Agent Skills and how to use them
If your AI assistant keeps giving generic, unreliable answers, the problem is not the model. It is the missing structure around it. That is exactly what Agent Skills solve. Modern LLMs are strong enough to handle complex tasks. The real issue is that they are working without stable context, rules, or domain-specific instructions that guide how they should behave in your environment. This is where…
Agent Skills are a solution to the issue of generic, unreliable outputs from AI assistants. Modern Large Language Models (LLMs) are capable enough to handle complex tasks, but the real problem lies in the lack of stable context, rules, or domain-specific instructions that guide how they should behave in a specific environment. Agent Skills address this by providing structured instructions and resources that an AI model can use for a given task, improving its output.
They are designed to work with modern AI coding assistants and LLM-based agents, allowing for domain expertise, repeatable workflows, and cross-product reuse. Agent Skills load in three stages: discovery, activation, and execution, ensuring that only relevant skills are loaded when needed, keeping a small context footprint.
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