What Are Agent Skills Besides Claude?

Agent Skills by Anthropic on Oct 16, 2025, as a way to extend Claude with reusable skills. Within a few months, this concept gained traction throughout the AI ​​community and began to evolve into a broader design pattern for building modular, portable agent capabilities beyond Claude himself.

As an AI professional, I have been using Claude's Code for a long time and have seen many tutorials explaining how to build Agent Skills within the Claude ecosystem. However, when I tried to implement the same concept for our business solution without relying on Claude, I quickly faced a different set of design questions. What exactly defines “agent competence”? How should it be built? And how should capabilities be implemented and configured in a custom agent framework?

For Claude, a typical Agent Skill is defined as a Markdown file containing a name, description, instructions, and documentation. This natural language interaction works well in many situations, especially when ambiguity is acceptable. But in manufacturing systems, accuracy is often more important than flexibility. Another constant challenge I faced was evoking a skill: sometimes a skill could be powerful and well designed, but Claude couldn't call it because the meaning was so vague.

For our agents, I need strong credentials—skills that start with confidence and behave consistently every time. That requirement led me to apply the skills directly to Python in the abstract sense. But doing so raises an interesting architectural question: if a skill is only implemented in code, how is it different from a tool, a feature, or just another function? Even though these skills are reusable and used when needed, what do they actually do agent skills?

This article explores these questions and shares a practical idea for designing and implementing agent capabilities in custom AI agents without relying on Claude.

Agent Skills vs Tools vs Features

A tool a primitive ability, or a single action an agent can take. Each tool does one specific thing. Tools are individual tools, such as hammers, saws, and drills.

• Example: Claude's tools include things like bash_tool (use the command), web_search (search the internet), view (read file), str_replace (edit file), web_fetch (find web page), places_search, weather_fetch and so on.

Ability is a set of instructions on how to organize multiple tools to accomplish a complex task efficiently. The skill doesn't give me new skills. It provides agents with expertise in integrating existing tools effectively. Skills are like a detailed recipe that tells agents which tools to use, in which order, and which mistakes to avoid.

• Example: The docx skill, for example, tells Claude to use it bash_tool they will run npm install docxthen write JavaScript using certain patterns, and run bash_tool and to verify the output, then use present_files to share it.

A feature product-level concept, or something the user sees and can change on or off. The feature is enabled by giving the agent access to certain tools and capabilities.

• Examples: “Code Execution and File Creation,” “Web Search,” and “Artifacts” features. So “create file” as a feature is enabled by the bash tool, the create file tool, the various documentation capabilities, and the current_files tool, all working together.

Skills are what bridge the gap between access to raw tools and high quality output. Without the docx capability, Claude can still technically create a Word document, but may miss things like “always set the page size clearly because docx-js defaults to A4” or “never use unicode characters.”

Should agent capabilities be in markup files format?

That's not the case. The concept of agent skills is broader than format.

Skill is fundamental integrated technology to do the job well. That technology can take many forms:

• Markup file with instructions (Claude Agent ability)
• A Python script that does the job directly
• A configuration file or JSON schema
• Example set of inputs and outputs
• A combination of all of the above

In my case, the A Python script it seems appropriate because I need a fixed and reliable execution every time without variation. It is fast, cheap, and predictable in process. I marking command methodology becomes valuable when the work involves ambiguity or judgment. Sometimes, we need LLM study instructions and advice on what to do next to add value to a solid script.

A a mixture the method is also familiar. I can keep my Python code and reduce it to tool implementation, but I add a capability markup file that helps agents understand when to use my agent capability and how to interpret the results. A typical product iteration might start with the use of Python and gradually include Markdown instructions, eventually building a hybrid design.

Agent skill vs MCP? Agent Skill + MCP!

Our agents already connect to data sources through MCP servers, but many of our agent capabilities include the ability to read directly from the database. This raises a practical architectural question: when should an agent use an MCP, and when should a skill stay within a skill?

Anthropic explains the difference clearly with a helpful kitchen analogy:

MCP connects Claude to data; Skills teach Claude what to do with that data.

• MCP provides a professional kitchen – access to tools, ingredients, and equipment.
• Capabilities provide recipes – instructions that tell the agent how to use those resources to produce something valuable.

With my design, I manage MCP communication as an infrastructure again capabilities such as orchestration of how data is used.

MCP servers are responsible for exposing external data sources and services. For example, they may provide structured access to databases, logs, APIs, or internal systems. Their role is to make these services available to the agent in a standardized manner.

Agent capabilities, on the other hand, describe how the agent must use data from MCP servers and other databases to accomplish a task.

For this reason, I often use:

• Database access, APIs, and data retrieval as tools (or MCP capabilities)
• Decision logic and workflow as agent capabilities

Skills as “Agentic RAG”

Ideally, agent capabilities dynamically load information from tools when executed. This makes the pattern very similar to agent-augmented generation (RAG).

Instead of preloading all the context in the notification, the ability can:

1. Identify what information you need
2. Retrieve the relevant data through the MCP tool or server
3. Process that data according to its instructions
4. Generate the final output

This approach keeps agents lightweight while still allowing the ability to access large or changing datasets when needed.

The conclusion

Anthropic introduced an important paradigm shift, and Claude's use of Agent Skills provides important inspiration for building our agent systems. As long as our capabilities capture the core purpose of what the capability implies, including the reusable capabilities used by the agent, the specific format and details of use may vary. Ultimately, design decisions about when and how capabilities will be used should be guided by the needs and constraints of the product we are building.

Thanks for reading! I hope this was helpful to you.