Minko Getschev, AI lead at Google, shares insights on building effective agent skills, including architecture, skill creation best practices, and evaluation methods. Agent decision-making, planning, and hybrid architectures are explored, with a focus on the React loop for tool utilization. Context expansion and management, utilizing tools like MCP and CLIs, enhance agent power. The discussion includes comparisons between MCP and CLIs in agent systems, emphasizing context handling and debugging. Agent skills are structured as procedural instructions, with an open standard focused on tasks and workflow logic. Best practices for skill workflow involve avoiding redundancy, optimizing front matter, and focusing on predictable execution. Utilization and management of agent skills are crucial for performance optimization. Continuous skill evaluation, testing, and improvement are highlighted, with SkillGrade for evaluating agent skills. Skill workflow execution analysis involves identifying failures, log analysis, and rerunning workflows for successful execution.