TL;DR
- The Shift: The unit of AI work is moving from prompts to skills: packaged instruction sets an agent runs on demand.
- The Format: A skill is one markdown file, an SOP for AI. Models read it more accurately than PDFs, and it improves every time you use it.
- The Product: Subject-matter expertise is the raw material. A voice dump and a skill creator are enough to package it.
- The Flywheel: Practitioners are using their own skills to build the sites and content that sell the skills.
- The Bridge: Skills are the on-ramp to agents. Coding agents and MCP connections turn skills into work that runs across your tools.
There is a pattern I have watched repeat for a decade: a new distribution layer opens, everyone stares at the biggest player in it, and the actual money gets made three levels deeper. The app stores are crowded. The Shopify app store, the Chrome extension store, the WordPress plugin market, the Notion template economy: they all got built by people who stopped asking how to win the platform and started asking what they could sell on top of it.
AI is at that moment now, and the newest layer is skills. A skill is a packaged instruction set, an SOP for AI, that lets a model execute one specific type of work the way an expert would do it. Practitioners are already selling them as digital products, and the early numbers are real: one documented skill stack cleared $3,000 in its first 45 days with no paid promotion, and a 20 to 25 skill package sells for around $99 through plain website distribution.
This matters to every B2B marketer and founder for one reason. You already own the raw material. The question is whether you have packaged it.
The Skill Is the New Unit of AI Work
Most people still work with AI the way they worked with it in 2023: one prompt at a time. Power users graduated to projects and custom GPTs, which beat raw prompting but inherit its core weakness. Every session still depends on you re-explaining context, standards, and preferences, and the knowledge stays trapped in whichever chat it was built in.
A skill collapses all of that into one file. It is granular by design: separate skills for an SEO blog post, an email newsletter, and a sales email, rather than one broad instruction set that does all three poorly. Each skill encodes the best practices, the style rules, the failure modes, and the output format for a single task. Think of it as a recipe: same ingredients every time, same result, no improvisation required from the cook.
Two properties make skills structurally different from what came before. First, they update in real time. When a skill produces something off, you tell it what went wrong and it rewrites itself on the spot, so that mistake does not happen again. The quality of a skill compounds with use, which is something no prompt or project can claim. Second, they are portable. Skills are markdown files, and the AI models process markdown more accurately than almost any other format, including PDFs, which they read poorly. A skill built for one coding agent transfers to the next one, so your system survives model churn.
Why Skills Beat Prompts and Projects
| Prompt | Project or GPT | Skill | |
|---|---|---|---|
| Context | Rebuilt every session | Stored in one chat | Encoded in one file |
| Consistency | Varies with your phrasing | Better, still drifts | Same standard every run |
| Error correction | Fix it manually each time | Fix it manually each time | Fix once, never again |
| Automation ceiling | None | Low | Runs inside agents |
| Portability | None | Locked to one app | Any agent, any model |
The table is the pitch. Prompts are how you talk to AI. Projects are where you keep the conversation. Skills are how you teach it a job. Once the job is encoded, it can run inside an agent that does the work while you do something else, which is the direction I have been pushing teams toward since I stopped telling people to collect prompts and started telling them to build workflows their agents can run.
The Product Path: From Brain Dump to Markdown File
Here is the part that sounds exaggerated until you watch someone do it. Anthropic publishes free skills, including a skill creator, that encode best practices for building new skills correctly. The entire creation process, end to end, takes minutes.
Not your whole job. One subtask with a clear output: a blog post from a video, a newsletter, a sales email, a thumbnail design.
Open the assistant on your phone and talk through your process: the steps, the standards, the mistakes to avoid, the examples you measure against.
Ask it to turn the dump into a skill markdown file, test it on a real task, and correct it in real time until the output matches your standard.
That is the whole barrier to entry. If you can hold a conversation with AI, you are technical enough to build a skill. The expertise is the product, and expertise is not limited to marketing. Gardeners, contractors, nonprofit operators, collectors: anyone with years of judgment in a domain can encode it, and people will pay for the judgment because it took them years to build and the buyer gets it in one file.
The Flywheel Workflow That Proves the Category
The most instructive skill in the wild is the video-to-post writer, because it shows how a skill becomes both an internal system and a revenue asset. The workflow takes a YouTube URL and produces an SEO-structured blog post: internal and external links, author bio, FAQ section, keyword targeting, all in one short prompt. The practitioner runs it on every video he publishes, and the posts rank in Google and in AI-generated answers alongside the videos they came from. One prompt, one skill, a repeatable content engine.
The same operator ships a companion skill that filters AI tells out of drafts, a list of the tired words and phrases that make machine writing recognizable, and it runs as a final pass on everything. Add MCP connections, the standard that lets agents reach external services, and skills stop producing files and start doing work: publishing to WordPress, creating Stripe products, scheduling social posts from inside the agent interface. This is the architecture I run behind my own operation, and it is the same progression I mapped out when I documented moving from one automation to a team of AI agents.
Then comes the part that turns a productivity hack into a business. The skills that run the operator’s own site were used to build the site itself, and the site sells the skills to other people. His web design skill knew his branding, his tone, and his logo, so it built the landing page, the Stripe product, and the sales flow. The flywheel is closed: the system produces the content, the content ranks, the traffic converts, and the same skills that did the work get sold to the people who discovered them. Distribution is plain: a website, email newsletters, and soft calls to action, no marketplace required. The marketplaces will come later, and the sellers who own distribution before they arrive will have the advantage.
What I Actually Think
The skills trend validates something I have believed since I started running this business on AI systems: the discipline that makes internal AI work is itself a product line. I run my operation through a crew of named agents with documented roles, and every platform I work on has a manual that tells the agent exactly how to execute: the steps, the standards, the mistakes that have already been made and logged. That is a skill stack. I just had not called it that, and I had not thought to sell it.
When your AI workflow is good enough to run your own business, it is good enough to sell. That is the line between using AI and owning it.
Here is my read on where this goes. Agencies and consultants are sitting on the most sellable asset they have ever owned: the exact way they do their best work, encoded and repeatable. The firms that package their methodology as skills will sell the system once and keep the retainer for the judgment. The moat is not the file. The moat is the taste that built it, which is why this window favors people with real subject-matter expertise over people who are good at prompting. The prompt writers can copy the format. They cannot copy twenty years of judgment, and the market will figure that out quickly.
If you are a marketing leader or founder, the move is not complicated. Pick the one task your team does best with AI, package how you do it, and force yourself to sell it, even for a token price. You will learn more about your own system in one sales cycle than in a month of internal optimization, and you will be measuring the output the way I argue every AI investment should be measured: by impact, not by output.
A skill is a promise: do this task the way I would do it. The market for those promises is just opening, and the barrier to entry is expertise you already have.
Want to talk about turning your team’s AI workflows into a repeatable system, or a product? My inbox is open.
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