We provide a structured overview of fundamental skills and collective scenarios in swarm robotics, highlighting core models, methods, and applications. This chapter distinguishes between fundamental skills and more complex scenarios that form the building blocks of robotic collective intelligence. We categorize five domains of skills: physical (e.g., motion, collision avoidance), temporal (synchronization), numerical/logical (counting), computational (data processing), and minimal communication for information sharing. Individual skills, such as collision avoidance, ensure safety, while synchronization remains difficult without a global clock. Random motion strategies (e.g., Lévy flights) support efficient exploration. Collective behaviors are modeled through frameworks, such as those developed by Reynolds, Couzin, and Vicsek, which highlight complex dynamics and scale-free correlations. Scenarios progress from basic aggregation and dispersion to advanced tasks such as pattern formation, clustering, self-assembly, and collective construction. Further topics include collective transport with possible super-linear gains, as well as foraging, shepherding, and division of labor, where dynamic environments may yield counterintuitive effects such as “less is more.”