General-purpose agents handle a broad range of tasks, but you still need them to follow the procedures that run your business: compliance checks, document-processing workflows, escalation policies, engineering conventions. Encoding all of that in one system prompt or in application logic gets hard to maintain and update. Skills are a modular alternative. A skill is a reusable set of instructions, usually stored in a SKILL.md file, that teaches an agent a domain-specific task like redacting a contract, reconciling an invoice, or following a team’s pull-request conventions. Because skills follow the open Agent Skills standard, they are portable across compatible harnesses, and the agent loads only the skill it needs at runtime instead of carrying every procedure in its core instructions.

A skill packages one or more tools with the context an agent needs to use them correctly:

This modular approach helps teams specialize agents faster, reuse proven procedures across agents and workflows, keep behavior consistent, and update domain-specific guidance without fine-tuning the underlying model or rewriting the agent’s core logic.

This skill composability in agents introduces two failure modes that general output-quality metrics can miss: the agent invokes a skill that is not appropriate for the task and the agent invokes the right skill but skips or only partially follows its instructions. Both failures can produce a fluent, plausible response without having used your pre-determined domain knowledge. An evaluation therefore cannot examine the final response alone.

To make these failures measurable, Strands Evals SDK and Amazon Bedrock AgentCore Evaluations , a capability of Amazon Bedrock AgentCore, add skill-focused evaluators:

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