What are agent skills? Here’s how to level up your AI agents

There’s an old saying: "Give a man a fish and you feed him for a day. Teach him how to fish and you feed him for a lifetime." Whether or not this saying is familiar, it’s applicable to the workplace. A manager who simply gives an employee an answer or mandate doesn’t teach the employee how to reason through a particular problem and learn from it. Similarly, as capable as your AI is, it also needs help producing a desired outcome. Both employees and AI agents need training — and even with training, they’re likely to deliver results that reflect their own unique context and perspective.

With AI agents, the goal is to level up their skills so that they always learn and improve, without needing the same inputs over and over. That’s where defining agent skills becomes particularly useful. You can think about it like preparing onboarding information for a new hire.

What are AI agent skills?

Agent skills are reusable capabilities that any agent can invoke.

The Agent Skills format was originally developed by Anthropic for Claude, but has quickly grown to represent a wide package of instruction, context, and domain knowledge that ultimately shapes how AI agents approach tasks over time. The goal is to give agents the bandwidth and knowledge to approach tasks in similar ways across sessions. In practical terms, this means that you don’t have to feed the agent your brand style guidelines or most current product positioning with every request. Instead, the agent already knows. Over time, the agent becomes a specialist — much like a long-time employee — who follows established processes, matches standards, and produces outputs that adhere to brand standards.

Agent skills are now supported as a general concept across a growing number of agent building tools, including Airtable Hyperagent, and leading models like Claude and ChatGPT. 

Examples of what agent skills do 

AI is great at looking across information and processing it to produce a given content format. That said, returning results with specific framing, messaging, and consistent stylistic choices requires clear input. Consider when you need agents to repeatedly follow processes that are specific to your team and business, and what this might look like in practice. Here are a few common examples:

Overall, any time you find yourself repeatedly giving AI agents the same context or instructions, consider training your agent on a skill.

What’s inside a skill? Key components 

Skills are applicable across workflows so that you don’t need to repeat the same information with every prompt. That said, they don’t automatically apply to every project. Instead, skills help maintain a consistent point of view across your domain, no matter what the task at hand is. For example, a brand voice skill may apply to every workflow while a skill around a specific framework only applies to specific deliverables.

No matter the skill, key components include:

Together, these components help agents produce results that are specific and reliable. Also, while skills are a general concept, not every company may use the same language. Skills may be inherently baked into low- or no-code agent processes so that as you build AI agent workflows, you naturally build its skills.

Agent memories vs. agent skills: What’s the difference?

Contextual memory and agent skills are both ways of giving agents knowledge, although they serve different purposes. Skills are more structured than memories — they're reusable techniques, often with scripts or documentation, that agents can discover and apply.

Ideally, agents apply both memory and skills to ensure their output is as tailored and accurate as possible over time. Skills incorporate broad standards that apply to every employee (i.e., brand guidelines) while specific prompts might apply to a temporary integrated marketing campaign. The agent should take into account and incorporate both.

What makes a good agent skill? 

The most effective skills share a few qualities. They are:

How to improve agent skills over time 

Agent skills compound, building success and accuracy over time. By creating and refining skills early, you create the foundation for consistent and reliable output at scale. Here are three easy steps to get started:

Why your agent skills need a system of record 

Many teams start building skills in isolation. Multiple teams may build a brand voice skill, potentially using different documents or versions. One team may build a research framework skill, while another creates a template for reports. That may seem fine at the individual team level, but it also means that the skills aren’t more broadly accessible, leading to skills that are rebuilt instead of reused. That is, unless you’ve built your workflows on an agent system of record. 

A system of record, like Airtable, creates a central operational surface where anyone on the team can discover and use agent skills. Within a system of record, you might think of a skills library as similar to an engineering codebase: shared, versioned, and observable. This also means that not everyone on the team has permission to edit everything. Instead, clear processes and workflows allow you to assign ownership so that as processes evolve, changes to the skill apply everywhere. And, in the event that an agent produces unexpected output, you can inspect the skill and trace its actions.

Train once, deploy everywhere 

Airtable is designed to provide the operational context and oversight into human-agent collaboration that you need. Both human and AI agents can access the same data and have visibility into workflows, decisions, actions, and results. And, by operationalizing your AI agent skills, you can deploy agents that everyone in your organization can depend upon, while maintaining full control over skill versions, updates, and refinement.

Learn more about how Airtable supports AI workflow management.