There are two types of AI users: One opens Claude every day, inputs a long background description, gets a response, and closes the page. The next day, they come back and input the same description again. After 30 days, their efficiency remains exactly the same as on day one.
The other also uses Claude, but after 30 days, their AI has become something completely different—automatically writing in their tone, automatically outputting in their format, automatically applying the methodologies they've taught it. And the time they spend 'instructing the AI' actually decreases day by day.
The same tool, the same model, the same price. How did the gap emerge?
It's not a gap in skill. It's a gap in cognitive frameworks.
Today, I'll introduce three frameworks. Understand them, and your way of using AI will fundamentally change.
Framework One: The Three-Layer Evolution Theory—Which Layer Are You On?
There are three layers to using AI. The vast majority of people are stuck on the first layer forever.
Layer One: Prompt
A Prompt is the temporary instruction you type into the chat box. "You are a senior copywriter," "Use a concise style," "Give me three options."
It's effective for the moment. It disappears when the session closes.
This is like explaining who you are to an amnesiac genius every morning. It is indeed smart, but tomorrow it won't recognize you again. Your tone preferences, brand guidelines, output format, industry terminology—all reset to zero, all need to be re-explained.
What does 30 days of this look like? Day 1: wrote a good Prompt, got a good result. Day 15: you've repeated inputting roughly the same context 15 times. Day 30: your productivity is exactly the same as Day 1. Zero accumulation.
And on a tired day, you'll miss details, and the output quality drops. On a busy day, you might skip the context altogether, and Claude gives you a generic, universal version.
You yourself are the bottleneck. Every single conversation.
Layer Two: Project
In a Project, you upload reference documents, style guides, system instructions. Every conversation within this Project knows your context.
This is like giving a new employee an onboarding manual. Much better than explaining verbally every day.
But there's still a problem: you have to remember to open the correct Project. Your knowledge is locked inside specific Projects; switch to another scenario and you have to start from scratch.
Layer Three: Skill
A Skill is a structured file—you write it once, install it once, and afterwards, Claude automatically triggers it when it recognizes a relevant task.
No need for you to open a specific Project. No need for you to input any prompt. Claude just knows what to do.
This is like training an employee once, and it's effective forever.
All three layers use the same Claude. But the first layer is a chat tool, the third layer is a work system.
So, after understanding this layering, how do you jump from the first layer to the third? This requires the second framework.
Framework Two: Transactional Thinking vs. Compound Thinking
This is the most important of the three frameworks. It's not a tool usage technique, but a cognitive model.
Prompt is a transaction. You invest time writing an instruction, get one output. Next time, invest again, get another output. Input and output have a 1:1 linear relationship. You stop investing, output immediately drops to zero.
Skill is compound interest. You invest 10 minutes on day one writing a Skill, and on day two it's already working. By day 15, you've accumulated 3 Skills, each building on the previous ones. By day 30, your Claude is different from everyone else's.
The setup cost is one hour dispersed over the first week. The return is that every subsequent conversation runs on a higher baseline.
The work from the first week is still paying returns in the sixth month. This is compound interest.
The transactional thinker asks daily: "How do I use AI to do this well today?"
The compound thinker asks: "How do I make the AI know how to do this forever?"
A difference in wording. But if you use AI with a compound mindset, after 30 days you'll discover something magical: the time you spend "teaching the AI" decreases, while the work the AI helps you complete increases. Because every Skill you taught before is continuously effective.
This leads to a practical question: How exactly should a Skill be written? What should go in, and what should stay out? This is the third framework.
Framework Three: Thin Harness, Fat Skills—Put 90% of Your Effort in the Right Place
This framework comes from YC's head, Garry Tan, who distilled it into an extremely concise architectural principle: Thin Harness, Fat Skills.
What does it mean?
When you work with AI, you are actually building a three-layer system—whether you realize it or not:
Top Layer: Skills. The operating manuals you teach the AI—processes, judgment criteria, domain knowledge. This is where 90% of the value lies.
Middle Layer: Harness. The program or environment that runs the AI—calling the model, managing context, reading/writing files. Keep it extremely thin.
Bottom Layer: Deterministic Tools. Database queries, code compilation, mathematical calculations—operations where the input is the same, the output is the same, every single time.
The principle is: Push intelligence into the Skills. Push execution into the deterministic tools. Keep the middle Harness as thin as possible.
What's the anti-pattern? Thick Harness, Thin Skills. You've seen that situation: spending a lot of time debugging toolchains, configuring various plugins, optimizing API calls, but the content that actually teaches the AI "how to do this well"—not a single word written.
The result is: the toolchain is beautiful, but the quality of the AI's output is not fundamentally different from naked chatting. Because you optimized the pipeline, but what flows through it is still tap water.
The model's intelligence is already sufficient. It fails not because it's not smart enough, but because it doesn't understand your specific context—your norms, your conventions, the particular shape of your problem. Skill solves this problem.
Another important corollary of this framework is: When the next, more powerful model is released, all your Skills will automatically become better.
Because Skills define processes and standards; improvements in underlying judgment power will make these processes execute more accurately. You don't need to rewrite anything. A model upgrade for you is not "having to learn again," but "my system got a free upgrade."
Skill is a permanent asset.
How to Use the Three Frameworks Together
Step One: Use the Three-Layer Evolution Theory to locate yourself.
Which layer are you on now? If you're re-entering context every conversation—you're on the first layer. If you're using Projects but no Skills—you're on the second layer. Knowing where you are tells you where to go.
Step Two: Use compound thinking to find your Skill candidate list.
Recall your conversations with AI over the past month. Which instructions have you repeated? Which contexts have you explained over and over? Which format requirements do you have to remind it of every time? Which processes have you manually guided step-by-step?
If you've repeated it more than three times, that's a Skill waiting to be created.
There's an even more radical principle: If you had the AI do something, and it's something you'll do again in the future—the first time should become a Skill. Do it manually the first time, review the output, and if satisfied, immediately encode it into a Skill file.
The test standard: If you need to ask for the same thing a second time, the system has failed.
Step Three: Use Thin Harness, Fat Skills to decide where to focus your energy.
Don't spend three days debugging a toolchain and then run tasks with a naked Prompt. Do the opposite—spend three days writing your core Skill, and use the simplest toolchain possible.
What does a Skill actually look like? Extremely simple, it's a text file:
Name—What it's called. Description—What it does (one sentence). This is the most crucial part—Claude uses this sentence to judge when to trigger automatically. Instructions—How to do it (specific steps). Constraints—What not to 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".
Prompt says: "Help me write a competitor analysis." Skill says: "When doing competitor analysis, first identify 3-5 core competitors, compare them across three dimensions: functionality/pricing/market positioning, output in SWOT format, attach data sources to each conclusion, and finally provide 3 actionable recommendations."
The Prompt provides the task. The Skill provides the methodology. When the two work together, the AI transforms from an "intern waiting for you to tell them what to do every step" into an "employee who knows how to do the job."
And the same Skill can be called repeatedly with different inputs—input a competitor company, you get a competitor analysis; input an industry trend, you get a trend report; input an investment target, you get a due diligence brief. Same process, different objects, completely different outputs.
This is not Prompt engineering. This is software design with Markdown.
How to build your first Skill
The fastest way: Let the AI build it for you.
Claude has a built-in "Skill Creator"—a Skill that creates Skills. You just need to say: "Help me create a Skill for [your specific task]."
Claude will interview you, refine the process, and output a structured .md file. Save it and you can use it.
In one afternoon, you can set up your entire personal Skill system. Each takes 10 to 15 minutes. Writing style, competitor analysis, meeting minutes, email responses, report generation, content calendar—all together less than two hours.
The compound return on these two hours has no upper limit.
Finally
Three frameworks, three sentences:
Three-Layer Evolution Theory: From Prompt to Project to Skill, the same AI, three completely different experiences. Which layer are you on?
Transaction vs. Compound Interest: Prompt is a transaction that resets daily. Skill is an asset that appreciates daily. Which do you choose?
Thin Harness, Fat Skills: Don't spend energy on the toolchain. Put 90% of your attention into writing good Skills—that's where the value is.
Every Skill you build is a permanent upgrade to your AI system. It doesn't degrade, doesn't forget, and automatically becomes stronger when the model updates.
Prompt is a verbal instruction. Skill is an SOP manual. One resets daily, one compounds daily.
Starting today: Find that task you've repeated more than three times. Spend 10 minutes, write your first Skill.
Then you'll never want to go back to using only Prompts again.
