Why the Future of AI Isn’t Custom Agents — It’s Skills

A shift from fragmented AI agents to reusable, modular expertise

Many companies today are building AI agents the wrong way. Every new task gets its own agent — a financial agent, a coding agent, a marketing agent, a sales agent… and so on.

The problem?
This approach doesn’t scale, and it wastes the capabilities of modern general models like Claude.

Recently, I came across a talk that reframed the entire architecture:
Instead of creating new agents for every use case, we should treat code as a universal interface and start building “Agent Skills” — modular, file-based packages of expertise that can be loaded into any general-purpose agent.

These Skills give the model procedural, domain-specific knowledge without forcing us to build a new agent from scratch.

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🧠 1. The Core Problem: Intelligence vs. Expertise

The speakers described the gap perfectly:

• Today’s models have high general intelligence.
• But they lack consistent, repeatable domain expertise.

They compared this to choosing between:

• a “mathematical genius” (high reasoning power)
• a “tax professional” (procedural mastery)

A genius might solve a novel problem,
but a tax professional will do the job correctly every single time.

So instead of building:

• a Financial Agent
• a Coding Agent
• a Research Agent

…we should build Skills that add reusable expertise to one universal agent.

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🧩 2. What Exactly Is a “Skill”?

A Skill is surprisingly simple:

A Skill is just an organized folder containing reusable procedural knowledge.

That’s it.
No exotic abstraction.
No proprietary logic.

A Skill folder can be:

• version-controlled (Git)
• synced in cloud storage
• zipped and shared
• improved over time

And most importantly:

If you can create a folder on your computer, you can build a Skill.

A model can also generate Skills automatically.
If the agent notices you perform a process repeatedly, you can tell it:

“Turn this into a Skill so I can reuse it.”

And it will.

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🔧 3. How to Build a Skill (Technical Breakdown)

A Skill follows a simple structure:

Step 1 — The Container

Create a folder that holds everything the Skill needs.

Step 2 — The Definition File (skill.md)

This is the core of the Skill.

It uses Progressive Disclosure, meaning the agent only sees what it needs:

• Header (metadata):
  Shown always. Tells the agent what the Skill is.

• Body (instructions):
  Read only when the agent decides to use the Skill.

This protects the context window and keeps reasoning efficient.

Step 3 — Executable Tools (Scripts)

A Skill can include actual runnable code, such as:

format_slides.py generate_invoice.sh resize_images.js

yaml Copy code

Why use code?

• It is self-documenting.
• The agent can read, debug, and fix it.
• It solves the “cold start problem”.

Step 4 — Automation (Skill Creator)

You don’t even have to create Skills manually.

A “Skill Creator Skill” can build new Skill packages for you automatically.

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🧱 4. A New Architecture for AI

The speakers proposed an architecture that mirrors classic computing:

• Model = Processor
  (raw intelligence)

• Agent Runtime = Operating System
  (manages files, tools, execution)

• MCP = Connectivity Layer
  (I/O with real-world systems)

• Skills = Applications
  (domain-specific logic)

This shifts development away from building isolated agents and toward building reusable Skill modules.

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🔄 5. The Vision: Continuous Learning Through Skills

As Skills accumulate, they become:

• a reusable memory
• a knowledge base
• a set of capabilities that compound over time

Imagine this:

If an agent learns a complex process today, it can save it as a Skill.
A future agent can load that Skill and instantly gain that ability.

That means:

• less repeated work
• better long-term performance
• shared ecosystem of knowledge
• compounding improvement

Or as the speakers put it:

“Claude on Day 30 should be significantly better than Claude on Day 1.”

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🚀 Final Thought

Most companies are trying to rebuild the “AI operating system” over and over again by creating new agents for every task.

This talk made a strong case that we should stop doing that.

Instead of building more agents, we should build more Skills — modular, composable pieces of expertise that make a single agent smarter, more reliable, and more consistent.

The future isn’t a hundred siloed AI agents.

The future is one powerful agent… with thousands of Skills.