PM Prioritization Advisor

Use this skill to decide how work should be prioritized before anyone starts pretending a spreadsheet score is objective truth.

What this skill solves

Different contexts need different prioritization lenses.

• Early-stage products often need speed and judgment.
• Growth-stage teams often need lightweight scoring and clearer tradeoffs.
• Mature products often need more data discipline and portfolio thinking.

The point is not to worship a framework. The point is to use one that fits the situation.

Common frameworks and when they fit

RICE

Use when:

• you have enough usage or customer volume data
• work items are comparable
• reach and effort can be estimated with some discipline

Avoid when:

• impact is mostly strategic or qualitative
• estimates are mostly fiction

ICE

Use when:

• speed matters more than precision
• the team needs a lightweight first pass
• data exists but is incomplete

Avoid when:

• leadership expects rigorous financial or portfolio logic

Value vs Effort

Use when:

• you need to compare quick wins, strategic bets, and expensive investments
• the team needs a fast workshop-friendly view

Avoid when:

• you need a repeatable scoring mechanism across many teams

Weighted scoring

Use when:

• you need transparency across multiple stakeholder dimensions
• the org wants criteria such as strategic fit, retention effect, revenue, compliance, and effort

Avoid when:

• the team will game the weights without agreeing on the criteria definitions

Cost of delay or urgency-based lenses

Use when:

• timing changes the value dramatically
• compliance, contract deadlines, or market windows matter

Avoid when:

• everything gets labeled "urgent"

Decision questions

Assess the situation on four axes:

1. Product stage
2. Data quality
3. Stakeholder complexity
4. Nature of the tradeoff

Product stage

• pre-PMF
• scaling
• mature optimization
• multi-product portfolio

Data quality

• weak
• partial
• strong

Stakeholder complexity

• small aligned team
• several functions but aligned
• misaligned or political environment
• portfolio coordination across teams

Nature of the tradeoff

• filter a large backlog
• compare strategic bets vs quick wins
• defend a decision to leadership
• sequence work under dependency constraints

Recommendation logic

Use these defaults:

• weak data + early stage -> ICE or value/effort
• moderate data + single-team roadmap -> RICE or weighted scoring
• strategic / cross-functional dispute -> weighted scoring plus narrative rationale
• high urgency or contractual timing -> add cost-of-delay view

If the request mixes several contexts, recommend a primary framework and a secondary check rather than inventing one giant hybrid.

Scoring hygiene

Regardless of framework:

• define each criterion clearly
• score all items on the same horizon
• show assumptions next to scores
• do not claim precision beyond the evidence
• rerun the framework when stage or evidence materially changes

Output format

Return:

1. Context read

• stage
• data quality
• stakeholder environment
• decision type

2. Recommended framework

• framework name
• why it fits
• when it will fail

3. How to apply it

• scoring dimensions
• rating guidance
• needed inputs

4. Ranked view or scoring template

• score table or comparison table

5. Final recommendation

• top options
• what drove the ranking
• biggest assumption still affecting the decision

Quality bar

This skill is successful only if:

• the framework recommendation matches the situation
• assumptions are visible
• the output helps the user make a decision now
• it does not hide weak evidence behind fake math

Use together with

• pm-feature-investment-advisor when cost, margin, or payback matter
• pm-roadmap-planning when prioritization must feed directly into sequencing