Prioritization

Prioritization

Concept of the skill

This skill provides prioritization frameworks for AI engineering: RICE-A (adding AI Ambiguity to RICE) for product features, ICE for research experiments, and MoSCoW for MVP/Release scoping.

Domain Context

What is this skill? This skill provides prioritization frameworks for AI engineering: RICE-A (adding AI Ambiguity to RICE) for product features, ICE for research experiments, and MoSCoW for MVP/Release scoping. Use when ranking the backlog, deciding which model research path to follow, or defining the scope of a new feature. Do NOT use for one-off task sequencing (use task skill) or personal time management.

Workflow

Use the ordered phases, checklists, and guardrails in the sections below as the canonical workflow for this skill. When multiple subsections describe steps, follow them in the order presented.

Coverage

Three prioritization frameworks (RICE-A, ICE, MoSCoW), the Human vs AI Matrix for task delegation, accuracy threshold setting to prevent scope creep, and framework selection rules for matching the right framework to the current development phase.

Philosophy of the skill

Without explicit prioritization frameworks, agents default to working on whatever seems most interesting or most recently mentioned. In AI-assisted development, this is especially dangerous because research tasks have unbounded ambiguity. The RICE-A extension adds an Ambiguity denominator that forces experimental work through a research phase before it competes with proven features for engineering time. This skill prevents the two most common prioritization failures: shipping low-confidence features ahead of proven ones, and chasing diminishing accuracy returns instead of delivering core value.

Prioritization is the science of ranking work by expected impact vs. effort. In AI development, traditional prioritization fails because it ignores research uncertainty. Good prioritization accounts for model ambiguity while maximizing the delivery of core product value.

1. RICE-A Framework — Product Feature Prioritization

Use for ranking user-facing features when you have a baseline model.

$$Score = \frac{Reach \times Impact \times Confidence}{Effort \times (\frac{Ambiguity}{2})}$$

RICE-A Definitions

Factor	Definition	Scale
Reach	Users/quarter affected	Absolute number
Impact	Contribution to core value proposition	3 (Massive) to 0.25 (Minimal)
Confidence	Data quality & baseline model presence	100% (Proven) to 50% (Guess)
Effort	Person-weeks (Inference + Data effort)	Number
Ambiguity	"Unknown unknowns" of model performance	1 (Deterministic) to 5 (Highly Experimental)

Rule: A high Ambiguity (A) score acts as a denominator that lowers the priority of experimental features until they move through the ICE research phase.

2. ICE Framework — Research Prioritization

Use for ranking 10+ experiments when you are in the "Discovery" phase.

$$Score = Impact \times Confidence \times Ease$$

Factor	Scale (1-10)	Definition
Impact	1-10	How much does this improve the baseline metric?
Confidence	1-10	How sure are we that this experiment will succeed?
Ease	1-10	How fast can we run this experiment (ignoring long-term COGS)?

Rule: ICE is for "fail-fast" research. Prioritize the highest score to find the working model architecture before applying RICE-A for product integration.

3. MoSCoW Method — MVP/Release Scoping

Use for defining the "Musts" of a specific delivery milestone.

Category	Definition	AI Example
Must-Have	Non-negotiable core functionality	"Model must correctly calculate profit_cents"
Should-Have	High priority but not critical for launch	"Latency should be < 2s for 95% of queries"
Could-Have	Desirable enhancements ("Nice-to-have")	"Multi-modal image support for product matching"
Won't-Have	Out of scope for this milestone	" chasing the final 0.1% of accuracy"

Rule: Protect the team from "Accuracy Creep". Define the "Must-Have" accuracy threshold before starting implementation.

4. The Human vs. AI Matrix (The Gold Quadrant)

Prioritize work based on who is best suited for the task.

       High |  (1) AI ASSISTED       |  (2) THE GOLD QUADRANT 
Human       |  (Research, Strategy)  |  (Bulk Gen, Triage, Tests)
Effort      |------------------------|--------------------------
            |  (3) IGNORE            |  (4) HUMAN ONLY
       Low  |  (Trivial Tasks)       |  (Creative, High-Stakes)
            ----------------------------------------------------
                   High              Low
                       AI Effort

• The Gold Quadrant: High Human Effort / Low AI Effort. These tasks have the highest ROI for AI agents (e.g., generating 50 tests, triaging 1000 logs).

Verification

PRIORITIZATION CHECK
====================
[ ] Framework matches the phase (ICE for Research, RICE-A for Product)
[ ] Ambiguity (A) score assigned for experimental features
[ ] Confidence score grounded in data quality (not just vibes)
[ ] MoSCoW defined for the current milestone
[ ] Accuracy threshold set (prevents Accuracy Creep)
[ ] Task sits in the "Gold Quadrant" for AI agents

Do NOT Use When

Instead of this skill	Use	Why
Sequencing the steps inside one chosen task	task-path-optimization	Task-path optimization owns execution order; prioritization owns which work item should be selected.
Estimating confidence under uncertainty	bayesian-reasoning	Bayesian reasoning owns evidence updates; prioritization consumes confidence as one scoring input.
Competitive positioning or market strategy	positioning	Positioning shapes market claims and alternatives; prioritization ranks work after strategy inputs are known.
Defining product requirements or specifications	spec-driven-development	Prioritization ranks work; SDD defines what the work contains

Source: REPORTS/Report_UI-UX-Thesis-Audit_Gemini-3-Flash_13-03-2026-05-15.md