Agent Skill 和 mcp 和 prompt区别 (English)

Generated: 2026-06-24 17:57:25

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Can You Really Tell Agent, Skill, MCP, and Prompt Apart? After Reading This, I Bet 90% of People Get Them Mixed Up

A couple of days ago, a reader frantically messaged me: "My brain is about to explode! Agent, MCP, Skill, Prompt… they all sound like things that make AI do stuff, but what's the actual difference?"

I replied: "Take it easy. I fell into that same trap two years ago—multiple times, and every time I landed flat on my back."

Honestly, I've been tinkering with AI applications since 2017. Back then, there was no Agent, no MCP. The most advanced technique we had was—"writing prompts." If you wrote a good one, AI was a genius. If you wrote a bad one, AI turned into a disaster. You gave it a sentence, it did one thing, then forgot everything after the conversation ended.

But now, suddenly, a bunch of new stuff has appeared. Agent, Skill, MCP—each one claims to "make AI smarter." Yet if you ask ten engineers how they actually work together, nine of them won't be able to explain it clearly.

So today, I'm going to spill all the hard-earned practical experience I've gathered and break these things down so thoroughly that you'll have an "aha" moment by the end. And you'll even be able to show off to your colleagues.

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Step 1: Prompt—Every Word You Say to AI, It Forgets Right After

Let's start with the most familiar one.

What is a Prompt? It's the sentence you send to AI: "Summarize this PDF for me," "Write a resignation letter—make it emotional," "Analyze the root cause of this bug."

That's it.

It has no memory, no workflow, and it won't proactively call tools. You tell it "review this code," and it just stares at those few lines of text. It won't go look at your Git history, and it won't run unit tests on its own. Why? Because you didn't tell it to!

So a used prompt is like yelling "Hey boss, a bowl of noodles!" at a restaurant—once the order is placed, its job is done.

Yet even now, some people still believe Prompts are the ultimate solution. They spend all day studying "Prompt Engineering" and "spell books of incantations." Let me tell you, that mindset is stuck in 2018.

A Prompt only solves one thing: what to do this time. But think about it—any slightly complex task can't be handled in a single exchange. For example: "Analyze this week's user feedback, find the top 3 issues, and write an improvement plan." With just one prompt, AI has no idea what the first step is, let alone the second.

So you see, a Prompt is just the starting point, not the end. If you want it to really "work," you need to add something more.

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Step 2: MCP—Giving AI a Standardized Pair of Hands, No More Messy Tool Connections

By the end of 2024, everyone discovered a huge problem: every time you wanted AI to do something practical (query a database, send an email, operate a browser), you had to write custom code to call the API. Calling A required one approach, calling B required another. You'd be reinventing the wheel until you felt like throwing up.

That's when MCP (Model Context Protocol) came out. It solves the standardization of tool integration.

Imagine: without MCP, every tool's interface is like a different country's power socket. AI says "I want to look up the database," and you have to write a special adapter for it. It says "send a Feishu message," and you have to write another one. You repeat this for every project—annoying, right?

With MCP, all tools become a unified interface—write once, reuse everywhere. It's like a universal travel adapter that works wherever you go.

When I built my first MCP Server (I chose a filesystem tool), I hit a massive pitfall: I copied the JSON-RPC format from the official documentation, but Claude Desktop refused to accept it. I spent an entire afternoon debugging, only to find out that I had missed a sessionId field! Just one field made me question life for four hours. Later, as the MCP spec stabilized, those kinds of headaches became rare.

Now you understand: MCP is a handshake protocol between the AI client (like Claude or Cursor) and the tool service (database, Git, browser) . It uniformly exposes the capabilities of tools and converts them into a Function Calling format that the large model can understand, letting AI know: "Oh, so I can do this."

In a word: MCP answers the question—can you do this? Can you query the database? Can you send emails? Can you control the browser? It all depends on how many MCP tools you've connected.

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Step 3: Skill—Packaging Your Repetitive Work into "Muscle Memory"

Alright, here comes the key part. This is where most confusion happens.

Many people see Agent Skills and the first thought is: "Isn't this just putting a few prompts into a folder? Just a different name?"

Let me tell you: that idea is dead wrong. Some Skills are indeed just folders of long prompts, but a truly powerful Skill is not just about writing a few lines of instructions.

It doesn't tell AI "think carefully" or "pay attention to formatting"—those are empty words that make no difference. A real Skill captures the complete workflow of a class of tasks from start to finish: under what conditions it triggers, what steps to follow, what context to bring in, which MCP tools can be invoked, and finally how to decide if the result is acceptable.

Let me show you with a "Competitor Analysis Skill" I wrote:

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name: competitor-analysis
description: "Use when the user needs to analyze the competitive landscape of an industry, compare multiple brands on specific dimensions, or generate a competitive analysis report."
metadata: { "openclaw": { "emoji": "📊" } }
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# Competitor Analysis Skill

1. Use web_search to search for "[industry name] major brands", extract the top 5-10 brands
2. For each competitor, use web_search to search for data on each dimension
3. Organize all data into a comparison table
4. Combine the comparison table and key findings, output the report

See? No fluff. Just cold, hard steps. But when AI reads this, it no longer has to figure out from scratch "how should I analyze competitors." Instead, it gets the instruction and follows the process directly.

So, Prompt is "what to do this time," Skill is "how to do this type of task." The difference is like deciding on the spot what to eat (ordering takeout today) versus having your mom's treasured recipe book—she follows the steps every time.

Skills have a lifecycle: when the Agent starts, it scans all available Skills, loads the one that matches the task, and unloads it after use. Not like Prompts, which are stuffed into every conversation, wasting tokens and polluting context.

What's a huge pitfall I fell into? When I first started writing Skills, I made the description in the metadata too vague: "Analyze industry information." Then a user said, "Tell me what trends are happening in the smartphone market this year," and my Competitor Skill got triggered—it output a bunch of brand comparisons, not the trend analysis the user wanted. The hit rate was under 50%!

Later I changed the description to: "Use when