AI Wakes Up With Amnesia Every Morning. Agent Skills Fix That.

After two years of using AI, my biggest frustration is not that AI is not smart enough.

It is that AI forgets too much.

Every time I open a new chat, I have to teach it the same workflow again.

Give me three topic ideas. Each one needs a title and an outline.

Search for the latest information. Prioritize official blogs and English-language sources.

After the first draft, review it. Remove filler phrases. Make it sound less like generic AI writing.

I know the workflow. The problem is that the AI does not remember it.

It feels like hiring a brilliant intern who wakes up with amnesia every morning.

Your process, your taste, your quality bar, your writing rules, your small preferences from two years of practice: gone.

Agent Skills are designed to solve exactly this problem.

The Fourth Evolution Of AI Tools

The first major shift came in late 2022, when ChatGPT made AI conversational. You could ask questions and get useful answers. It was impressive, but the AI could only talk. It could not actually do much.

In 2023, tool use and function calling changed that. AI systems could search the web, run code, read files, and write files. The AI moved from being a consultant to being an assistant.

In late 2024, Anthropic introduced MCP, the Model Context Protocol. MCP gave AI tools a standard way to connect to external systems: GitHub, databases, Slack, internal tools, and more. MCP gave AI hands.

Then came Agent Skills.

The first three shifts answered the question: what can AI do?

Agent Skills answer a different question: how should AI do the work in your way?

MCP gives the AI access. But access is not enough.

If you hire a new employee, give them a laptop, and give them every system permission, they still do not know how your company writes weekly reports, reviews code, handles customer emails, or structures research.

Skills are the onboarding manual.

What A Skill Is, And What It Is Not

At the surface level, a Skill is simple.

It is a Markdown file named SKILL.md.

No compiled code. No complex dependency. No special IDE. You can create one in any text editor.

But a Skill is not just a saved prompt. It has three important properties:

• It is modular.
• It is discoverable and triggerable.
• It is shareable.

The modular part matters most.

Many people put all their rules into one global system prompt. I understand the impulse. If the AI needs to know your rules, why not load them all the time?

Because context is not free.

I currently use 27 Skills. If I dump all of them into a system prompt, every conversation starts with thousands of words of instructions. That burns context before the actual work even begins.

Skills are loaded on demand.

When I am writing, the writing Skill loads.

When I am doing data analysis, the analysis Skill loads.

The AI only sees what it needs for the current task. The idea is similar to how an operating system manages memory: load what is needed, keep the rest out of the working set.

Here is the cleaner analogy:

MCP is like USB ports and device drivers. It lets the computer connect to printers, cameras, and external drives.

Skills are like apps. They tell the computer how to use those capabilities to complete real work.

MCP means: what can I connect to?

Skill means: how should I work?

Together, they form a more complete agent stack.

What A Simple Skill Looks Like

A minimal Skill can look like this:

---
name: weekly-report
description: |
  Generate a weekly team report. Use this skill when the user mentions
  "weekly report", "team update", or "this week's summary".
---

# Weekly Report

1. Ask for the main work completed this week.
2. Organize the result into three sections: Done / In Progress / Next.
3. Keep each bullet to one sentence, no more than 15 words.
4. Keep the final report under 300 words.

There are two layers.

The frontmatter tells the system what the Skill is and when it should be loaded.

The body tells the AI how to execute the task.

Notice the line: “no more than 15 words.”

If you only write “make it concise,” the AI has to guess what concise means. If you write “no more than 15 words,” the instruction becomes concrete. Modern language models are surprisingly good at following that kind of structure.

This is one of the underrated strengths of Skills: they turn fuzzy preference into operational instruction.

In my experience, a good Skill is usually between 500 and 2,000 words.

More detail is not always better. A good recipe does not teach you how to hold a knife or turn on the stove. It tells you the specific things that matter: medium heat, three minutes, half a spoon of salt, add scallions before serving.

A good Skill works the same way. It should encode your standards, preferences, examples, and constraints. It should not try to teach the model everything from zero.

Why This Becomes Portable Work Experience

By 2026, more than 20 AI products support the Agent Skills pattern, including tools such as Claude Code, Cursor, Codex, Gemini CLI, and JetBrains Junie.

The important part is portability.

A well-written SKILL.md is not locked inside one chat history or one product. It can move across tools.

Write once. Use everywhere.

That changes what “personal AI setup” means.

Instead of repeatedly telling each AI tool your preferences, you package your workflow into a file.

For me, Skills now cover most of my recurring work:

• topic ideation
• research
• drafting
• editing
• illustration prompts
• WeChat formatting
• data analysis

Each high-frequency workflow has its own Skill.

The value is not that the AI becomes magically smarter.

The value is that it becomes more familiar with how I work.

The More Interesting Direction: Distilling Thinking Frameworks

There is another direction that I find even more interesting than workflow automation.

In March 2026, a GitHub project called “coworker.skill” became popular in the Chinese AI community. The idea was simple: feed an AI a former colleague’s messages, emails, and documents, then generate a Skill that captures how that colleague communicates and works.

That is useful, but it is only the beginning.

The more interesting version is not distilling behavior.

It is distilling thinking frameworks.

Charlie Munger left books, speeches, and decades of shareholder meeting transcripts. Richard Feynman left lectures and interviews. Naval Ravikant left thousands of public posts. Public material from thinkers like this is often richer than any coworker’s chat history.

A creator I follow built a Skill called nuwa.skill to distill frameworks from thinkers such as Munger, Feynman, Elon Musk, Naval, and Nassim Taleb.

The point is not roleplay.

It is not “write in Feynman’s voice” or “pretend to be Naval.”

The point is to extract usable mental models.

The filtering standard is strict:

• The idea must appear across multiple domains.
• It must generate useful answers for new problems.
• It must be distinctive, not just something every smart person would say.

If a framework passes those tests, it becomes part of the Skill.

This changes how you can use AI.

When writing, you can ask: would this concept be clearer with a Feynman-style analogy?

When making a decision, you can ask: where is the leverage here?

When evaluating risk, you can ask: what is the worst case, and can I survive it?

That is when Skills stop being only automation tools.

They become extensions of thought.

The Moat Is What You Cannot Write Down

There is a paradox here.

The people who turn their workflows into Skills are often the least likely to be replaced by Skills.

Why?

Because they separate the repeatable parts from the creative parts.

They let the Skill handle the repeatable work, then use the freed-up attention for new judgment, taste, and synthesis.

They stay ahead of their own automation.

The more dangerous position is the opposite: never organizing your process, never extracting your standards, never turning your work into reusable instructions.

If your repetition and creativity are mixed together in a messy, implicit way, someone else may be able to approximate a surprising amount of it from your public output or chat history.

“Know yourself” is no longer only a philosophical question.

It has become an engineering question:

What belongs in your SKILL.md?

And more importantly:

What cannot be written into it?

The part that can be written down can be systematized.

The part that cannot be written down is where your real judgment lives.

Protect that part. Improve it. Use Skills to clear space for it.

That is the real reason I care about Agent Skills.

Not because they make AI look impressive.

Because they force you to understand your own work.