What problem does it solve? Product teams often rank backlogs on gut feel or quote framework scores as if they were hard data, hiding the guesses behind Reach, Impact, Confidence, and Effort inputs. This Skill provides the mechanics of the four common prioritization frameworks, names when each fits, and forces the assumptions behind every score into the open. ## Core Features & Use Cases - Four framework mechanics: Full formulas, input scales, and fit conditions for RICE, ICE, WSJF, and value-vs-effort 2x2 grids. - Score honesty checks: Named failure modes (reach-as-guess, confidence creep, effort sandbagging) plus mandatory Notes columns recording the basis for every estimate. - Override discipline: A four-step process for disagreeing with a framework result while logging the override in the initiative's rationale field. - Use Case: When ranking 15 candidate initiatives for quarterly planning, use this Skill to produce a RICE table with segment-weighted reach, evidence-anchored confidence, and engineering-signed effort estimates that stakeholders can challenge line by line. ## Quick Start Ask the AI to score and rank your backlog items using RICE with notes explaining each Reach, Impact, Confidence, and Effort estimate.
