"AI agents are machine learning models (e.g. Claude Opus 4.6) that have access to other software through a CLI harness (e.g. Claude Code) and operate in an iterative loop. These agents can be instructed to handle various tasks, some of which may not be covered in their training data. When lacking the appropriate training, software agents can be given access to new "skills," which are essentially added reference material to impart domain-specific capabilities."
"For example, an AI agent could be instructed how to process PDFs with a skill that consists of markdown text, code, libraries, and reference material about APIs. While the agent might have some idea how to do this from its training data, it should perform better with more specific guidance. Yet according to a recent study, SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks, asking an agent to develop that skill on its own will end in disappointment."
AI agents operate via a CLI harness and iterative loop, enabling them to call external software and handle tasks beyond their training. Skills are added reference materials—instructions, metadata, scripts, templates, and libraries—that supply procedural, domain-specific knowledge to agents. Supplying well-crafted skills improves task performance compared with relying on model inference alone. A benchmark called SkillsBench shows that asking agents to develop necessary skills autonomously often leads to poor results. The intelligence of LLMs at inference time can be overstated, so curated skills and integrations remain essential for reliable agent behavior.
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