Claude Skills vs Subagent: What’s the difference?

General-purpose AI is pretty amazing, let's be honest. But if you're running a business, the real value comes from specialized AI assistants that can handle specific, complex tasks without messing up. It’s the difference between a generalist who can do a bit of everything and a specialist who’s an expert in one crucial area. When it comes to customer support or IT, you want the expert.

Anthropic’s Claude is pushing this forward with two main ways for developers to build these specialized agents: Skills and Subagents. They might sound similar, but they work in completely different ways. Our goal here is to clear up the confusion, compare how they operate in a straight Skills vs Subagent breakdown, and talk about a more accessible way for business teams to get the same results without needing an engineering team on standby.

What are Claude Skills and Subagents?

Before we get into the side-by-side comparison, let's get a handle on what each one actually is. Both are meant to make Claude smarter and more capable, but they tackle the problem from different angles.

What are Claude Skills?

Think of Skills as individual tools you add to Claude's toolkit. They're like little plugins that give the AI a brand-new ability. Each Skill is basically a folder with instructions, scripts, and any other files it needs to get a specific job done.

The most important thing to know about Skills is that Claude decides when to use them. You don't tell it, "Hey, use the PowerPoint Skill now." Instead, Claude looks at your request and figures out on its own which Skill from its library is the right one for the job. For instance, you could have a Skill for creating a PowerPoint that uses your company’s branding. When you ask Claude to "make a presentation about our Q3 results," it finds that Skill and uses it to build the file correctly. They’re also designed to be stackable, so Claude can combine multiple Skills to handle more complicated requests.

What are Claude Subagents?

If Skills are the tools in the toolbox, Subagents are more like the expert specialists you bring in for a specific project. A Subagent is a self-contained AI assistant with its own personality, knowledge, and tools. You could create a "code-reviewer" subagent that’s a pro at finding bugs or a "data-scientist" subagent that’s an expert in SQL queries.

Unlike Skills, you have a bit more control over Subagents. Claude can automatically pass a task to the right one, or a user can specifically call on it. The biggest differentiator, though, is that each subagent works in its own separate context window. This is huge. It means the subagent doesn't get confused by the main conversation history, letting it stay completely focused on its own, often multi-step, task.

Key differences: A Skills vs Subagent breakdown

Alright, now that we know what they are, let's dig into the practical differences. Your choice between them really boils down to how much control you need, how they handle information, and the complexity of the task.

Invocation and control: Automatic vs. explicit

The main split is in how you get them to do work.

Skills are all about automatic discovery. Claude finds and uses them on its own, which is super handy for straightforward tasks. You don't have to memorize a list of tool names; you just say what you want, and the AI handles the rest. The downside? You have less direct control. If Claude doesn't think a Skill fits your request, it just won't use it.

Subagents give you more options. Claude can delegate to them automatically, but you can also explicitly tell it what to do (e.g., > Use the debugger subagent to check this error). This gives you precise control over the workflow, making sure the right "expert" is always on the case.

Context and complexity: Simple add-ons vs. focused experts

How they manage information is another key point of difference.

Skills are meant to be light and efficient. They only pull the necessary info into the main conversation, acting as quick add-ons for single tasks. They’re there to enhance the current chat, not to start a whole new one.

Subagents get their power from their isolated context window. For complicated, multi-step jobs like debugging a piece of code or analyzing a big dataset, this separation is a lifesaver. The subagent can go through its entire process without getting sidetracked by the main chat, ensuring it stays focused and accurate from beginning to end.

A side-by-side comparison

Here’s a quick table to wrap up the core differences in the Skills vs Subagent debate.

Feature	Claude Skills	Claude Subagents
Primary Concept	Modular, reusable capabilities (a toolbox)	Specialized AI personalities (an expert team)
Invocation	Automatic (model-invoked)	Automatic delegation or explicit user command
Context Window	Works within the main conversation	Operates in a separate, isolated context
Best For	Single, well-defined tasks (e.g., file conversion)	Complex, multi-step workflows (e.g., debugging)
Configuration	A folder with a SKILL.md file and resources	A single Markdown file with YAML frontmatter
Control Level	Lower (relies on AI's own own discovery)	Higher (can be explicitly commanded)

The challenge: Making specialized AI work for business teams

Both Skills and Subagents are powerful, but they have one thing in common: they were built for developers.

For non-technical teams in support, IT, or operations, this creates some real roadblocks. First off, you need to be comfortable with developer tools. Setting up a Skill or a Subagent involves using the command line, editing configuration files, and managing folder structures. It's not exactly something a support manager can whip up between meetings.

Second, these features are locked into the Claude ecosystem. If your team spends its day in tools like Zendesk, Slack, or Confluence, you can't just flip a switch and have a Subagent start handling tickets. You'd need a costly and time-consuming custom development project to get these AI capabilities into your daily workflows.

eesel AI: Specialized agents without the engineering team

This is where platforms built for business users come into play. A tool like eesel AI gives you the power of both Skills and Subagents, but through a simple, no-code interface that anyone on the team can figure out. It’s made to connect directly to the software you already use, letting you build specialized AI assistants in minutes.

Build specialized "subagents" with custom knowledge

With eesel AI, you can create distinct AI bots that work a lot like Subagents. But instead of writing code, you define each bot’s personality and instructions in a simple text editor.

Even better, you can decide exactly what it knows with just a few clicks. Need an AI that only answers questions about your billing policies? Just connect it to that specific part of your Confluence or a couple of key Google Docs. This keeps your AI bot on-topic and prevents it from giving answers about things it shouldn't, putting you in complete control.

A screenshot showing how eesel AI connects to various knowledge sources to build a specialized subagent, simplifying the Skills vs Subagent choice for businesses.

Deploy pre-built "skills" with one-click AI Actions

eesel AI's "AI Actions" are the business-friendly version of Skills. They are powerful, pre-built abilities you can turn on with a click. Instead of writing scripts, a support manager can give their AI the power to do things like "Tag Ticket," "Escalate to Tier 2," or "Look up Order in Shopify."

These actions are ready to use out of the box and connect to the tools your team uses every day, whether it's a help desk like Zendesk or an IT tool like Jira Service Management.

Test and deploy with confidence

Maybe the biggest win for business teams is being able to test everything without any risk. Claude's developer tools are impressive, but they don't offer an easy way for a business user to check an agent's work before it's live.

eesel AI has a simulation mode that lets you test your new AI on thousands of your past support tickets in a safe environment. You can see exactly how it would have replied, get solid forecasts on its resolution rates, and tweak its behavior before it ever talks to a real customer. This takes the guesswork out of the equation and lets your team deploy new AI features with confidence, which is a must-have in a support or IT setting.

The eesel AI simulation mode, where users can test AI performance, a key advantage when considering alternatives to the developer-focused Skills vs Subagent options.

The right tool for the job

When you look at the Skills vs Subagent comparison, Skills are modular tools for specific, one-off tasks, while Subagents are focused experts designed for complex workflows. Both are fantastic building blocks for developers working with Claude.

But for business teams who need to solve problems right now, these developer-focused tools are often more trouble than they're worth. They require technical know-how to set up and a serious effort to integrate with your existing software.

eesel AI helps bridge that gap. It offers the power of specialized, autonomous AI agents through a simple, self-serve platform that connects to the tools you already have. You get the expert knowledge of a Subagent and the practical actions of Skills, all wrapped in an interface that lets you go from an idea to a live AI assistant in a matter of minutes.