prioritization-frameworks

Imported from borghei/claude-skills (project-management/execution/prioritization-frameworks/SKILL.md). Install upstream with npx skills add borghei/claude-skills --skill prioritization-frameworks. Copyright stays with the author (MIT + Commons Clause).

Prioritization Framework Expert

Overview

A comprehensive reference to 9 prioritization frameworks with automated scoring, ranking, and guidance on which framework to use in which situation. The core principle: prioritize problems (opportunities), not features. Features are solutions to problems. If you prioritize features directly, you skip the step of understanding whether the problem is worth solving.

Core Capabilities

• 9 frameworks — RICE, ICE, Opportunity Score, Eisenhower, Impact vs Effort, Risk vs Reward, Kano, Weighted Decision Matrix, MoSCoW (full definitions, formulas, and worked examples in references/frameworks-catalog.md).
• Framework selection — a decision tree maps the thing you are prioritizing (problems, features, personal tasks, high-uncertainty bets) to the right method.
• Automated scoring — prioritization_scorer.py ranks items for RICE, ICE, Opportunity, MoSCoW, and Weighted Decision Matrix.
• Two-step discipline — prioritize problems first (Opportunity Score), then prioritize solutions (RICE/ICE).

When to Use

• Backlog Grooming -- Too many items, need to rank them objectively.
• Quarterly Planning -- Deciding which initiatives to invest in.
• Stakeholder Alignment -- Need a structured way to resolve competing priorities.
• Feature Triage -- Quick sorting of a long list into actionable categories.

Clarify First

Before scoring, settle the inputs below. When one is missing or vague and would change the result, ask about it; when it wouldn't, use a sensible default and say which one you used.

• What you're ranking: problems/opportunities vs features vs personal tasks (the decision tree picks the framework from this).
• Framework: rice / ice / opportunity / moscow / weighted (each requires different score fields and emits different rankings).
• Quality of the estimates: measured data vs guesses for reach/impact (guesses make RICE/ICE precision misleading; switch to a coarser method).

Ask about the two or three inputs that matter most for this request, not the whole list. If the user wants a result straight away, produce it and state your assumptions at the top.

Quick Start

Supported frameworks: rice, ice, opportunity, moscow, weighted. See references/tool-and-troubleshooting.md for input JSON schemas and flags.

Anti-Patterns

Every item scores massive impact

Mistake: Most of the backlog gets Impact = 3 on the RICE scale. Why it happens: Each item has a champion, and nobody wants theirs marked minor. Instead: RICE ranks items against each other, so uniform impact ranks nothing. If more than 30% of items score 3, recalibrate: only items with behavioural or revenue evidence keep it.

Confidence padded to reach the cut line

Mistake: A favoured item's Confidence moves from 50% to 80% with no new evidence. Why it happens: The ranking was decided first, and the score is adjusted to match it. Instead: Add a Confidence Source column. An item with no written source is capped at 50%.

Effort scored without engineering

Mistake: The PM sizes Effort alone, as small, medium or large. Why it happens: It is quicker than booking engineers, and backend, security and migration work is hard to see from the product side. Instead: Effort is the denominator, so a wrong estimate flips the ranking. Don't publish a score until an engineer has signed off on its Effort.

Eight more in references/red-flags.md; framework-specific mistakes in references/prioritization-guide.md.

References

Load the reference that matches the task — keep this file lean and pull detail on demand:

• references/frameworks-catalog.md — full definitions, formulas, strengths/weaknesses, and worked examples for all 9 frameworks, the framework decision tree, and the "prioritize problems, not features" principle. Read when choosing or applying a specific framework.
• references/prioritization-guide.md — detailed formulas, decision tree, and facilitation tips. Read when facilitating a scoring session with a group.
• references/red-flags.md — anti-patterns and warning signs in prioritization practice. Read when a process feels off or results are being gamed.
• references/tool-and-troubleshooting.md — prioritization_scorer.py flags, per-framework input JSON schemas, troubleshooting table, and success criteria. Read when running the tool or diagnosing scoring problems.
• assets/prioritization_matrix_template.md — scoring templates for each framework. Use when capturing a manual scoring exercise.

Scope & Limitations

In Scope:

• 9 prioritization frameworks with scoring, ranking, and explanation (RICE, ICE, Opportunity Score, Eisenhower, Impact vs. Effort, Risk vs. Reward, Kano, Weighted Decision Matrix, MoSCoW)
• Automated scoring and ranking for RICE, ICE, Opportunity Score, MoSCoW, and Weighted Decision Matrix
• Framework selection guidance via Decision Tree
• Demo data for each framework to illustrate input/output formats

Out of Scope:

• Real-time Jira/Linear backlog integration (manual JSON input required)
• Cost-of-delay or WSJF calculations (see senior-pm/ skill for SAFe portfolio prioritization)
• User research to gather importance/satisfaction data for Opportunity Score (see product-team/ skills)
• Strategic portfolio allocation decisions (see senior-pm/ skill)

Important Caveats:

• No framework produces a "correct" answer. Prioritization frameworks are decision-support tools that structure conversation, not algorithms that replace judgment.
• RICE and ICE are best for data-rich environments. If your reach and impact estimates are pure guesses, the precision of the formula is misleading.
• The most successful teams combine frameworks: start with Opportunity Score to identify the right problems, then use RICE to rank solutions.
• For teams with 50+ people or multiple stakeholder groups, use WSJF or Weighted Decision Matrix with agreed criteria to ensure buy-in.

Integration Points