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Prioritization

Skill viktorbezdek/skillstack/prioritization/skills/prioritization

Apply RICE, MoSCoW, ICE, and effort-impact frameworks to rank options and decide what to work on next. Use when the user asks to prioritize a backlog, rank features, decide what to build first, apply RICE or MoSCoW scoring, cut scope, or compare items by effort vs impact. NOT for defining success metrics or OKRs (use outcome-orientation). NOT for making a strategic bet under uncertainty (use strategic-decision workflow).From its SKILL.md

Install
npx -y skills add viktorbezdek/skillstack --skill prioritization

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SKILL.md

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Prioritization

Make better decisions about what to do first.

When to use this skill

  • Scoring features or initiatives to decide quarterly priorities
  • Categorizing requirements as Must/Should/Could/Won't for a release
  • Creating effort-impact matrices for sprint planning
  • Defending prioritization decisions to stakeholders with data
  • Auditing existing prioritization for bias (HiPPO, recency, squeaky wheel)
  • Choosing between RICE, ICE, MoSCoW, or effort-impact for a specific situation

When NOT to use this skill

  • Defining OKRs or success metrics → use outcome-orientation
  • Assessing project risks → use risk-management
  • Making strategic bets under deep uncertainty → use strategic-decision workflow
  • Stakeholder analysis for whose input to weight → use persona-mapping

Framework selection decision tree

Do you have reach data (users affected per quarter)?
  YES → Do you need quantitative rigor for stakeholder defense?
    YES → RICE (most defensible, separates reach/impact/confidence)
    NO  → Effort-Impact matrix (quick visual sort)
  NO  → Do you need release scoping with capacity constraints?
    YES → MoSCoW (60% rule prevents scope creep)
    NO  → ICE (lightweight, works with subjective scores only)

Using more than one? They complement each other:
  RICE or ICE for quarterly prioritization
  MoSCoW for release scoping
  Effort-Impact for sprint-level quick wins

RICE Scoring

Reach x Impact x Confidence / Effort

FactorDescriptionScale
ReachUsers affected per quarterNumber
ImpactEffect on goal0.25-3
ConfidenceCertainty level50-100%
EffortPerson-monthsNumber
Score = (R x I x C) / E

Impact Scale

  • 3 = Massive
  • 2 = High
  • 1 = Medium
  • 0.5 = Low
  • 0.25 = Minimal

MoSCoW Method

PriorityMeaningGuideline
MustRequired for successNon-negotiable
ShouldImportant but not vitalInclude if possible
CouldNice to haveIf time permits
Won'tNot this timeExplicitly excluded

Rule: Must = 60% max of effort

ICE Scoring

FactorDescriptionScale
ImpactPotential value1-10
ConfidenceCertainty1-10
EaseSimplicity1-10
Score = (I + C + E) / 3

Effort-Impact Matrix

HIGH IMPACT
     |
 BIG |  QUICK
BETS |  WINS
     |
-----+------ LOW EFFORT
     |
MONEY|  FILL
PITS |  INS
     |
LOW IMPACT

Priority order: Quick Wins -> Big Bets -> Fill Ins -> Avoid Money Pits

Prioritization Template

## Item: [Name]

### RICE Score
| Factor | Value | Notes |
|--------|-------|-------|
| Reach | [num] | [who] |
| Impact | [0.25-3] | [why] |
| Confidence | [%] | [evidence] |
| Effort | [PM] | [breakdown] |
| **Score** | [calc] | |

### Decision
Priority: [High/Med/Low]
Rationale: [reasoning]

Anti-Patterns

Common prioritization failures

  1. HiPPO (Highest Paid Person's Opinion) — the most senior person's preference wins regardless of impact data. Counter: present RICE comparison data; if overridden, document the override reason explicitly.
  2. Recency bias — the latest request displaces long-planned high-impact work. Counter: score every request before adding to backlog; compare new request score against current top items.
  3. Squeaky wheel — the loudest customer gets prioritized, not the most impacted segment. Counter: weight by reach data, not volume of complaints.
  4. Sunk cost fallacy — continuing a project because of past investment. Counter: re-score with only remaining effort and remaining impact; past investment is irrelevant to forward-looking decisions.
  5. Confidence inflation — scoring 90% confidence on everything, eliminating it as a differentiator. Counter: calibrate — 90% = strong evidence (A/B test, customer data), 70% = reasonable belief, 50% = intuition only.
  6. All Must-haves — MoSCoW with 15 "Must" items exceeding capacity. Counter: enforce the 60% rule; force-rank within Must and demote the lowest.
  7. RICE without data — guessing all values produces opinions with math, not defensible scores. Counter: switch to ICE (designed for subjective scoring) or invest in analytics before RICE.
  8. Strategic override without documentation — overriding RICE for a key account is sometimes correct, but undocumented overrides erode trust in the framework. Always write down why.

What ships with it: 2 files

3.2 KB alongside SKILL.md

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