Natural Language Processing

Build recommendation systems using collaborative filtering, content-based filtering, matrix factorization, and neural network approaches

Build collaborative and content-based recommendation engines for product recommendations, personalization, and improving user engagement

Build predictive models using linear regression, polynomial regression, and regularized regression for continuous prediction, trend forecasting, and relationship quantification

Classify text sentiment using NLP techniques, lexicon-based analysis, and machine learning for opinion mining, brand monitoring, and customer feedback analysis

Workout block model, types, and operations. Use when building workouts, managing block kinds (strength/AMRAP/EMOM/Tabata/ForTime/cardio), handling sets/reps, or working with workout persistence and benchmarks. Triggers: "workout", "block", "strength", "cardio", "AMRAP", "EMOM", "Tabata", "ForTime", "exercise", "set", "reps", "weight", "RIR", "timed block", "create workout", "build workout", "block kind", "discriminated union", "block result", "round", "interval", "benchmark", "template", "persist workout", "complete workout".

Build responsive, mobile-first layouts using fluid containers, flexible units, media queries, and touch-friendly design that works across all screen sizes. Use this skill when creating or modifying UI layouts, responsive grids, breakpoint styles, mobile navigation, or any interface that needs to adapt to different screen sizes. Apply when working with responsive CSS, media queries, viewport settings, flexbox/grid layouts, mobile-first styling, breakpoint definitions (mobile, tablet, desktop), touch target sizing, relative units (rem, em, %), image optimization for different screens, or testing layouts across multiple devices. Use for any task involving multi-device support, responsive design patterns, or adaptive layouts.