ai-prioritizelisted

## Why this exists, and what I'd change **Why it exists.** RICE doesn't work for AI features. Reach, impact, confidence, and effort assume predictable outputs. AI features live on a spectrum of "how well does it work" and need a different lens. This skill replaces RICE with four AI-native scores: model confidence, cost per inference, eval-readiness, and fallback quality. **Design tradeoffs.** - **Four scores, not one composite.** I don't roll them into a single number. Cost: harder to sort a backlog. PMs who want a ranked list have to apply their own weighting. - **Forces a ship/MVP/prototype/kill decision instead of a score.** Every feature ends with a verb, not a number. Cost: less satisfying for execs who like a "RICE score of 47" answer. - **Eval-readiness is a hard gate.** If you can't measure model quality on production data, the framework blocks shipping. Cost: real, slow features stuck in MVP because golden examples don't exist yet. **What I'd change.** Add a calibration loop. After three AI features ship, compare predicted confidence against actual quality, and adjust the framework's thresholds based on the team's track record. --- # AI Feature Prioritization: Confidence-Gated Framework **When to use:** When prioritizing AI-powered features, deciding what to ship vs prototype, or building an AI product roadmap. Traditional frameworks (RICE, WSJF, MoSCoW, ICE) break down for AI because they assume predictable impact and binary ship/no-ship decisions. AI features