What problem does it solve? Choosing which new menu item to launch is often guesswork. This Skill replaces gut-feel decisions with a multi-stage screening flow: it identifies gaps in the current menu portfolio, simulates segment-level customer reactions via SSR (Synthetic Segment Reaction), validates gross margin structure, and calibrates predictions against real sales from a limited release before full rollout. ## Core Features & Use Cases - Portfolio Gap Analysis: Uses menu engineering quadrants (Star/Plowhorse/Puzzle/Dog) and ABC contribution analysis to find missing price bands and categories in the current lineup. - SSR Reaction Simulation: Conditions personas on real customer segment data (frequency, spend, visit context) and scores purchase intent per candidate via embedding-based anchor comparison, producing relative rankings rather than absolute predictions. - Margin Gate & Limited Release: Runs read-only price simulation for break-even and cannibalization tolerance, then launches top candidates as a single-store, time-boxed promo with human publish approval, and calibrates SSR predictions against actual sales. - Use Case: A store manager has three seasonal menu ideas. The Skill ranks them by segment reaction, flags one that fails the margin check, launches the winner as a limited-time offer at one flagship store, and schedules a sales-based answer-check before chain-wide rollout. ## Quick Start Ask the assistant to screen your new menu ideas, for example: "I have three new menu candidates with prices and costs — which one should we actually launch?"
