What Language Are Agent Skills Written In? A Detailed Guide to Language Choices for the Description and Execution Layers

The Language Question Behind Agent Skills

With the rapid advancement of AI Agent technology, "Agent Skills" have become a core component in building autonomous AI systems. A common question in the developer community is: What language are Agent Skills actually written in? This seemingly simple question touches on a key issue in AI Agent architecture design — the paradigm of skill definition and execution.

This article takes a deep dive into the language choices for Agent Skills, the design philosophy behind them, and the trade-offs between different implementation approaches.

What Are Agent Skills?

Before diving into the language question, we need to clarify what Agent Skills are. Agent Skills are functional modules that give an AI Agent the ability to perform specific tasks, including calling external APIs, executing code, querying databases, and handling more complex multi-step workflows.

To understand where skills fit within an Agent architecture, it helps to first understand the dominant Agent runtime paradigm. Most modern AI Agents use variants of the ReAct (Reasoning + Acting) framework: the large language model first performs reasoning, decides what action to take next, observes the result of that action, and then enters the next round of reasoning. In this loop, Agent Skills are the concrete vehicles for the "Acting" step — they are the Agent's "hands and feet" for interacting with the outside world. From a software engineering perspective, Agent Skills play a role similar to plugin systems or microservices in traditional applications: they encapsulate specific capabilities in a modular fashion, interact with the main system through standardized interfaces, and support dynamic loading and compositional invocation. But unlike traditional plugins, the caller of Agent Skills (the LLM) doesn't select and invoke skills through hardcoded logic — instead, it makes dynamic decisions based on semantic understanding. This requires skill definitions to be understandable to both humans and AI.

From an architectural standpoint, an Agent Skill typically consists of two layers:

• The description/definition layer: Tells the LLM what this skill can do, what parameters it needs, and when to invoke it.
• The execution layer: The code that actually implements the functional logic.

These two layers use different "languages" for expression, which is exactly why the question "What language are Agent Skills written in?" deserves a thorough discussion.

The Description Layer: Natural Language and Structured Configuration

Defining Skills with Natural Language Prompts

In many modern Agent frameworks, the description layer relies heavily on natural language. Developers use natural language to tell the LLM what a tool does — for example, "This function queries the weather; input a city name and it returns temperature information." This approach leverages the LLM's natural language comprehension capabilities and lowers the barrier for skill definition.

The mechanism behind this is closely related to Prompt Engineering. In practice, a skill's natural language description is typically injected into the System Prompt, combined with the Agent's role definition, behavioral rules, and other context to form a complete prompt. During each reasoning step, the LLM "reads" the descriptions of all available skills, understands their functional boundaries and applicable scenarios, and then decides whether to invoke a skill and how to construct the call parameters. Consequently, the quality of skill descriptions directly affects the accuracy of Agent behavior — a vague description might cause the model to invoke a tool in the wrong scenario or pass inappropriate parameters. This is why more and more frameworks are emphasizing the philosophy of "tool description as documentation," requiring developers to write skill descriptions with the same care they'd put into high-quality API documentation.

Structured Definition with JSON Schema

To make skill invocation more reliable, the industry widely adopts structured formats like JSON Schema to define tool interfaces. OpenAI's Function Calling, Anthropic's Tool Use, and the MCP (Model Context Protocol) all use JSON as the standard carrier for skill descriptions.

JSON Schema itself is a specification standard for describing JSON data structures (an IETF draft standard), originally used widely in API validation and form validation scenarios. It can precisely define data types, formats, required fields, value ranges, and other constraints — exactly the kind of precision that Agent skill invocation demands. In terms of technical evolution, different platforms have taken different paths: OpenAI pioneered Function Calling in June 2023, allowing developers to define function signatures via JSON Schema, with the model outputting structured function call requests rather than plain text — this was seen as a milestone event in Agent skill invocation; Anthropic subsequently launched Tool Use, adopting a similar but more flexible JSON definition approach with improvements in error handling and multi-tool coordination; MCP (Model Context Protocol) is an open standard protocol proposed by Anthropic in late 2024 that defines not only the specification for Tools but also covers Resources and Prompts standardization, aiming to establish a universal interoperability standard akin to "USB-C for the AI world," enabling different Agent frameworks and LLM providers to share a common skill ecosystem.

The core idea behind this design is: use a structured format understandable to both humans and machines to clearly define the input/output contract of a skill, reducing ambiguity during LLM invocation.

The Execution Layer: Choosing a Mainstream Programming Language

When a skill is actually invoked and executed, the code running behind it is written in traditional programming languages. Here are the current mainstream choices:

Python: The Top Choice for the Agent Skills Execution Layer

Python is the dominant language for the Agent Skills execution layer, for several reasons:

• It has the richest AI/ML ecosystem; virtually all major Agent frameworks (such as LangChain, AutoGPT, and CrewAI) use Python as their core language.
• Its concise syntax is ideal for rapid iteration and prototyping.
• A massive collection of third-party libraries makes it easy for skills to integrate with a wide range of external services.

Python's dominance in AI has deep historical roots. Even before the rise of deep learning, Python had become the language of choice for data science thanks to scientific computing libraries like NumPy and SciPy. Then frameworks like TensorFlow and PyTorch chose Python as their primary interface language, further solidifying this ecosystem advantage. By the Agent era, this first-mover advantage had generated enormous network effects. Looking at specific major frameworks: LangChain was one of the first to abstract "chain calling" (Chain) and "tool use" (Tool Use), providing conveniences like the @tool decorator that let developers turn an ordinary function into an Agent-callable skill with just a few lines of Python; AutoGPT was the pioneering project that ignited the autonomous Agent concept in 2023, demonstrating the possibility of Agents autonomously planning, executing, and iterating, with its skill system built entirely on Python modules; CrewAI represents the multi-Agent collaboration paradigm, allowing developers to define multiple Agents with different roles and skill sets that work together as a team to accomplish complex tasks, with each Agent's skills implemented as Python functions.

TypeScript: A Strong Choice for Full-Stack Scenarios

In web and full-stack application scenarios, TypeScript is becoming an important choice. Frameworks like the Vercel AI SDK and LangChain.js enable developers to build Agent Skills in the Node.js environment, making it especially suitable for applications requiring deep frontend integration.

TypeScript's rise in Agent development is closely tied to the evolution of web platform computing power. The maturation of Edge Runtime and Serverless deployment models allows JavaScript/TypeScript code to run on globally distributed edge nodes with extremely low latency — a natural advantage for AI applications requiring real-time responses (such as chatbots and real-time assistants). The Vercel AI SDK leverages this architectural advantage, letting developers deploy Agent Skills as Edge Functions with millisecond-level cold start times. Additionally, TypeScript's strong type system provides extra safety in skill definitions — when a skill's inputs and outputs are defined through the TypeScript type system, many parameter errors can be caught at compile time rather than only surfacing at runtime. For teams that have already invested heavily in web frontend, writing Agent Skills in TypeScript enables full unification of the frontend and backend tech stack, reducing context switching and team collaboration costs.

Go and Rust: Exploring High-Performance Scenarios

Some developers use Go, Rust, and other languages to build high-performance execution layers for skills, especially in production environments with strict requirements for latency and concurrency, where the performance advantages of these languages are more pronounced.

An Emerging Paradigm: Code as Skills

A noteworthy new trend has recently emerged — having LLMs directly generate and execute code as skills. Rather than predefining a large number of fixed tools, the model writes Python code on-the-fly based on task requirements and runs it in a sandbox.

This "Code as Skills" approach treats the programming language itself as the Agent's universal action interface. Its advantage lies in extreme flexibility — theoretically, any task that can be expressed in code can be completed without the need to wrap a separate tool for each scenario.

This paradigm has been validated in practice across multiple projects. OpenAI's Code Interpreter (now integrated as the Advanced Data Analysis feature) is the most well-known example — it allows ChatGPT to dynamically write and execute Python code for data analysis, plotting, file processing, and other tasks, with no tools needing to be defined in advance; the model programs on its own based on requirements. In academic research, Voyager (a Minecraft AI Agent released by NVIDIA and other institutions) demonstrated an even more radical Code as Skills approach: the Agent not only generates code at runtime but also saves successfully executed code snippets as a reusable skill library, achieving "self-accumulation and evolution of skills."

However, this also introduces challenges around security and controllability, so it typically requires strict sandbox isolation mechanisms to ensure system safety. Current mainstream sandbox solutions include: Docker container isolation, which creates an independent container environment for each code execution, restricting network access and filesystem permissions; WebAssembly (Wasm) sandboxes, which provide near-native performance in a secure execution environment on the browser or server side; kernel-level sandboxes like gVisor, which offer more fine-grained security control by intercepting system calls; and solutions like Pyodide that compile the Python interpreter to WebAssembly, letting Python code run in a fully isolated environment. Each approach involves different trade-offs between security, performance, and compatibility.

Summary: Language Choices for Agent Skills Are Layered and Diverse

Returning to the original question — What language are Agent Skills written in? The answer needs to be examined layer by layer:

Layer	Mainstream Language/Format	Core Concern
Description Layer	Natural Language + JSON Schema	Readability and contract clarity
Execution Layer	Python (mainstream), TypeScript, Go/Rust	Functionality and performance
Emerging Paradigm	Dynamic code generation (primarily Python)	Flexibility and universality

For developers, the choice of language and paradigm depends on the specific use case, performance requirements, and the team's tech stack. Understanding this layered structure helps in designing and building reliable AI Agent systems more clearly.

As standardized protocols like MCP gain wider adoption, the way Agent Skills are written is moving toward standardization, with the prospect of more unified skill definition and execution standards in the future.