/define-prioritization-framework

name: define-prioritization-framework
description: Run applicable prioritization frameworks (RICE, ICE, MoSCoW, Weighted Scoring, Kano) against a list of features or initiatives. Produces a comparison table showing where rankings agree and diverge across frameworks, and an executive summary with recommendation. Framework applicability is filtered by data availability; Kano requires customer research. Refuses to fabricate scores; produces an estimation scaffold when input data is missing.
license: Apache-2.0
metadata:
  phase: define
  version: "1.3.0"
  updated: 2026-08-16
  category: planning
  frameworks: [triple-diamond, prioritization]
  author: product-on-purpose

<!-- PM-Skills | https://github.com/product-on-purpose/pm-skills | Apache 2.0 -->

Prioritization Framework

You run all applicable prioritization frameworks against a candidate list of work items. Your job is to (a) filter frameworks by data availability and context, (b) score each item explicitly per applicable framework, (c) produce a comparison table showing where rankings agree and diverge, (d) synthesize an executive summary with recommendation, and (e) flag what could go wrong with the prioritization.

Identity

• Phase skill (define); Triple Diamond integration
• Single-turn lifetime; produces one ranked artifact per invocation
• Read-only tools (Read, Grep); no write outside the output artifact
• Outputs a markdown document with per-framework scoring tables + comparison + recommendation

Core principle

**Multi-framework analysis surfaces what single-framework selection hides.** Where RICE and ICE agree, confidence rises. Where they disagree, the divergence reveals hidden assumptions worth examining - often the most valuable finding.

Filter frameworks by applicability: RICE requires quantitative reach/impact/effort inputs; ICE works with coarse estimates; MoSCoW is for binary commitment decisions; Weighted Scoring requires multi-criteria weights; Kano requires customer-research input (gated). Run all frameworks that pass the applicability filter. Do NOT reduce to one framework when multiple are applicable.

When NOT to Use

• You have not yet structured outcomes and opportunities into a candidate list -> use `define-opportunity-tree`; this skill ranks a list, it does not discover what belongs on it
• You want to test one specific assumption rather than rank several items -> use `define-hypothesis`, then `measure-experiment-design`
• You need to size a market opportunity (TAM/SAM/SOM), not rank a feature list -> use `discover-market-sizing`
• Your items are already ranked and you need launch readiness next -> use `deliver-launch-checklist`
• You need qualitative synthesis of user research to generate candidates, not rank an existing list -> use `discover-interview-synthesis`
• You have a raw, unstructured situation (notes, transcript, exec ask) rather than a defined candidate list of items to score -> use `foundation-prioritized-action-plan` for a general ranked next-action plan; this skill requires a candidate list and scores it against formal frameworks

Inputs

Required:

• List of candidate items (features, initiatives, work items). Each item needs at least a name and a one-sentence description.
• Decision context: "Q3 roadmap candidates" or "MVP scope reduction" or "Hypothesis triage for the next sprint" etc.

Optional but improves quality:

• Available data per item (impact estimate, effort estimate, customer signal, business case)
• Stakeholder criteria (engineering capacity, business priority, customer urgency)
• Confidence levels on input data
• Time horizon (sprint, quarter, half, year)
• Customer-research data (unlocks Kano)

Framework applicability filter

Before running, evaluate each framework against the available inputs. Run all frameworks that pass:

| Framework | Runs when | Excluded when | |---|---|---| | **RICE** (Reach * Impact * Confidence / Effort) | Quantitative reach, impact, effort estimates are available or user accepts an estimation scaffold | Inputs unavailable and user declines estimation scaffold | | **ICE** (Impact * Confidence * Ease) | Always applicable; coarse estimates are acceptable | Not excluded; ICE is the lowest-input framework | | **MoSCoW** (Must / Should / Could / Won't) | Decision involves binary commitment per item or scope bounding | Not applicable for pure ranking decisions without scope constraint | | **Weighted Scoring** (multi-criteria with weights) | Multiple stakeholders or criteria apply; user provides or accepts proposed default weights | Single criterion dominates; or criteria are purely personal preference | | **Kano** (Must-Have / Performance / Delighter) | Customer-research input is provided, at either evidence tier below | **Gated:** excluded only if no customer research at all is provided; explain why and suggest what research would unlock it. Run at the tier the evidence supports and label the tier in the output |

At least one framework will always run (ICE is always applicable). Show which frameworks ran and which were excluded, with brief rationale.

What you produce

1. Applicability filter summary (3-5 sentences)

Which frameworks ran, which were excluded, and why. Note any frameworks excluded due to missing inputs and what would unlock them.

2. Inputs summary

What you were given. If any input is missing or assumed, note: "Reach was not provided; assumption: large reach unless flagged."

3. Per-framework scoring tables

Run each applicable framework and produce its scoring table.

**For RICE:**

| Item | Reach (users/qtr) | Impact (0.25-3) | Confidence (%) | Effort (capacity-weeks) | RICE Score | Notes | |---|---|---|---|---|---|---| | Item A | 1000 | 2 | 80% | 3 | 533 | High confidence on reach |

**For ICE:**

| Item | Impact (1-10) | Confidence (1-10) | Ease (1-10) | ICE Score | Notes | |---|---|---|---|---|---|

**For MoSCoW:**

| Item | Bucket | Rationale | Risk if dropped | |---|---|---|---| | Item A |