Stop Building New Agents—Leverage Simple “Skill” Folders for Real AI Value

Many AI developers are discussing "Agents", but they often find that while agents are intellectually powerful, they lack the domain expertise needed for concrete tasks, leading to attempts to create all‑purpose agents that fall short.

Smart Genius vs Experienced Expert

Anthropic’s internal talk featured an analogy: Mahesh, a genius with IQ 300 who has never filed taxes, versus Barry, a regular‑IQ accountant with 20 years of tax experience. In real life, people choose Barry because he possesses domain knowledge and procedural experience, whereas Mahesh would need to relearn tax law from scratch. Current agents resemble Mahesh—highly capable but missing the "feel" of specific domains.

What Is a “Skill”?

Anthropic defines a Skill as a simple folder containing: skill.md – a markdown description.

Executable scripts (Python, Bash, etc.).

Reference files needed for the task.

An example is the PDF‑to‑Markdown Skill developed in a previous report.

Skill in Action: UI Design

When the author asked Cursor’s AI to design a "Classical Poetry App" without any skill, the result was a garish purple gradient background and poor aesthetics. After loading the frontend-design skill, the AI consulted the design specifications in the folder, produced a layout with proper whitespace, elegant colors, and a parchment‑style background, demonstrating the skill’s power to make the AI "understand the domain".

Code as a Universal Interface

Instead of hard‑coding tool usage in prompts, a Skill provides a runnable script that the agent can execute directly. For example, when an agent needs to process an Excel file, it can run a pre‑written Pandas script without re‑thinking how to call the library.

Creating Skills Without Deep Coding

While programming knowledge helps, it is not required. Users can describe their requirements and let AI coding assistants (e.g., Claude Code or Cursor) generate the necessary scripts, achieving 80‑90% quality. Anthropic also offers a Skill Creator to streamline this process.

Progressive Disclosure and Context Management

Agents start with a generic ability (e.g., "write documents") and only read the detailed instructions in a Skill folder when the user explicitly requests that capability. This progressive disclosure prevents context‑window explosion and allows an agent to host hundreds of skills without overloading its memory.

Knowledge Assetization

Companies can encode best practices into Skills, turning procedural knowledge into reusable assets. When senior employees leave, their expertise remains in the Skill folders, enabling new hires or new agents to instantly inherit that knowledge.

Empowering Non‑Technical Users

Because a Skill is just a folder with documentation and scripts, business experts (e.g., accountants) can write a step‑by‑step guide for a task; the AI fills in the code, effectively lowering the barrier to software development.

CPU / OS / App Analogy

The talk compares large models to CPUs, the agent runtime to an operating system, and Skills to applications. Just as the PC ecosystem succeeded by building countless apps on a stable CPU/OS foundation, the AI ecosystem should focus on creating valuable Skills rather than reinventing the underlying model or runtime.

Conclusion

The key insight is to stop obsessing over building a universal, all‑knowing AI and instead package domain expertise into simple, folder‑based Skills. In an era where models and runtimes converge, these Skills become the most valuable "apps" for ordinary users and developers alike.