Becoming a Claude Super User (in VS Code)

Seven habits that separate people who chat with AI from the few who run their business on it.

Most people use Claude like a search bar with manners. Type a one line prompt, receive an answer, ask a follow up, get more answers and move on. This mistake is costing you time, money and the AI’s efficiency all in one. Today, we’re going to talk about how to clean all of that up and become an AI Super User.

I run my company out of Claude inside VS Code. A workspace, with my whole business in it: the brand, the clients, the content calendar, the financials, the standing rules for how the work gets done. The difference between those two setups is the difference between a tool you visit and a system that runs with you.

Seven habits made the difference.

Organize the brain first

Before a single clever prompt, build the folder set (this is what makes up your local “Brain”). Everything Claude needs to act for me lives in files, not in my head and not buried in some old chat. One vault. A CLAUDE.md at the top that holds the standing rules: who I am, how I write, what I never do. A memory index for the facts worth keeping. One small state file that says where every project stands today, with the long history rotated into weekly logs so the current picture stays one page.

My sub folders include:

• Personal

  • Research (Podcasts + Articles I liked Transcribed & Summarized)

  • Finances, Budgeting & Taxes

  • Home School Resources

  • Gift Ideas & Reminders

• CBus AI Agents

  • Website Management

  • Task List (To Do)

  • Social Media Management

  • Clients (all Clients Folders & agents)

  • Agents (all Sub Agents)

  • Brand Guidelines

  • Contracts & Legal

  • Finance

  • Graphic Design

Inside of these folders exists the items I need to stay organized. That structure is the whole game, not housekeeping. A sharp model pointed at a messy ‘brain’ gives you sharp answers to the wrong context. Organize the inputs and every session after that starts smart instead of starting over. If you do one thing from this post, do this one.

Build skills for the work you repeat

A skill is a saved instruction, and the best ones run on code. When a task happens the same way every time, you should not pay a language model to reason through it again from scratch. You write the steps once as a script and let the skill call it.

My podcast audio runs through a processing script: same filter chain, same loudness target, every episode, no thinking required. My Saturday content batch, my investment research & planning, my site audits, the same. A script runs in a blink and costs next to nothing. A model reasoning through those same steps burns tokens and time for no extra value. Find the work you do every week. Turn it into a skill.

Hand the research to subagents

A subagent is a worker you spin up in its own context. It goes off, does a job, and reports back the conclusion instead of the mess.

Research is the obvious use. I send a subagent to read across forty files and it returns the two paragraphs that matter, while my main session stays clean. Research is only the start, though. Subagents also run several independent jobs at once instead of in a line: three content tracks, three workers, one wait instead of three. They take the heavy reads you do not want clogging your main thread, a stack of PDFs or a big spreadsheet. They give you a second set of eyes, a worker whose only job is to find the holes in what the first one produced. The main session stays focused. The grunt work happens somewhere else.

Becoming a prompting genius

Prompting is a skill, and it sharpens over time. When I sit with a client and they watch me work, the first difference they notice is how I talk to the AI.

The new user types one sentence. One task. Then waits. Then a follow-up, then waits again, one line at a time until the job is done. It is slow, it wastes effort, and worse, it wastes the intelligence sitting right in front of them.

Do the opposite. Write full paragraphs. Spell out what you want and how you want it. Name the parts you are unsure about and ask it to handle those. Give it bullet points, questions, the background and history, the context behind the request. Hand it the whole picture.

Feed it that and the AI goes to work on its own. It plans. It sends subagents out to fetch and research. It calls the skills it needs. It comes back with a fuller answer than any one-line prompt could pull. You finish in twenty minutes and three prompts instead of four hours and two hundred.

One session, one job

Open a session to start, work, and finish one thing. Then close it.

The fastest way to get slow is ten open tabs, each half-done, each carrying its own pile of context you reload every time you switch back. That drags on you and it drags on the model. Pick the task. Run it to done. Close it. Focus beats breadth here the same way it does in any shop. A session that knows exactly what it is for outperforms one that has been open since Tuesday and read everything you own.

Write it down in markdown

When a session ends, summarize it to a markdown file. Next time you need that work, you open the summary, not the entire session that chewed through PDFs, CSVs, and images to get there.

Few people build this habit, and it matters more than the flashy ones. Reloading a whole fat session is slow, it is expensive, and it makes the model dumber, because the one fact you need sits buried under a thousand lines of noise it has to wade through first. A clean markdown summary is the signal with the noise stripped out. You get a faster answer, a cheaper answer, and a smarter one. Document the session. Reference the document.

Why this matters to me

Three reasons, and they all point the same direction.

First, I believe pricing moves to a per-token model across the board. When you pay for what you use, waste stops being more than just time and turns into a costly line item. The efficient operator wins on cost without trying.

Second, this folder makes you system-agnostic. The vault is mine. The rules, the structure, the documented work, all of it lives in plain files I own. If Anthropic disappeared tomorrow, I point Grok or Gemini at the same folders and keep operating. That is leverage. While the labs fight a pricing war and leapfrog each other on intelligence every few months, I stay married to none of them. I own the system. They supply the engine.

Third, it makes the work faster. More output, less compute, more done before lunch. That compounds.

Train yourself right first

Now the part for anyone building a team. You set the standard by how you work, not by what you tell people. If the leader is sloppy with this, everyone downstream is sloppy with it, and you scale the mess.

Watch the metric you reward. Some companies started tracking token usage as a badge of honor, most tokens burned means most AI adoption, gold star for that person. It is a terrible measure and it backfires fast. Uber blew through its entire AI coding budget for the year by April. Reward consumption and you get consumption. The number that matters is output: the result delivered, the hours saved. The operator who produces the most with the fewest tokens is the one winning, and a usage leaderboard will never show you who that is.

Become the super user first. Then hand the framework to your team already knowing it works.

Jeff Binek runs CBus AI Agents in Dublin, Ohio, building AI systems for small businesses across Central Ohio. More at cbusaiagents.com.