5 Agent Skill Design Patterns from Google: Cut Token Waste, Trigger the Right Behavior

This article is distilled from Lavi Nigam (Google ADK Agent Engineering expert) on Agent Skill design, summarizing 5 production-ready SKILL.md design patterns to help developers reduce token waste and improve skill quality. Background: Why Do We Need Skill Design Patterns? In AI Native product development, the root cause of massive token waste is twofold: forcing the model to repeatedly guess user intent that should be clearly specified, and expressing with complex instructions what could be expressed simply. With structured SKILL.md design patterns, we can: Reduce the model's "guessing" cost and trigger the right behavior precisely Standardize how skills are written, reducing friction in team collaboration Use progressive knowledge loading to dramatically cut token consumption Let the Agent activate the right skill at the right time Overview of the 5 Patterns Pattern 1: Tool Wrapper Core idea: SKILL.md uses load_skill_resource to load spec files from references/. The Agent applies tho