How agent skills work

AI coding assistants are useful out of the box, but they get significantly more powerful when you can teach them specific workflows. That is what agent skills do. They are reusable instruction sets that guide an AI assistant through a particular type of task.

What is a skill?

A skill is essentially a structured prompt that tells the AI assistant how to approach a specific kind of work. Instead of explaining your preferred debugging process every time something breaks, you define a skill once, and the assistant follows that process automatically.

For example, a debugging skill might instruct the assistant to: reproduce the issue first, check recent changes, form a hypothesis before touching code, verify the fix, and confirm no regressions. Without the skill, the assistant might just jump straight to suggesting code changes.

Why not just use custom instructions?

Custom instructions are static. They apply to every conversation equally. Skills are contextual. They activate when the task calls for them and stay out of the way otherwise. You might have a skill for test-driven development, another for code review, another for writing documentation, and they only engage when that type of work comes up.

Skills can also be more detailed than custom instructions typically are. They can define multi-step workflows, specify the order of operations, include checklists, and enforce patterns that you want followed consistently.

Practical examples

A TDD skill might enforce: write the failing test first, run it to confirm it fails, write the minimal implementation, run the test again, then refactor. The assistant follows this cycle instead of writing test and implementation together.

A code review skill might instruct the assistant to check for security issues first, then logic errors, then style consistency, and only report issues above a certain confidence threshold.

A brainstorming skill might require the assistant to ask clarifying questions one at a time, explore multiple approaches before picking one, and get explicit approval before writing any code.

How they work in practice

In tools that support skills, they are typically stored as files alongside your project. When a relevant task comes up, the skill loads and the assistant follows its instructions. Some skill systems are automatic (the assistant detects when a skill applies), while others are invoked explicitly.

The key insight is that skills encode your team's best practices into something the AI can follow consistently. Instead of the assistant making ad-hoc decisions about process, it follows the same disciplined approach your best engineers would.

Building your own

If your AI tool supports skills, start by documenting one workflow you repeat often. Maybe it is how you approach bug fixes, or how you structure new features, or how you review pull requests. Write it as a clear, step-by-step process, and you have your first skill.

The best skills are opinionated and specific. "Write good code" is not a skill. "When fixing a bug, always reproduce it first, check git blame for recent changes, and write a regression test before the fix" is a skill.

Sources

• Model Context Protocol
• Anthropic Documentation