What Are Agent Skills Beyond Claude?

Agent Skills  by Anthropic on Oct 16, 2025, as a method to extend Claude with reusable capabilities. Inside months, the concept gained traction across the AI community and started evolving right into a broader design pattern for constructing modular, portable agent capabilities beyond Claude itself.

As an AI practitioner, I’ve been using Claude Code for quite a while and have seen many tutorials explaining the way to create Agent Skills throughout the Claude ecosystem. Nevertheless, after I tried to implement the identical concept for our enterprise solution without counting on Claude, I quickly bumped into a special set of design questions. What exactly defines an “agent skill”? How should it’s structured? And the way should skills be triggered and orchestrated in a custom agent framework?

In Claude, a typical Agent Skill is defined as a Markdown file containing a reputation, description, instructions, and scripts. This natural-language interface works well in lots of situations, especially when ambiguity is suitable. But in production systems, precision often matters greater than flexibility. One recurring challenge I encountered was skill triggering: sometimes a skill could be powerful and well-designed, yet Claude would fail to invoke it because the outline was too vague.

For our agents, I would like stricter guarantees—skills that trigger deterministically and behave consistently each time. That requirement led me to implement skills directly in Python with explicit logic. But doing so raised an interesting architectural query: if a skill is implemented purely in code, how is it different from a tool, a feature, or simply one other function? Even when these skills are reusable and invoked on demand, what actually makes them ?

This text explores these questions and shares a practical perspective on designing and implementing agent skills for custom AI agents without counting on Claude.

Agent skills vs Tools vs Features

A tool is a primitive capability, or a single motion the agent can take. Each tool does one specific thing. Tools are individual instruments, like hammers, saws, and drills.

• Example: Claude’s tools include things like bash_tool (run a command), web_search (search the web), view (read a file), str_replace (edit a file), web_fetch (get a webpage), places_search, weather_fetch and so forth.

A skill is a set of instructions for the way to orchestrate multiple tools to perform a fancy task well. A skill doesn’t give me latest capabilities. It gives agents expertise in combining existing tools effectively. Skills are like an in depth recipe that tells the agents which instruments to make use of, in what order, and what mistakes to avoid.

• Example: The docx skill, for instance, tells Claude to make use of bash_tool to run npm install docx, then write JavaScript using specific patterns, then run bash_tool again to validate the output, then use present_files to share it.

A feature is a product-level concept, or something the user sees and may toggle on or off. A feature is enabled by giving the agent access to certain tools and skills.

• Examples: “Code Execution and File Creation,” “Web Search,” and “Artifacts” are features. So “file creation” as a feature is powered by the bash tool, the file creation tool, various document skills, and the present_files tool, all working together.

Skills are what bridge the gap between raw tool access and high-quality output. Without the docx skill, Claude could still technically create a Word document, but it surely might miss things like “all the time set page size explicitly because docx-js defaults to A4” or “never use unicode bullets.”

Do agent skills should be within the format of markdown files?

Not necessarily. The concept of agent skills is broader than the format.

A skill is fundamentally codified expertise for the way to accomplish a task well. That expertise could live in lots of forms:

• A markdown file with instructions (typical Claude Agent Skill)
• A Python script that does the work directly
• A config file or JSON schema
• A set of example inputs and outputs
• A mixture of the entire above

In my case, the Python script seems appropriate because I would like deterministic and reliable execution each time with no variation. It’s faster, cheaper, and more predictable for a procedural process. The markdown instruction approach becomes useful when the duty involves ambiguity or judgment. Sometimes, we want LLM reading instructions and reasoning about what to do next so as to add value over a rigid script.

A hybrid approach can be common. I can keep my Python code and downgrade it to a tool implementation, but add a markdown skill file that helps the agents understand when to invoke my agent skill and the way to interpret the outcomes. A typical product iteration might begin with a Python implementation and progressively incorporate Markdown instructions, eventually forming a hybrid skill design.

Agent skill vs MCP? Agent Skill + MCP!

Our agents already hook up with data sources through MCP servers, but a lot of our agent skills also include the power to read directly from databases. This raises a practical architectural query: when should an agent use MCP, and when should the aptitude live inside a skill?

Anthropic explains the excellence clearly with a helpful kitchen analogy:

• MCP provides the skilled kitchen – access to tools, ingredients, and equipment.
• Skills provide the recipes – instructions that tell the agent the way to use those resources to supply something useful.

In my design, I treat MCP connections as infrastructure and skills as orchestration on how the information is used.

MCP servers are answerable for exposing external data sources and services. For instance, they could provide structured access to databases, logs, APIs, or internal systems. Their role is to make these resources available to the agent in a standardized way.

Agent skills, however, define how the agent should use the information from MCP servers and other databases to perform a task.

Because of this, I typically implement:

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

Skills as “Agentic RAG”

Ideally, the agent skills load information dynamically from tools once they are executed. This makes the pattern very much like agentic retrieval-augmented generation (RAG).

As an alternative of preloading all context into the prompt, the skill can:

1. Discover what information it needs
2. Retrieve the relevant data through a tool or MCP server
3. Process that data in response to its instructions
4. Produce the ultimate output

This approach keeps the agents lightweight while still allowing skills to access large or changing datasets on demand.

Conclusion

Anthropic introduced a vital paradigm shift, and Claude’s implementation of Agent Skills offers useful inspiration for constructing our own agent systems. So long as our skills capture the core purpose of what a skill is supposed to do, which is encapsulating reusable on-demand capabilities for an agent, the particular format and implementation details can vary. Ultimately, the design decisions around when and the way to use skills ought to be guided by the needs and constraints of the product we’re constructing.

Thanks for reading! I hope this has been helpful to you.