Building your business around AI comes down to two parts, data and skills. Data covers the hard facts about your company. Skills give AI the context it needs to operate like a senior member of your team.
At TJ Digital, this system lets AI handle roughly 70 to 80 percent of the hands-on work across our client campaigns, mostly through Claude. Within a year, I expect that number to reach 90 or 95 percent. I want to walk through exactly how we set it up, because the way skills should be written has changed.
What’s the Difference Between Using AI and Building Around AI?
Most businesses have already identified a set of tasks they can hand over to AI. Maybe they’ve even created a few skills. That’s a good start, and it leaves most of the value on the table.
Full integration means treating AI like a senior hire. Think about the difference between onboarding a junior employee and onboarding a senior one. The junior employee needs step-by-step instructions for everything, while the senior employee just needs context and goals.
The newest AI models work the same way. Give them the context a senior hire would need, and they’ll figure out the best process on their own.
What Data Does AI Need About Your Business?
AI needs every hard fact about your business collected in one place it can read. That includes your services, your audience, your processes, and your point of view.
I recommend storing this information in a standard format like Google’s OKF and keeping it somewhere like GitHub, so it’s easy to plug into any AI tool you use. We build this for every one of our clients and call it their Brand Ambassador. I’ve written before about why an AI knowledge base is the first step for any business adopting AI.
How Do You Write AI Skills for Frontier Models?
A skill is a set of markdown files with instructions for AI. Anthropic released its Agent Skills format as an open standard, so a skill you build today is portable across AI platforms.
Here’s what’s changed. The newest frontier models need the same thing a senior hire needs, context and goals. When you write skills for them, provide all the context the model needs to work as a representative of your business.
In fact, spelling out exact procedures usually hamstrings the model. You end up preventing it from finding a better process than the one you documented. The model performs best with context around how your specific business operates and thinks.
Here’s how the two approaches compare:
| Step-by-step skills | Context-based skills | |
| Treats AI like | A junior employee | A senior team member |
| What you document | Exact procedures for each task | Goals, philosophy, and business context |
| What the AI does | Follows your checklist | Designs the best process for the goal |
| Main risk | Hamstrings smarter models | Requires thorough documentation up front |
Why Should Every Piece of Information Have One Home?
Every piece of information about your business should live in exactly one skill. Repeated information falls out of sync the moment something changes. In coding, this principle is called DRY, which stands for Don’t Repeat Yourself.
Here’s how that works in practice at our agency. Our team uses Notion for task management. So we have one comprehensive skill that covers where everything is, what every field means, and how our internal processes run inside Notion.
Anytime Claude needs to do something in Notion, it refers to that one skill. No other skill ever talks about where things are in Notion. If another skill needs that information, it just references the Notion skill.
Beyond tool-specific skills, AI also needs skills that capture how your business thinks. For our agency, that includes:
- A website optimization skill covering our general philosophy on SEO
- A skill about authority and how it impacts rankings
- A skill about keyword research and how to pick the right topics for content
Each of these captures context and philosophy, and the model figures out the process on its own.
Will AI Replace Your Team?
I don’t think so, and our agency is the test case. Even with AI doing most of the hands-on work, we’re still very people-heavy, and I expect we will be a year from now.
People tend to multiply the value produced by AI. The more value AI is producing, the more valuable the humans reviewing and directing it become.
We also hold a hard rule on quality. A task only goes to AI if AI can truly do it better than a person, and a faster result at lower quality stays with the team.
What Else Should You Know About AI Skills?
How long does it take to set up data and skills?
It takes a long time to document and organize everything, and it’s well worth it. The payoff compounds, because every new skill builds on the same data foundation.
Do skills only work with Claude?
No. Skills are plain markdown files, and the Agent Skills format is an open standard adopted by platforms including Microsoft and OpenAI. A skill you write once can travel with you between tools.
What should a skill never include?
Information that already lives in another skill. Repeating details across skills creates conflicting versions the moment something changes. Reference the original skill instead.
Where Should Your Business Start?
Start with the data. Document the hard facts about your business, then build skills that capture how you operate and think.
We build this exact system for our clients every day. Request a free digital marketing audit and we’ll show you where AI can do the most for your business.