There are two types of people who use AI: One opens Claude every day, inputs a long description, receives an answer, and closes the page. The next day, they come back, input the same description again. After 30 days, their efficiency remains the same as the first day.

The other type also uses Claude, but after 30 days, their AI has transformed into something completely different—an AI that automatically writes in their style, automatically formats output as per their preference, and automatically invokes the methodologies they have taught. Moreover, the time they spend on "guiding the AI" decreases day by day.

Same tool, same model, same price. How did the gap arise?

It's not a gap in skills. It's a gap in cognitive frameworks.

Today, we share three frameworks. Understanding them will fundamentally change how you use AI.

Framework One: Three Layers of Evolution—Which Layer Are You On?

There are three levels of using AI. The vast majority of people are forever stuck at the first level.

First Level: Prompt

A Prompt is the temporary instruction you input in the dialogue box. "You are a senior copywriter," "Use a concise style," "Give me three options."

It is effective in the moment and disappears when the session ends.

It's like explaining who you are to an amnesiac genius every morning. It's indeed brilliant, but it forgets you by tomorrow. Your tone preference, brand guidelines, output format, industry terms—everything resets, everything needs to be re-explained.

What does it look like after 30 days? You wrote a good Prompt on Day 1 and got good results. By Day 15, you've repeated roughly the same context 15 times. By Day 30, your productivity is the same as Day 1. No accumulation.

Furthermore, on tired days, you miss details, and the output quality decreases. On busy days, you skip the context altogether, and Claude gives you a generic version.

You are the bottleneck. Every conversation is.

Layer 2: Project

You have uploaded reference documents, style guides, system commands in the Project. Every conversation in this Project knows your context.

It's like giving a new employee an onboarding manual. Much better than explaining everything verbally every day.

But there is one issue: you have to remember to open the correct Project. Your knowledge is locked within a specific Project, and switching contexts means starting over.

Layer 3: Skill

A Skill is a structured document—you write it once, install it once, and then Claude automatically triggers it when relevant tasks are identified.

No need to open a specific Project. No need to input any trigger words. Claude just knows what to do.

It's like training an employee once, and the training remains effective forever.

All three layers use the same Claude. But the first layer is a chat tool, and the third layer is a work system.

So, now that you understand this layering, how do you transition from the first layer to the third layer? This requires a second framework.

Framework 2: Transactional Thinking vs Compound Thinking

This is the most crucial of the three frameworks. It's not a tool-use technique but a cognitive model.

Prompt is transactional. You invest time to write an instruction and get one output. The next time you invest, you get another output. It's a 1:1 linear relationship between input and output. You stop inputting, the output immediately drops to zero.

Skill is compound. You spend 10 minutes on the first day to write a Skill, and it's already working the next day. By day 15, you have accumulated 3 Skills, each building on top of the previous ones. By day 30, your Claude is unlike anyone else's.

The setup cost is one hour of distributed input in the first week. The return is that every subsequent conversation operates at a higher baseline.

The work of the first week continues to yield returns in the sixth month. That's compound interest.

People with a transactional mindset ask every day, "How can I use AI to do this well?"

People with a compounding mindset ask, "How can I make AI always know how to do this?"

One word's difference. But if you use a compounding mindset with AI, after 30 days, you'll discover something magical: the time you spend on "teaching AI" decreases, and the work AI does for you increases. This is because every Skill you taught before continues to be effective.

This leads to a practical question: How should Skills be written? What should be included, and what should not? This brings us to the third framework.

Framework Three: Thin Harness, Fat Skills — Spend 90% of Your Energy in the Right Place

This framework comes from YC Co-founder Garry Tan, who distilled it into an extremely concise architectural principle: Thin Harness, Fat Skills.

What does this mean?

When you work with AI, you are actually building a three-layer system — whether you realize it or not:

Top Layer: Skills. The playbook you teach AI — processes, criteria, domain knowledge. This is where 90% of the value lies.

Middleware Layer: Harness. The program or environment running AI — invoking models, managing context, reading and writing files. Keep this extremely thin.

Bottom Layer: Deterministic Tools. Database queries, code compilation, mathematical calculations — operations where the input is the same, the output is the same, and it's consistent every time.

The principle is: Push intelligence into Skills. Push execution into deterministic tools. The thinner the Harness in the middle, the better.

What is the anti-pattern? Fat Harness, Thin Skills. Have you ever seen a situation where a lot of time is spent debugging toolchains, configuring various plugins, optimizing API calls, but not a single word is written on actually teaching AI "how to do this well"?

The result is: the toolchain is very sophisticated, but the quality of AI output is no different from naked chat. You may have optimized the pipeline, but what flows through the pipeline is still tap water.

The intelligence of the model is already sufficient. Its failure is not due to a lack of intelligence but a lack of understanding of your specific context—your specifications, your conventions, the unique nature of your problem. Skills address this issue.

Another important implication of this framework is: when the next, more powerful model is released, all your Skills will automatically improve.

Because Skills define the process and standards, an enhancement in underlying judgment will allow these processes to be executed more precisely. You don't need to rewrite anything. Model upgrades for you are not about "learning again," but "my system just received a free upgrade."

Skill is a permanent asset.

How to Integrate Three Frameworks

Step 1: Position Yourself with the Three-Layer Evolution.

Which layer are you in right now? If you re-enter context in each conversation—you are in the first layer. If you are using Project but have no Skills—you are in the second layer. Knowing where you are helps you know where to go.

Step 2: Use Compound Interest Thinking to Find Your Skill Candidate List.

Recall your conversations with AI over the past month. Which commands have you repeated? Which contexts have you repeatedly explained? Which formatting requirements do you have to remind each time? Which processes have you manually guided step by step?

If you have repeated something more than three times, it's a Skill waiting to be created.

There is an even more aggressive principle: if you have AI do something, and this action will be repeated in the future—it should become a Skill the first time. Do it manually the first time, check the output, if satisfied, immediately code it into a Skill file.

Evaluation criterion: If you need to request the same thing a second time, the system has failed.

Step 3: Use Thin Harness, Fat Skills to Decide Where to Focus Your Energy.

Don't spend three days debugging your toolchain and then run tasks with a bare prompt. Instead, spend three days perfecting your core skill, and keep the toolchain as simple as possible.

What does a Skill actually look like? Extremely simple, it's just a text file:

Name — What it's called. Description — What it does (in one sentence). This is the most critical part — Claude relies on this sentence to determine when to trigger automatically. Instructions — How to do it (specific steps). Constraints — What it cannot do.

A Skill doesn't tell the AI "what to do" — that's the Prompt's job. A Skill tells the AI "how to do it".

The Prompt says, "Help me write a competitive analysis." The Skill says, "When doing a competitive analysis, first identify 3-5 core competitors, compare them along the dimensions of features/pricing/market positioning, output in SWOT format, provide data sources for each conclusion, and finally give 3 actionable recommendations."

The Prompt provides the task. The Skill provides the methodology. When the two work together, AI transitions from being "an intern waiting for you to tell it each step" to "an employee who knows how to get the job done."

Furthermore, the same Skill can be repeatedly invoked with different inputs — input a competitor company, you get a competitive analysis; input an industry trend, you get a trend report; input an investment target, you get a due diligence brief. Same process, different subjects, completely different outputs.

This is not a Prompt engineering. This is software design using Markdown.

How to build your first Skill

The quickest way: Let AI help you build it.

Claude has a built-in "Skill Creator" — a Skill that can create Skills. You just need to say, "Help me create a Skill to [your specific task]."

Claude will interview you, distill the process, and output a structured .md file. Save it, and you're good to go.

In an afternoon, you can set up your entire personal Skill system. Each one takes 10 to 15 minutes. Writing style, competitive analysis, meeting minutes, email responses, report generation, content calendar — together take less than two hours.

This two-hour compounding return has no limit.

Epilogue

Three frameworks, three sentences:

Three-Tier Evolution: From Prompt to Project to Skill, the same AI, three completely different experiences. Which tier are you on?

Trading vs Compounding: Prompt is daily reset trading. Skill is daily compounding asset growth. Which one do you choose?

Thin Harness, Fat Skills: Don't spend energy on the toolchain. Put 90% of your focus on writing a great Skill—that's where the value lies.

Every Skill you build is a permanent upgrade to your AI system. It doesn't degrade, doesn't forget, and automatically strengthens with model updates.

Prompt is verbal instruction. Skill is an SOP manual. One resets daily, the other compounds daily.

Starting today: Identify the task you repeat more than three times. Spend 10 minutes and write your first Skill.

Then you'll never want to go back to the days of just using Prompt.