Get inspired!

Most people don't get bad results from AI. They get random ones. Here's the six-part CROFTC structure that turns prompt roulette into consistent, reliable output.

The word covers four quite different things, and only one of them is an agent. Here is the plain version, plus the honest test for whether you need one at all.

Two thousand free workflows, and you have used none of them, because you cannot tell which are any good. Four questions to ask before you import a single one.

You have got good at prompting and the results still depend on you being in the room. There is a reason for that, and it is not that you need to prompt harder.

To a person, sending a discount to our best customers has an obvious limit sitting inside it. To a system it has none. Here is where to build the place it stops.

Forty things you could automate and no way to tell which one pays. Five worth an evening, three categories worth leaving alone, and the reasoning underneath both.

It answered instantly, confidently, and wrong. You do not fix that by wording the question better. You fix it by putting your documents in front of it.

It worked on Monday, it worked on Tuesday, and on Wednesday it did nothing and told you nothing. It did not break. Here is what actually happened.

Feature lists tell you what each one can do on a good day. What decides it is how they charge, how they handle branching, and where your data is allowed to live.

It sounds like programming and it is not. Four steps, one afternoon, and a seven-line instruction block you can fill in and paste straight into the builder.

Seven days, one small finished thing each day, about half an hour at a time. Nothing to install, nothing you can break, and something you keep using at the end of it.

You have bought a guide to a fast-moving tool before and found half of it wrong by the time you finished. Here is what I did to the pages, and what it cost.

Connectors and MCP sound like developer territory. There are two ideas here, and you only ever touch one of them. Here is the plain version, and the rule to follow first.

Automation does not go wrong loudly. It goes wrong quietly, a hundred times, before you look. One question sorts what is safe to leave running from what needs your hand.

You worked out the right way to ask, and three weeks later you are rebuilding the same brief from memory. A Skill is where that method goes so you stop retyping it.

Skills and Agents get sold as the advanced version of what you already do. They are not. They are four different amounts of your own thinking, saved in four different places.

Somebody showed you a small tool they made with AI and you assumed they knew something you do not. Here is what is really involved, and one you can finish this afternoon.

You solve something properly, close the tab, and the next day you explain your whole situation again. A Project is the room that ends that. Here is exactly what to put in it.

Both are good. They are good at different things. Here is the split by job, the honest weak spot on each side, and the reason switching does not stop your results changing every time.

Same question, two different answers. The reason is not the model, it is the brief. Here is the six-part checklist that makes Claude repeatable.

Ask a good question with no direction and you get the most average answer in the room. That's the default setting of every AI: safe, middle-of-the-road, technically fine, and forgettable.

The scary thing about AI isn't when it's wrong. It's when it's wrong and sounds completely sure of itself.

Most bad AI answers aren't wrong. They're answers to a question you didn't ask.

You ask a clear question and get back a dense grey paragraph you now have to mine for the actual answer. The information is in there somewhere.