In a matter of months, skills have taken the agent ecosystem by storm: major LLM agent platforms (e.g., Cursor, Claude Code, Antigravity, Coze) now support skills natively, and skill counts across domains are growing at a breakneck pace. Although skills initially served as on-demand prompts to avoid excessive prompt context length, they differ fundamentally from conventional prompts in three respects: (i) skills embody locality: the very presence of a skill implies repeated reuse; (ii) skills encode concrete scenarios, yielding stronger determinism and verifiability than generic prompts; (iii) skills exhibit common requirements on the runtime environment, enabling systems to serve skills more effectively. These properties thus present both new opportunities and new challenges for system design. This paper surveys nearly 100,000 skills from public repositories and analyzes skill characteristics along the dimensions of structure, execution patterns, and system requirements. We argue that skills have become a new form of application and impose new demands on the underlying system; these demands will give rise to a new system abstraction, Skill OS, that treats skills as first-class execution artifacts and addresses caching, execution environment construction, global skill management, structured failure handling, and runtime security enforcement.