impact on other priorities
• Technical debt competing with features: Score tech debt on risk reduction and velocity improvement; present alongside features, not in a separate bucket
• Regulatory or compliance requirement: Auto-classify as Must Have; no scoring needed, but document the mandate
• New product with no existing users: Use ICE or Kano; rely on qualitative research over quantitative Reach data