Prioritization Framework

Quick facts

Phase: Define | Version: 1.0.0 | Category: planning | License: Apache-2.0

Try it: /prioritization-framework "Your context here"

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.

How to Use

Use the /prioritization-framework slash command:

/prioritization-framework "Your context here"

Or reference the skill file directly: skills/define-prioritization-framework/SKILL.md

Output Template

Prioritization: [Decision Context]

Applicability Filter Summary

Ran: [e.g., RICE, ICE, MoSCoW]
Excluded: [e.g., Kano - no customer research; Weighted Scoring - single criterion]

Inputs Summary

[Item list and available data per item; note assumptions]

Per-Framework Scoring

RICE

Item	Reach (users/qtr)	Impact (0.25-3)	Confidence (%)	Effort (eng-wk)	RICE Score	Notes
[Item A]	[N]	[0.25-3]	[%]	[N]	[score]	[note]

ICE

Item	Impact (1-10)	Confidence (1-10)	Ease (1-10)	ICE Score	Notes
[Item A]	[N]	[N]	[N]	[score]	[note]

MoSCoW

Item	Bucket (Must/Should/Could/Won’t)	Rationale	Risk if dropped
[Item A]	[Bucket]	[Why]	[Risk]

Per-Framework Ranking Output

RICE ranking: [items sorted by score, high to low]
ICE ranking: [items sorted by score, high to low]

Cross-Framework Comparison

Item	RICE rank	ICE rank	MoSCoW bucket	Agreement
[Item A]	[1]	[1]	[Must]	[Strong]
[Item B]	[2]	[8]	[Should]	[Divergent - why]

Executive Summary with Recommendation

[Recommendation]

Sensitivity / What Changes the Ranking

[If Confidence on Item X is wrong, then …]
[If Effort on Item Y doubles, then …]

Recommendations (Sequencing)

Fund now: [Items]
Defer / drop: [Items]
Data that would change this: [What to gather]

Limitations and Biases

[Limitation 1]
[Limitation 2]

Example Output

Prioritization: Q3 Roadmap Candidates (Project-Management SaaS)

All reach, impact, effort, and confidence values below are illustrative [fictional] PM inputs for this scenario; replace them with your own estimates.

Applicability Filter Summary

We have reach, impact, effort, and confidence estimates per feature, so RICE and ICE both run. The decision also bounds Q3 scope, so MoSCoW runs as a commitment view. Weighted Scoring is excluded (no competing multi-stakeholder criteria were provided). Kano is excluded: no customer-research data was supplied. To unlock Kano, run a Kano survey on these six features.

Inputs Summary

Six Q3 candidate features with PM-supplied estimates. Reach is measured in affected users per quarter; effort in engineering-weeks. Confidence reflects how solid the estimates are.

Per-Framework Scoring

RICE

Item	Reach (users/qtr)	Impact (0.25-3)	Confidence (%)	Effort (eng-wk)	RICE Score	Notes
Guest sharing links	5,000	1	80%	1	4,000	Cheap, broad
Bulk task editing	8,000	1	90%	2	3,600	High-confidence quick win
Mobile offline mode	12,000	2	60%	8	1,800	Big reach, big effort
SSO / SAML	2,000	3	90%	4	1,350	Narrow reach, high per-user impact
Custom dashboards	4,000	2	70%	5	1,120	Mid on everything
AI task suggestions	15,000	1	40%	10	600	Huge reach, low confidence, high effort

ICE

Item	Impact (1-10)	Confidence (1-10)	Ease (1-10)	ICE Score	Notes
SSO / SAML	9	9	6	486	High-value, well-understood
Guest sharing links	6	8	9	432	Easy and solid
Bulk task editing	6	9	8	432	Easy and solid
Custom dashboards	7	7	5	245	Middling
Mobile offline mode	8	6	3	144	Valuable but hard
AI task suggestions	7	4	2	56	Speculative and hard

MoSCoW (Q3 scope bound)

Item	Bucket	Rationale	Risk if dropped
SSO / SAML	Must	Three enterprise deals are blocked on it	Lose committed enterprise revenue
Guest sharing links	Must	Competitive parity; churn risk without it	Continued competitive losses
Bulk task editing	Should	High-value quick win	Slower power-user workflows
Custom dashboards	Should	Requested but not blocking	Mild dissatisfaction
Mobile offline mode	Could	Valuable but 8 eng-weeks	Mobile users wait another quarter
AI task suggestions	Won’t (this time)	Low confidence, 10 eng-weeks	Defer until validated

Per-Framework Ranking Output

Each scoring table above is sorted high to low, so the per-framework ranking is the row order shown (top item first, lowest last). The side-by-side rank positions, and the items where the frameworks disagree, are consolidated in the Cross-Framework Comparison below.

Cross-Framework Comparison

Item	RICE rank	ICE rank	MoSCoW bucket	Agreement
Guest sharing links	1	2	Must	Strong
Bulk task editing	2	3	Should	Strong
Mobile offline mode	3	5	Could	Divergent
SSO / SAML	4	1	Must	Divergent
Custom dashboards	5	4	Should	Close
AI task suggestions	6	6	Won’t	Strong (agree: defer)

Divergent - SSO / SAML (RICE 4th, ICE 1st, MoSCoW Must): RICE’s Reach term punishes SSO because it only touches 2,000 users. ICE has no reach term, so SSO’s high per-user impact and high confidence push it to the top. MoSCoW agrees with ICE because the 2,000 users are concentrated, high-value enterprise accounts with blocked deals. The divergence reveals that RICE under-weights revenue-concentrated features. This is the most important finding in the analysis.

Divergent - Mobile offline (RICE 3rd, ICE 5th): RICE rewards the large reach (12,000); ICE penalizes the low Ease (3, an 8-week build). The driver is effort vs. reach.

Executive Summary with Recommendation

Fund Guest sharing and Bulk task editing first: they top both scored frameworks and are cheap, so they are unambiguous wins. Fund SSO / SAML despite its 4th-place RICE score - the RICE Reach term misleads here because the 2,000 affected users are enterprise accounts with revenue already blocked, which ICE and MoSCoW both surface. Defer AI task suggestions (all three frameworks agree it is not ready). The recommendation is robust except for SSO, whose ranking depends entirely on whether you weight raw reach (RICE) or strategic revenue concentration (ICE + MoSCoW); given the blocked deals, weight the latter.

Sensitivity / What Changes the Ranking

If SSO’s enterprise deals were not actually blocked, its Impact drops and it falls in ICE too, validating RICE’s lower placement - so confirm the blocked-deal claim before committing.
If Mobile offline’s effort came in at 4 eng-weeks instead of 8, its RICE score doubles to 3,600 and it jumps to a clear Should.
AI task suggestions stays last unless confidence rises above ~70%; a cheap spike to de-risk it would change its standing more than any other input.

Recommendations (Sequencing)

Fund now: Guest sharing, Bulk task editing, SSO / SAML
Fund if capacity allows: Custom dashboards
Defer: Mobile offline (revisit if effort drops), AI task suggestions (revisit after a confidence-building spike)
Data that would change this: Confirm the SSO blocked-deal value; re-estimate Mobile offline effort; run a spike on AI suggestions

Limitations and Biases

RICE systematically under-ranks high-value, low-reach features (the SSO problem); do not let it auto-decide enterprise/strategic items.
None of these frameworks measure sequencing dependencies (e.g., if SSO must ship before an enterprise launch). Pair this ranking with a roadmap view.
All scores rest on PM estimates; the cross-framework agreement is only as good as those inputs.

Real-World Examples

See this skill applied to three different product contexts:

Storevine (B2B): Storevine B2B forecasting platform - reducing a 12-feature MVP wishlist to a shippable 8

Prompt:

/prioritization-framework

we need to cut our storevine MVP from 12 features to 8 to hit the launch
date. it's a B2B inventory forecasting platform. i don't have reach numbers
or solid effort estimates per feature yet - it's pre-launch.

features: 1. demand forecast core  2. reorder recommendations
3. multi-warehouse  4. CSV data import  5. API integration (Shopify etc)
6. dashboard/reporting  7. low-stock alerts  8. user roles/permissions
9. forecast accuracy tracking  10. seasonal adjustment
11. supplier lead-time modeling  12. mobile view

what should we cut?

Output:

Prioritization: Storevine MVP Scope Reduction (12 to 8)

Brainshelf (Consumer): Brainshelf consumer book-curation app - prioritizing 8 candidate features for the Q3 roadmap

Prompt:

/prioritization-framework

prioritize our Q3 candidate features for brainshelf (AI book-curation
subscription). decision context: Q3 roadmap, ~14 eng-weeks of capacity.

features (i have engagement data + eng estimates):
1. rec algorithm v2   2. social follow-friends   3. mobile app
4. public reading profiles   5. reading streaks   6. audiobook recs
7. export to goodreads   8. family plan

run whatever frameworks apply and show me where they disagree.

Output:

Prioritization: Brainshelf Q3 Roadmap Candidates

Workbench (Enterprise): Workbench internal dev-experience platform - triaging a 30-idea backlog to 5 for the next sprint

Prompt:

/prioritization-framework

triage our dev-experience backlog and help us pick 5 for next sprint. we have
~30 ideas, no hard data per item. our team cares about three things:
developer velocity, adoption risk (will engineers actually use it), and
technical-debt impact.

top ideas include: one-command dev env, faster CI, better build error
messages, service catalog, auto API docs, local secrets mgmt, standard
logging lib, PR template+checks, flaky-test detection, dep-upgrade bot,
onboarding golden path, incident runbook automation ... (~18 more smaller).

Output:

Prioritization: Workbench Dev-Experience Backlog Triage (30 to 5)

Quality Checklist

Before finalizing, verify:

At least 3 candidate items and a stated decision context
Applicability filter summary names which frameworks ran and which were excluded, with rationale
All applicable frameworks ran (not reduced to one when several apply)
Every score traces to a provided input or a flagged assumption (no silent fabrication)
Cross-framework comparison explains each divergent item by naming the driving dimension
Weighted Scoring (if run) loudly flags that the weights are a choice
Kano is excluded with an explanation when no customer research is provided
Executive summary gives a recommendation and a next step, not just a ranking