In AI, a skill is a reusable package of instructions that an assistant or agent can load when a specific type of task comes up. It might include workflow guidance, metadata, examples, templates, reference files, or even scripts. The point is to help a general-purpose AI act more like a specialist for a repeatable task.
That makes skills different from one-off prompts. A prompt is something you type for a single conversation. A skill is more like a standing playbook the AI can reuse when relevant. It is also different from a tool: a tool lets the AI take an action, such as searching the web or running code, while a skill teaches the AI how to approach the work.
For example, a company might create a brand style skill for marketing copy, a spreadsheet analysis skill for monthly reporting, a document creation skill for proposals, or a support workflow skill for handling customer tickets. Used well, skills reduce repeated prompting, capture institutional knowledge, and make AI output more consistent across a team.
The risk is that a skill can also encode a bad process. If the instructions are outdated, vague, or unsafe, the AI may repeat the same mistake over and over. Skills that include scripts or connect to outside systems need extra review, because they can affect files, data, or workflows directly. Treat skills like lightweight software: useful, reusable, and worth maintaining.
If you want to move from one-off AI prompting to reusable workflows your team can trust, start with the fundamentals of giving AI clear instructions. Coursera’s Prompt Engineering for ChatGPT course teaches the structure behind reliable prompts, which is the same foundation you need when designing useful AI skills.*