Content

Most people imagine a skill is a really good prompt saved for reuse. It isn't — that's a template. Skills are qualitative automations: reasoning frameworks Claude applies to a class of situations. Here's what that actually means, why most attempts fail, and how skills get good through use.

If Claude retained nothing between sessions, what would you change about how you work? That question reframes how you think about projects — what good instructions actually contain, what good files look like, and where everything else needs to live instead.

The difference between Claude giving you generic output and Claude working inside your reality is context. Four layers of it — user preferences, memory, a voice profile, and context documents — each with a hands-on exercise you can do right now.

Everything I know about working with Claude well, I learned from working with Claude Code badly. After a month of duplicate work, lost decisions, and re-explaining things I'd already unpacked, I developed a three-part session protocol — orient, do bounded work, close the loop — that quietly compounds the quality of every conversation you have with AI.

I've learned Claude the way I've always learned technology — clicking on everything, experimenting, working through discovery. This is the first in a series that walks you through getting set up properly, from choosing the right model and plan to the features most people miss in their first ten minutes.

Everyone's wondering whether they picked the right AI tool or whether their people are good enough at prompting. The real question is whether AI knows enough about your organisation to be useful regardless of which tool you pick. For almost everyone I'm speaking to, the answer is no.

Does this tool have an AI connector? Can I configure it programmatically? These aren't on most people's checklists yet. They should be. I set up my entire CRM in two sessions — concept to configured and populated — and the experience changed how I think about choosing technology.

Most people waste entire chat threads correcting AI's wrong assumptions because they give directives without context—flip the pattern by asking the model what it needs to know first, and you'll spend less time fixing and more time thinking.

My father needed help with a wine spreadsheet. Just under a week later, he had a production web app with photo recognition and recipe generation — not because I planned it, but because one experiment kept leading to the next.

Organisations are trying to write AI policies before they understand what they're governing. You can't regulate chatbot access into safety — we've spent decades designing systems around human error, and AI is no different. Build AI into specific workflows where governance is structural, not instructional. Design the constraints into the system instead of expecting perfect behavior.

The gap between imagination and execution just collapsed—AI went from annoying assistant to 10x capability amplifier—but most people are still debating whether it's useful while others are already building.