Agent Skills

When AI agents evolve from "general-purpose tools that can do anything" to "specialists that reliably handle specific tasks," the difference comes down to Skills design. Taking Claude Code as an example, Skills are defined as Markdown files. By describing execution steps, tools to use, output formats, and trigger keywords, a user simply saying "write an SEO article" activates the relevant Skill, which handles everything end-to-end — from brief confirmation to writing, quality checks, and database registration. In the OpenClaw ecosystem, over 5,700 community-created Skills are shared on a marketplace. There are even cases where agents write their own code to generate new Skills, and a self-expansion mechanism — where capabilities grow on demand — is steadily taking shape. The key to designing Skills is finding the right granularity. Too coarse, and they become overly generic with reduced accuracy; too fine, and the number of Skills explodes, driving up management costs. In the author's experience, a granularity of "one Skill corresponds to one user action" tends to be just right. While MCP handles the connection between agents and external tools, Skills handle the internal workflow and decision logic within the agent. The two are complementary.