prioritization — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited prioritization (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
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.
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.
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.
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.
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.
Use for ranking user-facing features when you have a baseline model.
$$Score = \frac{Reach \times Impact \times Confidence}{Effort \times (\frac{Ambiguity}{2})}$$
| 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.
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.
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.
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 EffortPRIORITIZATION 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| 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<!-- skill-graph-context:start (generated — do not edit by hand) -->
Classification
software-engineering-method (also: reasoning-strategy)trueWhen to use
prioritization-skill, roadmap-skill, priority-planning-modeRelated skills
constraint-awareness, bayesian-reasoning, playing-to-win, porters-five-forces, seven-powersKeywords
prioritization, RICE, ICE, MoSCoW, RICE-A, AI ambiguity, feature ranking, research prioritization, backlog management, MVP scope<!-- skill-graph-context:end -->
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.