Prioritization Advisor Agent Skill

Purpose

Guide product managers in choosing the right prioritization framework by asking adaptive questions about product stage, team context, decision-making needs, and stakeholder dynamics. Use this to avoid "framework whiplash" (switching frameworks constantly) or applying the wrong framework (e.g., using RICE for strategic bets or ICE for data-driven decisions). Outputs a recommended framework with implementation guidance tailored to your context.

This is not a scoring calculator—it's a decision guide that matches prioritization frameworks to your specific situation.

Input

Works best with: What you're trying to prioritize and why now (sprint planning, roadmap, stakeholder fight).

Also useful: Product stage, team size, data availability, and frameworks you've tried that failed.

Anything supplied with the invocation itself — text after the skill name, a pasted context dump, or an appended ARGUMENTS: line — counts as answers already given. Use it and skip whatever it covers; don't re-ask.

Arriving empty-handed? That works too. The advisor opens by asking about your product stage and the decision you need the framework to make.

Example invocation: Help me pick a framework: seed-stage B2B startup, 40-item backlog, zero usage data, loud enterprise prospect.

Key Concepts

The Prioritization Framework Landscape

Common frameworks and when to use them:

Scoring frameworks:

• RICE (Reach, Impact, Confidence, Effort) — Data-driven, requires metrics
• ICE (Impact, Confidence, Ease) — Lightweight, gut-check scoring
• Value vs. Effort (2x2 matrix) — Quick wins vs. strategic bets
• Weighted Scoring — Custom criteria with stakeholder input

Strategic frameworks:

• Kano Model — Classify features by customer delight (basic, performance, delight)
• Opportunity Scoring — Rate importance vs. satisfaction gap
• Buy-a-Feature — Customer budget allocation exercise
• Moscow (Must, Should, Could, Won't) — Forcing function for hard choices

Contextual frameworks:

• Cost of Delay — Urgency-based (time-sensitive features)
• Impact Mapping — Goal-driven (tie features to outcomes)
• Story Mapping — User journey-based (narrative flow)

Why This Works

• Context-aware: Matches framework to product stage, team maturity, data availability
• Anti-dogmatic: No single "best" framework—it depends on your situation
• Actionable: Provides implementation steps, not just framework names

Anti-Patterns (What This Is NOT)

• Not a universal ranking: Frameworks aren't "better" or "worse"—they fit different contexts
• Not a replacement for strategy: Frameworks execute strategy; they don't create it
• Not set-it-and-forget-it: Reassess frameworks as your product matures

When to Use This

• Choosing a prioritization framework for the first time
• Switching frameworks (current one isn't working)
• Aligning stakeholders on prioritization process
• Onboarding new PMs to team practices

When NOT to Use This

• When you already have a working framework (don't fix what isn't broken)
• For one-off decisions (frameworks are for recurring prioritization)
• As a substitute for strategic vision (frameworks can't tell you what to build)

Facilitation Source of Truth

Use workshop-facilitation as the default interaction protocol for this skill.

It defines:

• session heads-up + entry mode (Guided, Context dump, Best guess)
• one-question turns with plain-language prompts
• progress labels (for example, Context Qx/8 and Scoring Qx/5)
• interruption handling and pause/resume behavior
• numbered recommendations at decision points
• quick-select numbered response options for regular questions (include Other (specify) when useful)

This file defines the domain-specific assessment content. If there is a conflict, follow this file's domain logic.

Application

This interactive skill asks up to 4 adaptive questions, offering 3-4 enumerated options at each step.

Question 1: Product Stage

Agent asks:

"What stage is your product in?"

Offer 4 enumerated options:

• Pre-product/market fit — "Searching for PMF; experimenting rapidly; unclear what customers want" (High uncertainty, need speed)
• Early PMF, scaling — "Found initial PMF; growing fast; adding features to retain/expand" (Moderate uncertainty, balancing speed + quality)
• Mature product, optimization — "Established market; incremental improvements; competing on quality/features" (Low uncertainty, data-driven decisions)
• Multiple products/platform — "Portfolio of products; cross-product dependencies; complex stakeholder needs" (Coordination complexity)

Or describe your product stage (new idea, growth mode, established, etc.).

User response: [Selection or custom]

Question 2: Team Context

Agent asks:

"What's your team and stakeholder environment like?"

Offer 4 enumerated options:

• Small team, limited resources — "3-5 engineers, 1 PM, need to focus ruthlessly" (Need simple, fast framework)
• Cross-functional team, aligned — "Product, design, engineering aligned; clear goals; good collaboration" (Can use data-driven frameworks)
• Multiple stakeholders, misaligned — "Execs, sales, customers all have opinions; need transparent process" (Need consensus-building framework)
• Large org, complex dependencies — "Multiple teams, shared roadmap, cross-team dependencies" (Need coordination framework)

Or describe your team/stakeholder context.

User response: [Selection or custom]

Question 3: Decision-Making Needs

Agent asks:

"What's the primary challenge you're trying to solve with prioritization?"

Offer 4 enumerated options:

• Too many ideas, unclear which to pursue — "Backlog is 100+ items; need to narrow to top 10" (Need filtering framework)
• Stakeholders disagree on priorities — "Sales wants features, execs want strategic bets, engineering wants tech debt" (Need alignment framework)
• Lack of data-driven decisions — "Prioritizing by gut feel; want metrics-based process" (Need scoring framework)
• Hard tradeoffs between strategic bets vs. quick wins — "Balancing long-term vision vs. short-term customer needs" (Need value/effort framework)

Or describe your specific challenge.

User response: [Selection or custom]

Question 4: Data Availability

Agent asks:

"How much data do you have to inform prioritization?"

Offer 3 enumerated options:

• Minimal data — "New product, no usage metrics, few customers to survey" (Gut-based frameworks)
• Some data — "Basic analytics, customer feedback, but no rigorous data collection" (Lightweight scoring frameworks)
• Rich data — "Usage metrics, A/B tests, customer surveys, clear success metrics" (Data-driven frameworks)

Or describe your data situation.

User response: [Selection or custom]

Output: Recommend Prioritization Framework

After collecting responses, the agent recommends a framework:

# Prioritization Framework Recommendation

**Based on your context:**
- **Product Stage:** [From Q1]
- **Team Context:** [From Q2]
- **Decision-Making Need:** [From Q3]
- **Data Availability:** [From Q4]

---

## Recommended Framework: [Framework Name]

**Why this framework fits:**
- [Rationale 1 based on Q1-Q4]
- [Rationale 2]
- [Rationale 3]

**When to use it:**
- [Context where this framework excels]

**When NOT to use it:**
- [Limitations or contexts where it fails]

---

## How to Implement

### Step 1: [First implementation step]
- [Detailed guidance]
- [Example: "Define scoring criteria: Reach, Impact, Confidence, Effort"]

### Step 2: [Second step]
- [Detailed guidance]
- [Example: "Score each feature on 1-10 scale"]

### Step 3: [Third step]
- [Detailed guidance]
- [Example: "Calculate RICE score: (Reach × Impact × Confidence) / Effort"]

### Step 4: [Fourth step]
- [Detailed guidance]
- [Example: "Rank by score; review top 10 with stakeholders"]

---

## Example Scoring Template

[Provide a concrete example of how to use the framework]

**Example (if RICE):**

| Feature | Reach (users/month) | Impact (1-3) | Confidence (%) | Effort (person-months) | RICE Score |
|---------|---------------------|--------------|----------------|------------------------|------------|
| Feature A | 10,000 | 3 (massive) | 80% | 2 | 12,000 |
| Feature B | 5,000 | 2 (high) | 70% | 1 | 7,000 |
| Feature C | 2,000 | 1 (medium) | 50% | 0.5 | 2,000 |

**Priority:** Feature A > Feature B > Feature C

---

## Alternative Framework (Second Choice)

**If the recommended framework doesn't fit, consider:** [Alternative framework name]

**Why this might work:**
- [Rationale]

**Tradeoffs:**
- [What you gain vs. what you lose]

---

## Common Pitfalls with This Framework

1. **[Pitfall 1]** — [Description and how to avoid]
2. **[Pitfall 2]** — [Description and how to avoid]
3. **[Pitfall 3]** — [Description and how to avoid]

---

## Reassess When

- Product stage changes (e.g., PMF → scaling)
- Team grows or reorganizes
- Stakeholder dynamics shift
- Current framework feels broken (e.g., too slow, ignoring important factors)

---

**Would you like implementation templates or examples for this framework?**

Examples

Example 1: Good Framework Match (Early PMF, RICE)

Q1 Response: "Early PMF, scaling — Found initial PMF; growing fast; adding features to retain/expand"

Q2 Response: "Cross-functional team, aligned — Product, design, engineering aligned; clear goals"

Q3 Response: "Lack of data-driven decisions — Prioritizing by gut feel; want metrics-based process"

Q4 Response: "Some data — Basic analytics, customer feedback, but no rigorous data collection"

Recommended Framework: RICE (Reach, Impact, Confidence, Effort)

Why this fits:

• You have some data (analytics, customer feedback) to estimate Reach and Impact
• Cross-functional team alignment means you can agree on scoring criteria
• Transitioning from gut feel to data-driven = RICE provides structure without overwhelming complexity
• Early PMF stage = need speed, but also need to prioritize high-impact features for retention/expansion

When to use it:

• Quarterly or monthly roadmap planning
• When backlog exceeds 20-30 items
• When stakeholders debate priorities

When NOT to use it:

• For strategic, multi-quarter bets (RICE favors incremental wins)
• When you lack basic metrics (Reach requires usage data)
• For single-feature decisions (overkill)

Implementation:

Step 1: Define Scoring Criteria

• Reach: How many users will this feature affect per month/quarter?
• Impact: How much will it improve their experience? (1 = minimal, 2 = high, 3 = massive)
• Confidence: How confident are you in your Reach/Impact estimates? (50% = low data, 80% = good data, 100% = certain)
• Effort: How many person-months to build? (Include design, engineering, QA)

Step 2: Score Each Feature

• Use a spreadsheet or Airtable
• Involve PM, design, engineering in scoring (not just PM solo)
• Be honest about Confidence (don't inflate scores)

Step 3: Calculate RICE Score

• Formula: (Reach × Impact × Confidence) / Effort
• Higher score = higher priority

Step 4: Review and Adjust

• Sort by RICE score
• Review top 10-20 with stakeholders
• Adjust for strategic priorities (RICE doesn't capture everything)

Example Scoring:

| Feature | Reach | Impact | Confidence | Effort | RICE Score |

|---------|-------|--------|------------|--------|------------|

| Email reminders | 5,000 | 2 | 70% | 1 | 7,000 |

| Mobile app | 10,000 | 3 | 60% | 6 | 3,000 |

| Dark mode | 8,000 | 1 | 90% | 0.5 | 14,400 |

Priority: Dark mode > Email reminders > Mobile app (despite mobile app having high Reach/Impact, Effort is too high)

Alternative Framework: ICE (Impact, Confidence, Ease)

Why this might work:

• Simpler than RICE (no Reach calculation)
• Faster to score (good if you need quick decisions)

Tradeoffs:

• Less data-driven (no Reach metric = can't compare features affecting different user bases)
• More subjective (Impact/Ease are gut-feel, not metrics)

Common Pitfalls:

• Overweighting Effort — Don't avoid hard problems just because they score low. Some strategic bets require high effort.
• Inflating Confidence — Be honest. 50% confidence is okay if data is scarce.
• Ignoring strategy — RICE doesn't capture strategic importance. Adjust for vision/goals.

Example 2: Bad Framework Match (Pre-PMF + RICE = Wrong Fit)

Q1 Response: "Pre-product/market fit — Searching for PMF; experimenting rapidly"

Q2 Response: "Small team, limited resources — 3 engineers, 1 PM"

Q3 Response: "Too many ideas, unclear which to pursue"

Q4 Response: "Minimal data — New product, no usage metrics"

Recommended Framework: ICE (Impact, Confidence, Ease) or Value/Effort Matrix

Why NOT RICE:

• You don't have usage data to estimate Reach
• Pre-PMF = you need speed, not rigorous scoring
• Small team = overhead of RICE scoring is too heavy

Why ICE instead:

• Lightweight, gut-check framework
• Can score 20 ideas in 30 minutes
• Good for rapid experimentation phase

Or Value/Effort Matrix:

• Visual 2x2 matrix (high value/low effort = quick wins)
• Even faster than ICE
• Good for stakeholder alignment (visual, intuitive)

Common Pitfalls

Pitfall 1: Using the Wrong Framework for Your Stage

Symptom: Pre-PMF startup using weighted scoring with 10 criteria

Consequence: Overhead kills speed. You need experiments, not rigorous scoring.

Fix: Match framework to stage. Pre-PMF = ICE or Value/Effort. Scaling = RICE. Mature = Opportunity Scoring or Kano.

Pitfall 2: Framework Whiplash

Symptom: Switching frameworks every quarter

Consequence: Team confusion, lost time, no consistency.

Fix: Stick with one framework for 6-12 months. Reassess only when stage/context changes.

Pitfall 3: Treating Scores as Gospel

Symptom: "Feature A scored 8,000, Feature B scored 7,999, so A wins"

Consequence: Ignores strategic context, judgment, and vision.

Fix: Use frameworks as input, not automation. PM judgment overrides scores when needed.

Pitfall 4: Solo PM Scoring

Symptom: PM scores features alone, presents to team

Consequence: Lack of buy-in, engineering/design don't trust scores.

Fix: Collaborative scoring sessions. PM, design, engineering score together.

Pitfall 5: No Framework at All

Symptom: "We prioritize by who shouts loudest"

Consequence: HiPPO (Highest Paid Person's Opinion) wins, not data or strategy.

Fix: Pick *any* framework. Even imperfect structure beats chaos.

References

Related Skills

• user-story.md — Prioritized features become user stories
• epic-hypothesis.md — Prioritized epics validated with experiments
• recommendation-canvas.md — Business outcomes inform prioritization

External Frameworks

• Intercom, *RICE Prioritization* (2016) — Origin of RICE framework
• Sean McBride, *ICE Scoring* (2012) — Lightweight prioritization
• Luke Hohmann, *Innovation Games* (2006) — Buy-a-Feature and other collaborative methods
• Noriaki Kano, *Kano Model* (1984) — Customer satisfaction framework

Dean's Work

• [If Dean has prioritization resources, link here]

Skill type: Interactive

Suggested filename: prioritization-advisor.md

Suggested placement: /skills/interactive/

Dependencies: None (standalone, but informs roadmap and backlog decisions)