prioritization-effort-impact

$npx mdskill add lyndonkl/claude/prioritization-effort-impact

Rank backlog items by effort and impact for clear decisions.

  • Transforms overwhelming backlogs into actionable priorities.
  • Uses 2x2 matrix scoring to categorize features and bugs.
  • Identifies quick wins, big bets, time sinks, and fill-ins.
  • Delivers a prioritized roadmap with validated decisions.

SKILL.md

.github/skills/prioritization-effort-impactView on GitHub ↗
---
name: prioritization-effort-impact
description: Transforms overwhelming backlogs into clear, actionable priorities by mapping items on a 2x2 effort-vs-impact matrix, identifying quick wins (high impact, low effort), big bets, time sinks, and fill-ins. Use when ranking backlogs, deciding what to do first, prioritizing feature roadmaps, triaging bugs or technical debt, allocating resources across initiatives, identifying low-hanging fruit, evaluating strategic options, or when user mentions prioritization, quick wins, effort-impact matrix, high-impact low-effort, big bets, or "what should we do first?".
---
# Prioritization: Effort-Impact Matrix

## Workflow

Copy this checklist and track your progress:

```
Prioritization Progress:
- [ ] Step 1: Gather items and clarify scoring
- [ ] Step 2: Score effort and impact
- [ ] Step 3: Plot matrix and identify quadrants
- [ ] Step 4: Create prioritized roadmap
- [ ] Step 5: Validate and communicate decisions
```

**Step 1: Gather items and clarify scoring**

Collect all items to prioritize (features, bugs, initiatives, etc.) and define scoring scales for effort and impact. See [Scoring Frameworks](#scoring-frameworks) for effort and impact definitions. Use [resources/template.md](resources/template.md) for structure.

**Step 2: Score effort and impact**

Rate each item on effort (1-5: trivial to massive) and impact (1-5: negligible to transformative). Involve subject matter experts for accuracy. See [resources/methodology.md](resources/methodology.md) for advanced scoring techniques like Fibonacci, T-shirt sizes, or RICE.

**Step 3: Plot matrix and identify quadrants**

Place items on 2x2 matrix and categorize into Quick Wins (high impact, low effort), Big Bets (high impact, high effort), Fill-Ins (low impact, low effort), and Time Sinks (low impact, high effort). See [Common Patterns](#common-patterns) for typical quadrant distributions.

**Step 4: Create prioritized roadmap**

Sequence items: Quick Wins first, Big Bets second (after quick wins build momentum), Fill-Ins during downtime, avoid Time Sinks unless required. See [resources/template.md](resources/template.md) for roadmap structure.

**Step 5: Validate and communicate decisions**

Self-check using [resources/evaluators/rubric_prioritization_effort_impact.json](resources/evaluators/rubric_prioritization_effort_impact.json). Ensure scoring is defensible, stakeholder perspectives included, and decisions clearly explained with rationale.

## Common Patterns

**By domain:**

- **Product backlogs**: Quick wins = small UX improvements, Big bets = new workflows, Time sinks = edge case perfection
- **Technical debt**: Quick wins = config fixes, Big bets = architecture overhauls, Time sinks = premature optimizations
- **Bug triage**: Quick wins = high-impact easy fixes, Big bets = complex critical bugs, Time sinks = cosmetic issues
- **Strategic initiatives**: Quick wins = process tweaks, Big bets = market expansion, Time sinks = vanity metrics
- **Marketing campaigns**: Quick wins = email nurture, Big bets = brand overhaul, Time sinks = minor A/B tests

**By stakeholder priority:**

- **Execs want**: Quick wins (visible progress) + Big bets (strategic impact)
- **Engineering wants**: Technical debt quick wins + Big bets (platform work)
- **Sales wants**: Quick wins that unblock deals + Big bets (major features)
- **Customers want**: Quick wins (pain relief) + Big bets (transformative value)

**Typical quadrant distribution:**
- Quick Wins: 10-20% (rare, high-value opportunities)
- Big Bets: 20-30% (strategic, resource-intensive)
- Fill-Ins: 40-50% (most backlogs have many low-value items)
- Time Sinks: 10-20% (surprisingly common, often disguised as "polish")

**Red flags:**
- ❌ **No quick wins**: Likely overestimating effort or underestimating impact
- ❌ **All quick wins**: Scores probably not calibrated correctly
- ❌ **Many time sinks**: Cut scope or reject these items
- ❌ **Effort/impact scores all 3**: Need more differentiation (use 1-2 and 4-5)

## Scoring Frameworks

**Effort dimensions (choose relevant ones):**
- **Time**: Engineering/execution hours (1=hours, 2=days, 3=weeks, 4=months, 5=quarters)
- **Complexity**: Technical difficulty (1=trivial, 5=novel/unprecedented)
- **Risk**: Failure probability (1=safe, 5=high-risk)
- **Dependencies**: External blockers (1=none, 5=many teams/approvals)
- **Cost**: Financial investment (1=$0-1K, 2=$1-10K, 3=$10-100K, 4=$100K-1M, 5=$1M+)

**Impact dimensions (choose relevant ones):**
- **Users affected**: Reach (1=<1%, 2=1-10%, 3=10-50%, 4=50-90%, 5=>90%)
- **Business value**: Revenue/savings (1=$0-10K, 2=$10-100K, 3=$100K-1M, 4=$1-10M, 5=$10M+)
- **Strategic alignment**: OKR contribution (1=tangential, 5=critical to strategy)
- **User pain**: Problem severity (1=nice-to-have, 5=blocker/crisis)
- **Risk reduction**: Mitigation value (1=minor, 5=existential risk)

**Composite scoring:**
- **Simple**: Average of dimensions (Effort = avg(time, complexity), Impact = avg(users, value))
- **Weighted**: Multiply by importance (Effort = 0.6×time + 0.4×complexity)
- **Fibonacci**: Use 1, 2, 3, 5, 8 instead of 1-5 for exponential differences
- **T-shirt sizes**: S/M/L/XL mapped to 1/2/3/5

**Example scoring (feature: "Add dark mode"):**
- Effort: Time=3 (2 weeks), Complexity=2 (CSS), Risk=2 (minor bugs), Dependencies=1 (no blockers) → **Avg = 2.0 (Low)**
- Impact: Users=4 (80% want it), Value=2 (retention, not revenue), Strategy=3 (design system goal), Pain=3 (eye strain) → **Avg = 3.0 (Medium-High)**
- **Result**: Medium-High Impact, Low Effort → **Quick Win!**

## Guardrails

**Ensure quality:**

1. **Include diverse perspectives**: Don't let one person score alone (eng overestimates effort, sales overestimates impact)
   - ✓ Get engineering, product, sales, customer success input
   - ❌ PM scores everything solo

2. **Differentiate scores**: If everything is scored 3, you haven't prioritized
   - ✓ Force rank or use wider scale (1-10)
   - ✓ Aim for distribution: few 1s/5s, more 2s/4s, many 3s
   - ❌ All items scored 2.5-3.5

3. **Question extreme scores**: High-impact low-effort items are rare (if you have 10, something's wrong)
   - ✓ "Why haven't we done this already?" test for quick wins
   - ❌ Wishful thinking (underestimating effort, overestimating impact)

4. **Make scoring transparent**: Document why each score was assigned
   - ✓ "Effort=4 because requires 3 teams, new infrastructure, 6-week timeline"
   - ❌ "Effort=4" with no rationale

5. **Revisit scores periodically**: Effort/impact change as context evolves
   - ✓ Re-score quarterly or after major changes (new tech, new team size)
   - ❌ Use 2-year-old scores

6. **Don't ignore dependencies**: Low-effort items blocked by high-effort prerequisites aren't quick wins
   - ✓ "Effort=2 for task, but depends on Effort=5 migration"
   - ❌ Score task in isolation

7. **Beware of "strategic" override**: Execs calling everything "high impact" defeats prioritization
   - ✓ "Strategic" is one dimension, not a veto
   - ❌ "CEO wants it" → auto-scored 5

## Quick Reference

**Resources:**
- **Quick start**: [resources/template.md](resources/template.md) - 2x2 matrix template and scoring table
- **Advanced techniques**: [resources/methodology.md](resources/methodology.md) - RICE, MoSCoW, Kano, weighted scoring
- **Quality check**: [resources/evaluators/rubric_prioritization_effort_impact.json](resources/evaluators/rubric_prioritization_effort_impact.json) - Evaluation criteria

**Success criteria:**
- ✓ Identified 1-3 quick wins to execute immediately
- ✓ Sequenced big bets into realistic roadmap (don't overcommit)
- ✓ Cut or deferred time sinks (low ROI items)
- ✓ Scoring rationale is transparent and defensible
- ✓ Stakeholders aligned on priorities
- ✓ Roadmap has capacity buffer (don't schedule 100% of time)

**Common mistakes:**
- ❌ Scoring in isolation (no stakeholder input)
- ❌ Ignoring effort (optimism bias: "everything is easy")
- ❌ Ignoring impact (building what's easy, not what's valuable)
- ❌ Analysis paralysis (perfect scores vs good-enough prioritization)
- ❌ Not saying "no" to time sinks
- ❌ Overloading roadmap (filling every week with big bets)
- ❌ Forgetting maintenance/support time (assuming 100% project capacity)

**When to use alternatives:**
- **Weighted scoring (RICE)**: When you need more nuance than 2x2 (Reach × Impact × Confidence / Effort)
- **MoSCoW**: When prioritizing for fixed scope/deadline (Must/Should/Could/Won't)
- **Kano model**: When evaluating customer satisfaction (basic/performance/delight features)
- **ICE score**: Simpler than RICE (Impact × Confidence × Ease)
- **Value vs complexity**: Same as effort-impact, different labels
- **Cost of delay**: When timing matters (revenue lost by delaying)

More from lyndonkl/claude

SkillDescription
abstraction-concrete-examplesBuilds structured abstraction ladders that translate high-level principles into concrete, actionable examples across 3-5 levels. Bridges communication gaps, reveals hidden assumptions, and tests whether abstract ideas work in practice. Use when explaining concepts at different expertise levels, moving between abstract principles and concrete implementation, identifying edge cases by testing ideas against scenarios, designing layered documentation, decomposing complex problems into actionable steps, or bridging strategy-execution gaps.
academic-letter-architectGuides the creation of evidence-based academic recommendation letters, reference letters, and award nominations that combine concrete examples, meaningful comparisons, and genuine enthusiasm. Use when writing recommendation letters for students, postdocs, or colleagues, or when user mentions recommendation letter, reference, nomination, letter of support, endorsement, or needs help with strong advocacy and comparative statements.
adr-architectureDocuments significant architectural and technical decisions with full context, alternatives considered, trade-offs analyzed, and consequences understood. Creates a decision trail that helps teams understand why decisions were made. Use when choosing between technology options, making infrastructure decisions, establishing standards, migrating systems, or when user mentions ADR, architecture decision, technical decision record, or decision documentation.
adverse-selection-priorProduces a Bayesian prior probability that an offered transaction is +EV for the recipient, given that the counterparty chose to propose it. Applies Akerlof market-for-lemons logic -- if they offered it, they believe it is +EV for them, so the prior that it is +EV for us is materially below 50%. Reusable across trade evaluation, waiver drops (another team dropping a player is also adverse selection), job-offer analysis, M&A, and any "someone offered me this" situation. Use when you receive an unsolicited trade/offer/proposal, analyzing incoming trade prior, evaluating why a counterparty proposed a deal, or when user mentions adverse selection, market for lemons, why did they offer this, incoming trade prior, they proposed it, Bayesian adjustment on received offer.
alignment-values-north-starCreates actionable alignment frameworks that give teams a shared North Star (direction), values (guardrails), and decision tenets (behavioral standards). Enables autonomous decision-making while maintaining organizational coherence. Use when starting new teams, scaling organizations, defining culture, establishing product vision, resolving misalignment, creating strategic clarity, or when user mentions North Star, team values, mission, principles, guardrails, decision framework, or cultural alignment.
analogy-weight-checkFor every analogy in a substacker draft, verifies it carries mechanical weight — the analogy does real work explaining the mechanism, not merely decorates it. Cross-references analogy-catalog.md for novelty (is this analogy reused from a prior post?) and domain fit (biology > organizational > sports preferred; physics/military disfavored). Use whenever an analogy appears in the draft. Trigger keywords: analogy weight, decorative, mechanical weight, reused analogy, catalog check, metaphor check.
answer-uncomfortable-questionTakes one strategic question about substacker ("should we launch paid?", "is this section dead?", "are we writing for the wrong audience?") and produces the mandatory evidence + reasoning + downside triad plus a recommendation. Used 3 times per Growth Strategist review. Trigger keywords: uncomfortable question, strategic question, evidence reasoning downside, triad.
attribute-performanceFor each substacker post that materially over- or under-performs the rolling baseline (|z| ≥ 1.0), produces a plain-English attribution paragraph with calibrated confidence (high / medium / low / unexplained). Considers subject-line effect, topic zeitgeist, external share, day-of-week, length effect, and audience-notes signals. Labels unexplained outliers explicitly rather than fabricating a story. Use after compute-baseline when outlier posts exist. Trigger keywords: attribution, why did this post work, outlier explanation, performance analysis.
auction-first-price-shadingComputes the optimal shaded bid for a first-price sealed-bid auction given a true private value, an estimate of the number of competing bidders N, and a value-distribution assumption. Implements the `(N-1)/N` equilibrium shading rule for uniform private values, adjusts for log-normal or empirical value distributions, layers a risk-aversion adjustment, and caps output against the bidder's remaining budget. Domain-neutral auction theory reusable across fantasy sports (baseball FAAB, NBA/NHL waiver auctions), prediction-market limit sizing, sealed procurement bids, and any blind-bid context. Use when user mentions "first-price auction bid", "sealed bid shading", "(N-1)/N", "FAAB bid amount", "auction shading", "optimal bid first-price", "bid for sealed-bid", "blind bid sizing", or when downstream logic needs a principled shade factor rather than an ad-hoc heuristic.
auction-winners-curse-haircutApplies a Bayesian haircut to a bid valuation for common-value auctions where winning is itself evidence the bidder over-estimated. Takes a raw valuation, a value-type classification (common_value / private_value / mixed), the number of informed bidders N, and a signal-dispersion estimate, and returns an adjusted valuation. Domain-neutral and reusable across fantasy FAAB, prediction markets, M&A bids, ad-auction budgets, and any generic bidding context. Use when user mentions "winner's curse", "common value auction", "valuation haircut", "adverse valuation", "Bayesian bid adjustment", or "over-paying in auction".