Every time a technological revolution begins, people habitually use old maps to seek new territories.

If you look at the so-called “AI agent” community today, you’ll find a bustling scene. Various platforms are launching what they call “Skill stores” and “plugin marketplaces,” encouraging developers to create a wide range of Skills—weather checks, web searches, data retrievals. The entire industry seems to be celebrating this “Lego-like” prosperity.

However, behind this noise lies an absurd logical disconnect. Stripping away all product packaging and marketing jargon, and examining the situation from a purely engineering and business perspective, we find that most of the popular “Skills” in the industry today are merely industrial waste of the AI era.

Understanding the Problem with Skills

To understand why Skills are worthless, we need to examine what they truly are. In the current application architecture, a Skill is essentially a short piece of glue code (Payload) along with a description of an API interface (JSON Schema). It tells the large model: “I have a tool, named X, that can check Y data, and you need to provide me with Z parameters.”

This is just a thin layer of window dressing.

Why do major companies and SaaS platforms package it as an independent concept, even creating “Skill stores”? The answer is simple: it is a remnant of the path dependency from the Web2 era, a lingering dream of “rent-seeking” by platform providers.

In the mobile internet era, apps were the gateways to traffic and the moats of ecosystems. Apple and Android established unbreakable business empires through their App Stores. The current players in the AI platform space still think in these classical traffic terms. They attempt to forcibly cut and solidify the powerful dynamic generation capabilities of large models into static “plugins (Skills)” and place them on shelves.

They try to create an artificial scarcity, making users feel that “my agent is superior to yours because I have more impressive Skills.”

This is a tragic case of trying to find a sword by carving a boat. The essence of large models is the compression of knowledge and the emergence of logic; it is a dynamic, fluid intelligence. Yet, the current platform providers insist on freezing this fluid intelligence back into standardized industrial components (Skills) and selling them by the piece.

The Devaluation of Standardized Components

Why are these “standard components” worthless today? Because, in the face of top-tier large language models, the cost of executing actions has approached zero.

In the past, you needed an engineer to write code for several days to connect a system’s interfaces, handling various authentications, parsing, and error retries. That code had value.

But today, as long as you have a clear interface document, a model can generate a perfectly tailored calling logic in seconds. These dozens or hundreds of lines of micro-instructions and glue scripts hardly deserve to be called “independent technology.”

Since AI can instantly write calling code for any interface at any time, why should we pre-write this code, package it as something called a “Skill,” and store it?

Hoarding Skills is like filling your yard with hundreds of water tanks in an era where water pipes are already laid out and you can simply turn on the tap. This practice is not only bulky but also ridiculous. Developers who take pride in their micro-instructions fail to realize they are hoarding industrial waste that will inevitably be eliminated by the times.

The Illusion of Universality and Complex Business Realities

Some may argue that the Skills provided by platforms are tested and universal, saving development time. This exposes their ignorance of how the real business world operates. In genuine high-level application environments, especially in the deep waters of enterprise management and business decision-making, the universality of micro-actions is a complete fallacy.

The real business world is muddy and complex, filled with interest games and historical legacy issues. The action of A Company querying its subsidiary’s quarterly performance and B Company doing the same involves entirely different ERP system structures, financial metric definitions, and even inter-departmental authority barriers.

To attempt to adapt a standardized “financial query Skill” packaged in the cloud to fit all enterprises’ complex environments is akin to trying to use a factory-produced master key to open all the intricately customized safes in the world.

Valuable tool invocation must, and can only, be based on the specific contextual environment at that moment, written temporarily and generated dynamically. Different scenarios and environments require different code. Pre-packaged static Skills, once removed from their pre-set greenhouse environments and thrown into the real business battlefield, will collapse instantly due to incompatibility.

The Bulldozer of Infrastructure

If all micro-execution actions are written and used on the fly, what maintains stability in the system? The answer is: standardized underlying protocols. For instance, the truly strategically valuable MCP (Model Context Protocol) in today’s developer community.

Many confuse MCP with Skills or think MCP is merely for better connecting Skills; this is a serious misunderstanding. The true mission of MCP is not to connect those static Skills but to ultimately eliminate them.

MCP provides an absolutely standardized, unified interconnection bus. When an enterprise’s internal financial databases, HR systems, and even complex business simulation sandboxes are exposed as contextual nodes through this standardized protocol, your intelligent agent does not need to pre-install any “Skills.”

In this ultimate scenario:

1. The intelligent agent perceives the need for a management action.
2. It dynamically understands the current enterprise architecture and data bus.
3. AI generates a set of instructions temporarily and on-demand based on the current situation, using the MCP protocol to complete data retrieval or action dispatch.
4. The action is completed, and the code is discarded.

This is called “Just in Time” intelligence. The protocol is the ironclad camp, the real highway; the specific instructions running on it are merely transient travelers. We only need to build the highway and do not need to raise those carriages running on it.

The Shift of Moats

When we completely strip down the industry’s facade, and all code related to “execution,” “calling,” and “interfaces” becomes worthless, where then lies the true barrier of AI applications?

The answer is: structured business cognition and awareness of power dynamics.

For a true “AI management expert” aimed at core decision-makers like chairpersons and CEOs, its value does not lie in having a menu of a hundred flashy Skills.

Executives do not need a robot that fills out forms for them, nor do they need a retrieval tool that only executes query commands.

A truly advanced intelligent agent has its moat deeply embedded in its diagnostic intuition of complex systems.

When the system detects an anomaly in the profit margin of a business line, a basic tool will mechanically retrieve financial reports (the typical Skill approach);

Whereas an intelligent agent with deep management cognition can keenly penetrate the data, realizing that this may be due to resource waste from power struggles between the top two leaders of that business line, thus autonomously deciding to retrieve recent personnel approval flow records and communication frequencies of key positions.

Executing an instruction is extremely cheap, but knowing “what step to take in the current intricate chess game” is exceedingly valuable.

The true barrier lies in the thinking framework you provide to this AI system: does it have a strategic perspective that overlooks the whole? Does it understand the friction within the organization? Can it conduct complex business simulations?

This is the “brain,” while those Skills that can be replaced and generated at any time are merely fingernails.

As we stand on the cusp of this era, the most common mistake is to confuse means with ends, treating transitional products as the crown of the future.

Those developers who are still proud of having packaged a few “exclusive Skills,” and those platforms trying to create an “AI skills supermarket,” are irreversibly heading toward mediocrity. They are using pre-industrial thinking to confine a future of ubiquitous, fluid intelligence.

Throw away that industrial waste. Stop wasting your life on micro glue code. Build a true cognitive engine and confront the complex truths of the business world. This is the true path for experts and developers in the AI era.