Advanced AI Agent Skills: Behind the Scenes and a Developer’s Guide

Introduction

After earlier tutorials that introduced what AI Agent Skills are and how to use them in tools like Cursor, this article dives into the "core backend" of Skills, explains how they originated, and shows front‑end developers how to understand, learn, and master them.

1. Who invented Skills?

Skills are not the invention of a single person; they are an inevitable product of AI progress.

Key milestones

ReAct pattern (2022) : Early researchers at Google Research and Princeton discovered that letting an AI "Reason, then Act" dramatically improves capability. This became the prototype of Skills, enabling purposeful use of external information.

OpenAI Function Calling (2023) : OpenAI added Function Calling to the GPT API, turning Skills into an industry standard by allowing the model to output structured JSON to invoke functions instead of plain text.

Current models (Gemini, Claude) : Leading large models such as Google Gemini and Anthropic Claude now natively support this ability, commonly called "Tool Use".

2. How Skills are used in other large models

Although each provider names the feature differently (GPT calls it Function Calling, Gemini calls it Tool Use), the core principle is identical.

The standard loop (The Loop)

User query : "How's the weather in Hangzhou today?"

Model reasoning : The model realizes it lacks real‑time weather data but remembers a tool named get_weather.

Model request : Instead of answering directly, it outputs a JSON request:

{"tool":"get_weather","args":{"city":"Hangzhou"}}

System execution : Your backend (Python/Node.js) intercepts the request, calls a real weather API, and receives "25°C, sunny".

Model answer : The result is fed back to the model, which replies "Hangzhou's weather is nice, 25°C and sunny."

Conclusion: In any model that supports Tool Use, you can configure API parameters to turn your functions into AI Skills.

3. Hands‑on: Customizing Your Skills

Using the OpenAI API format (the de‑facto industry standard), you pass both the prompt and the potential tools to the model.

Step 1 – Tell the model (The Menu)

Define each tool as a JSON description. Example for a search_employee tool:

{
  "messages": [{"role":"user","content":"查一下张三"}],
  "tools": [
    {
      "type":"function",
      "function": {
        "name":"search_employee",
        "description":"查询员工信息...",
        "parameters": { ... }
      }
    }
  ]
}

Step 2 – Model orders (The Order)

The model does not execute code; it merely "orders" a tool. If it decides to use search_employee with argument "张三", it returns a JSON request. No code runs at this point.

Step 3 – Code mapping and execution (The Kitchen)

The backend code maps the tool name (a string) to a real function and runs it. Node.js example:

// 1. Define the real work function
async function realSearchFunction(name) {
  return await db.query(`SELECT * FROM users WHERE name = '${name}'`);
}
// 2. Build a mapping table
const availableTools = {
  "search_employee": realSearchFunction, // ← key link
  "get_weather": realWeatherFunction
};
// 3. Handle the model's response
const response = await callAIModel(...);
const toolCall = response.choices[0].message.tool_calls[0];
if (toolCall) {
  const functionName = toolCall.function.name; // e.g., "search_employee"
  const targetFunction = availableTools[functionName];
  const result = await targetFunction(toolCall.function.arguments);
}

Summary of the mapping:

In the model’s view, search_employee and get_weather are just string names.

In your code, those strings map to real functions (local or HTTP calls).

The bridge is the availableTools mapping logic you write.

4. Advanced topic: Skills vs. MCP (Model Context Protocol)

With AI’s rapid evolution, a new term – MCP – has emerged. MCP is a protocol standard (similar to USB) that defines how AI connects to data and tools.

Core differences

Skills (Function Calling) : Capability on the model side; the model learns to call external functions.

MCP : Standard on the connection side; an open protocol that normalizes AI‑tool interaction across models.

Analogy

Skills : Like a proprietary charger – each AI platform needs its own definition.

MCP : Like a USB‑C interface – develop once, run anywhere.

Comparison

Development effort : Skills – repeated work for each platform; MCP – write once, run anywhere.

Flexibility : Skills – high (fully custom logic); MCP – medium (must follow protocol).

Ecosystem : Skills – isolated plugins per vendor; MCP – shared community servers.

Suitable scenarios : Skills – simple, ad‑hoc scripts; MCP – complex, reusable system‑level integrations.

Choosing which to use:

If you just want to add AI capability to a small script, Skills (Function Calling) is the fastest.

If you are a tool developer or building an enterprise‑grade AI knowledge base, MCP is the future direction.

5. Summary: From User to Creator

Ordinary users : Only need natural‑language commands.

Developers : Must write good prompts, define schemas, and implement code that wraps traditional APIs into AI‑readable Skills.

Architects : Should also consider MCP to build reusable, standardized AI ecosystems.

Mastering Skills gives you the ability to embed AI into any real‑world system – robots, databases, automated emails, and more. Future articles will detail how to unlock this AI gateway and share your own Skills.