Agent Skills

Agent skills are modular components that define specific abilities or behaviors for AI agents. Rather than hardcoded instructions, a skill is like a “job description”, it describes what the agent can do, the kind of input it expects, and the kinds of outputs or behaviors it can produce. Skills guide agents by providing them with knowledge, context, and purpose, helping them to operate effectively in non-deterministic scenarios where flexibility and judgment are required.

Key characteristics of agent skills:

• They enable agents to handle complex, open-ended, or ambiguous tasks—such as understanding documentation or synthesizing information from large volumes of text.
• Skills are not tools themselves, but rather reusable competency modules that you can compose, mix, and match.
• Skills provide structure and guidance, making agents more reliable and explainable.
• They serve as excellent documentation, clarifying what the agent is capable of and how it should behave.
• Skills are defined in ways that LLMs (large language models) can understand.

A useful mental model for Agent Skills begins with the idea that whenever an LLM or agent is assigned a task, it starts from scratch with an entirely empty context. Skills provide initial guidance, highlighting what is important and how to begin.

Visit Memgraph Agent Skills repo, to start using skills.