Something you’d ask one of your best employees.

Sadly, the result was mediocre. Most people would think the tool failed them.

Here’s what actually happened: you compared a day-one intern to your best employee.

The fix isn’t a smarter model or a magic prompt. It’s a system that points AI at the parts that slow down your business.

I built an AI system for my business (mostly with Claude). Thanks to it, my small team handles work that would normally take twice as many people.

Below are the rules I follow to use AI in business:

• You don’t need to become “an AI business”

• Point AI at one real task (not your whole job)

• Set aside a few hours for a meeting with AI (make it do the middle 80%)

• Train AI like a new employee

• How to write the instructions for your AI employee

After weeks, I finally cracked how to make Claude control my iPhone (and do tasks for me 24/7)

The full step-by-step guide drops next week — exclusive for paid subscribers. If you’re getting value from Claude, consider becoming a paid subscriber today (you’ll get full access to my best guides)👇

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1. You don’t need to become “an AI business”

Nobody calls themselves an internet business anymore.

You just use the internet to sell, deliver, and talk to people, like everyone else.

AI is heading the exact same way. Your work doesn’t have to become an AI thing. You just need to use AI inside it.

The people quietly winning with AI right now aren’t running futuristic robot companies. They’re running boring, normal businesses (a local service, a small consultancy, a paid community) and using AI inside them to do more with a smaller team.

My team is a good example of this. Thanks to Claude, we can handle work that would've taken at least twice as many people.

But you shouldn’t only “use AI.” Many people now use AI.

This is becoming more like knowing how to use Word or Excel: useful, yes, but no longer impressive. The edge comes from how you use it and what you can actually produce with it.

This leads us to the next point.

2. Point AI at one real task (not your whole job)

I've been obsessed with AI since the ChatGPT release. So trust me — get one task done successfully with AI, and you'll want to hand everything off to it asap.

Don’t make that mistake!

Here’s a lesson I learned from Dan Martell.

First, ask yourself: Where is your work actually stuck?

Every workflow has a chain. For a business it looks something like:

Reach → Leads → Sales → Delivery → Retention

For your job, the chain might be:

Requests → Triage → Work → Delivery → Follow-up

Now ask yourself: if I 10x this task tomorrow, would my revenue actually go up?

The stage where the answer is a clear “yes” is your bottleneck. That’s where AI goes first. Not where AI looks coolest, where you’re stuck.

Here’s an example: I run a newsletter about AI (this one). There are dozens of tasks I could automate, but, believe it or not, generating articles with AI wasn’t at the top of my list. My bottleneck was reach, so I built automations to republish my free articles on other platforms and also adapt them into short posts for social media. Thanks to that, new readers found my newsletter and subscribed. Things have been great since then :)

Again, resist the urge to automate your entire job in one weekend. Start with one task.

If you don’t know where to start, write down what you actually do in a day and try this:

Here’s a list of my daily activities: [paste your list].

Which one of these can I use AI for, and which tool should I use? Walk me through it step by step.

For most tasks, Claude Cowork will be the solution.

We’ve built ready-made automations with Cowork: an invoice tracker that scans your inbox and flags who hasn’t paid, a prompt that turns any meeting transcript into action items (with owners and deadlines), a downloads-folder organizer.

Copy, paste, done.

Here’s what the invoice tracker gives you after one run:

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3. Set aside a few hours for a meeting with AI (make it do the middle 80%)

Whether you have a small or big team, remember this: You should be the first to automate your workflows with AI, not the last.

Why? Because you know the business more than anyone else. You might not be technical, but you can spot the bottleneck, define what success looks like, and what’s actually worth your team’s time

Once you’ve automated one of your own tasks, you understand how this stuff actually works, and explaining it to others becomes easy.

Also, you’re not being the boss who says “use AI” without ever having done it.

You’re leading by example.

I’ve done this myself. Every day, I set aside a few hours to spot what tasks can be automated with Claude. Once I know what to automate, I do this:

• I ask Claude Cowork whether it can handle that task (and how)

• I go back and forth with Claude until we’re on the same page

• Once I’m happy with Claude’s plan, I tell it to execute it

• I hit edge cases during execution. That’s fine. I don’t give up until the task is done

• Once AI succeeds, I turn the chat into a skill or a scheduled task

You don’t have to do this every day (as I do). Set a few hours per week.

Protect it like a meeting with your most important client, because that’s what it is.

One warning before you automate everything that moves: some work should stay yours. Two things you shouldn’t use AI for:

1. making critical decisions

2. building relationships

That said, you can still automate everything around those two!

Usually, when we say “automation,” we picture autopilot mode (the tasks Claude runs while we’re sleeping), but, at least for me, automation can also mean collaboration mode (keeping a human in the loop).

Collaboration mode follows the 10-80-10 rule.

The first 10% is yours: the ideas, the direction, and the definition of done. Talk to AI about what you’re trying to accomplish, give it your expertise, and agree on what a finished result looks like. This 10% is your building stage. It might seem small, but it's the foundation everything else rests on.

The middle 80% is AI’s: the execution. The drafting, building, formatting, organizing. The part that takes hours but doesn’t need your taste.

The final 10% is yours again: the review, the personal touch, the judgment call before it ships.

In practice, here’s how this works for my team: whenever a workflow needs human judgment, I assign one person to it. Sometimes I split an automation into two phases, so their phase-1 decisions improve the phase-2 output. Other times, they only weigh in at the very end, right before it reaches the client.

You stay the author. AI does the typing.

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4. Train AI like a new employee

Rome wasn’t built in a day.

If you want to make the most out of AI, you should treat it like a new hire.

You don’t expect a new employee to perform as well as your top people on day one. You ramp them up. You give them context, feedback, and time to learn how you work.

AI is no different.

If you want AI to perform at its best, do what you did with your team on day one.

Give AI:

1. The way you work (guides, SOPs, etc)

2. The templates you use

3. and any document you’ll give to a new hire

The good news? AI learns faster. It can swallow a ton of information at once.

Over time, your SOPs will become Claude skills, and Claude will fill in your templates. With this, AI will be able to handle many of your tasks in one shot.

But don’t expect that to happen on day 1.

To get there, you’ll need time to learn from your own automation and connect the dots.

Here’s the setup I use to train Claude like a new employee.

I create a Claude project for every major task I have. Inside a project, I give access to a folder that contains my onboarding files (md files, templates, etc)

Here’s what this looks:

However, my workspace only looks like that at the end (after weeks of building).

Here’s my typical journey:

• Day 1: Things are messy. I gather the files, but my workspace isn’t tidy

• Week 1: The automation is done, but it’s still not the most optimal solution (doesn’t follow the best practices, not token-efficient, etc)

• Month 1: I optimize the automation, starting from those that are critical

On day one, my AI employee can barely do a thing. By the end of week one, it’s getting real work done (sometimes in one shot), but it’s not yet at its peak. It’s only after weeks of trial and error and connecting the dots that I get its best version.

Why do I work like this?

When I build systems, I don’t care if it follows the best practices. My main goal is to build something that works reliably.

I know this isn’t the right way to do things. However, I prefer to occasionally fix a few things on the fly, rather than get stuck trying to make everything perfect from the start.

Is all this training worth it?

Of course! Just look at customer support.

AI answers questions straight from the help docs. When it gets one wrong, a human steps in, answers the customer, and then fixes the docs. Next time that question comes in, AI nails it.

Every fix makes the system permanently smarter.

Bonus: How to write the instructions for your AI employee

A new hire can’t read your mind. Neither can AI.

You need to break your tasks down into steps and translate your decisions and even your intuition into clear instructions.

Write them down, including your if-this-then-that branches (”if the client replies in under an hour, I prioritize them”).

You could ask Claude: “Which of these steps can an AI do without me?”

The answer will most likely involve steps that don’t need human judgment.

Pro tip: Instead of writing down the steps, record yourself explaining the task (what you're doing and why), then hand the transcript to AI. I do this with the iPhone's Voice Memos app, which generates a transcript automatically. I haven't tried video yet, since Claude can't watch videos. Google Gemini can, though, so I might give that a shot soon (or find a way to connect them)

One more thing

Right now, the distance between people who can hand real work to AI and people who poke at it like a toy is enormous. Everything in this guide is something you can start this week: pick one bottleneck, automate one task, write one context file.

One task. Start small. That’s the whole ask.

If you want the full path (from your first real prompt to systems that run themselves), consider becoming a paid subscriber. You’ll get every deep-dive guide I publish, and your future self, the one who stopped doing the boring 80%, says thanks.

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Click here to get my FREE Claude course (20+ lessons)

A year ago, there were barely any guides or video tutorials for Claude.

Today it’s the opposite. You open Google and find dozens of guides and Claude video courses. You watch ten minutes of one, feel a little lost, and close the tab.

A week later, you’re exactly where you started.

I’ve been there, so I did the digging for you. I went through more than 20 free courses and threw out the noise. Then I sorted what’s left by the way you actually like to learn.

Here are the best Claude courses.

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If you like step-by-step guides

If you’re the kind of person who likes to read, follow along, and do it yourself at your own pace, this one’s for you.

The course is split in 3 levels, so you don’t drown trying to learn everything at once. Think of it like a video game: you finish level 1 before you unlock level 2.

Here’s what the levels are about:

Level 1: Claude chat

This is the regular Claude you talk to, the one that looks a lot like ChatGPT.

Most people stop here and never get past the chatbot stage.

You’ll learn Claude features, how to set it up properly, and how to create a simple file that tells Claude who you are and how you work, so you stop re-explaining yourself every single time.

Once you do that, you’re already ahead of most users who just open a blank chat and start typing.

Level 2: Claude Cowork

This is where Claude stops talking and starts doing.

Cowork lives in the desktop app. The big difference: ChatGPT gives you text you have to copy and paste somewhere else, while Cowork creates the actual files for you (the document, the spreadsheet, the styling, all of it).

In level 2, you’ll learn how to set up Cowork, build your first skill, run tasks from your phone, turn Claude into something close to a 24/7 employee and more! For most non-technical people, this is the level that changes how you work.

Level 3: Claude Code

Claude Code is Claude Cowork on steroids.

They made Cowork to break the “I’m afraid of coding” barrier. But once you start hitting Cowork’s limits, Claude Code is how you break through them. And no, you don’t need to know how to code (the name scares people off, that’s all).

Level 3 covers why Claude Code is worth it, what to learn first, how skills work, claude.md and more. For this particular level, I haven’t covered everything yet, so you’ll need to take the Claude Code course below for a deep dive.

You don’t have to reach Level 3 to get a ton of value. But if you want to break Cowork’s limits, this is the door.

How to get my free Claude course

If you’re not a subscriber, click here to get my free Claude course (or leave your email below). After you subscribe, you’ll get a welcome email with the link to the course.

If you’re already a subscriber (and can’t find the welcome email), DM “free“ on Instagram or LinkedIn, and I’ll send you the link (you could leave a comment here, but I don’t get notifications, so I might lose your message)

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P.S. Levels 1, 2, and 3 are free for everyone, but some guides I publish weekly are exclusive to paid subscribers. Support us by becoming a paid subscriber.

If you like video tutorials

Some people learn better by watching someone do it on screen.

Here are three free YouTube courses I’d actually recommend. Each one covers a different piece of the Claude puzzle.

Claude Cowork 60 Minute Masterclass (Sabrina Ramonov)

If you only watch one video, make it this one.

If you want one video to understand Cowork, this is it. Sabrina sets up the Claude desktop app from scratch, then runs five real use cases live: organizing a messy files folder, building an email assistant, creating on-brand content, and automating social media. It pairs perfectly with my Level 2 above.

Watch it, then go build something.

Key timestamps:

• 00:42 Download and install Claude Desktop + interface tour

• 02:10 Chat mode vs Cowork mode vs Claude Code (the difference that confuses everyone)

• 09:48 Skills explained (reusable AI playbooks)

• 19:00 Use case: organizing a messy local files folder

• 30:28 Use case: a personal email assistant with Gmail and Calendar

• 39:05 Use case: creating on-brand marketing content

Watch it here:

Claude Code Full Course, 4 Hours (Nick Saraev)

This one is for people who want to go all in. This is an end-to-end beginner course that takes you from installing Claude Code all the way to shipping (and selling) a real project. Nick covers setup, using the CLAUDE.md file as your project’s brain, building your first web app, then the power-user stuff: skills, sub-agents, and git worktrees to run hours of work in minutes.

Save this one for after you’re comfortable with Levels 1 and 2.

What it covers:

• 03:44 Setting up and installing Claude Code

• 24:01 Building your first web app with Claude Code

• 30:29 Using the CLAUDE.md file as your project brain

• 54:42 Advanced features (hooks, slash commands, the .claude directory)

• 2:35:29 Creating skills to work faster

• 3:11:08 Sub-agents and agent teams (Claude working in parallel)

Watch it here:

Bonus: Claude Design 2 Hour Course (Nate Herk)

Claude can also design. This 2-hour course takes you from zero to shipping real work with Claude Design, the kind of thing you’d normally use a tool like Canva for. Nate builds a full brand called Tally from scratch, so you watch one project come together start to finish.

If you create visuals, slides, or websites for work, this is worth the watch.

What it covers:

• 00:00 What Claude Design actually is

• 11:33 Creating the design system

• 19:50 Building a pitch deck

• 40:45 Building a high-fidelity website

• 57:51 A mobile app prototype

• 1:42:01 Pushing the site live with GitHub and Vercel

Watch it here:

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If you want courses straight from Anthropic

Here’s something a lot of people don’t know: Anthropic, the company that makes Claude, has its own free academy with certified courses taught by the people who actually built Claude.

They’re free and you can get a certificate for LinkedIn.

→ Link to Anthropic courses: anthropic.skilljar.com

Here are the best courses they offer:

Claude 101

If you take only one course off this entire list, take this one.

Claude 101 covers features such as projects, artifacts, skills, connectors, enterprise search, and research mode. These are the features that turn Claude from a chatbot you talk to into something you build real workflows inside. It’s broken into short modules, with use-case breakdowns by role at the end (marketers, writers, analysts, operations). Takes about 1 to 2 hours.

AI Fluency: Framework and Foundations

This course is a way of thinking about how to work with AI effectively and responsibly.

The whole thing is built around four ideas they call the four D’s:

• Delegation: knowing when to hand a task to AI and when to do it yourself

• Description: clearly telling the AI what you actually want

• Discernment: judging whether the output is any good before you use it

• Diligence: the responsibility you still carry even when AI did the work

If you manage a team or you’re the person deciding how AI gets used at your company, this course will shift how you think about it more than any feature tutorial.

The AI Fluency spin-offs: Educators, students, teaching

Anthropic took that same fluency framework and made three more versions for specific groups:

• AI Fluency for Educators: for teachers and anyone designing courses, applied to curriculum design and assessments

• AI Fluency for Students: the framework reframed around studying, learning, and career planning

• Teaching AI Fluency: for people whose actual job is teaching AI fluency to others, like trainers and L&D teams

The advanced track

Here are some courses recommended for those who want to build with Claude

• Claude Code 101: how Claude’s command-line tool fits into a daily coding workflow

• Claude Code in Action: it’s 15 short lectures on real development tasks — navigating a codebase you’ve never seen, planning a feature before writing it, etc

• Introduction to MCP: how to connect Claude to your own tools, files, and databases

One more thing

You don’t need 20 courses.

You need the right one for where you are right now.

And if you want a course that keeps updating, subscribe to Artificial Corner 👇 I release new Claude guides every week.

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Let’s connect! You can find me on LinkedIn, Instagram and X

Most people think you need a technical background to understand AI.

You don’t.

And you don’t even need to read every research paper. You just need someone to explain what’s actually happening, in plain English.

I read the most relevant AI research papers, so you don’t have to.

I pull out the concepts that actually matter for you as a Claude or ChatGPT user and break them down in a way anyone can follow.

In this guide, you’ll learn:

• How AI reads your prompt, and why your long chats start to fall apart

• Why training data isn’t enough

• Why AI sounds polite, why it agrees too easily, and how to fix it

• Why giving AI more time to think leads to better answers

• What an AI agent really is (and why you need them in 2026)

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1. How AI reads your prompt, and why long chats start to fall apart

As you read this, you go word by word, from left to right.

AI doesn’t work that way.

When you send a message, the model looks at everything at once. It compares words with other words, phrases with phrases, your instructions with your examples, and figures out which parts are connected.

Before this worked the way it does now, systems had to process text in order, one piece at a time. That made it hard to connect ideas that were far apart.

The model that changed all of this is called a Transformer. Instead of reading from start to finish, it looks at many relationships in the text at the same time.

In the past, AI processed language like a slow reader: one word at a time (using RNNs). Transformers made this process faster.

But this comes with a cost.

Comparing everything with everything is expensive. If you double the length of your message, the work almost quadruples. That’s why every AI tool has a limit on how much text it can process at once. This is what people call the context window.

The context window is everything the AI can “see” in an active conversation: your messages, its responses, and the files you uploaded.

When it fills up, it starts losing access to what was said earlier. No warning. It just forgets.

So what do you do?

Put your most important instructions at the beginning of a long prompt (or repeat them at the end). And if the conversation has already gone on too long, start a new one with a quick summary of what was decided.

The goal: keep the context clean. Noise takes up space too, and that affects the quality of the answers.

👉 If you’re curious, the paper is here

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2. Why AI training data isn’t enough

Read more

Click here to get my FREE Claude course (20+ lessons)

Most people who want to make money with Claude start in the exact same place. They try to build an app.

They watch someone vibe-code a tool in an afternoon, think “I could sell that,” and spend the next three weekends fighting bugs for a product nobody ends up buying.

Here’s the uncomfortable part: the vast majority of vibe-coded apps never make a single sale.

The people actually paying their rent with Claude are doing something far less exciting. They sell a service, and let Claude do most of the work behind it.

You don’t need to code
You don’t need to be an AI expert
You don’t even need a brand-new idea

You need to be useful to someone who’s willing to pay.

In this guide, you’ll learn:

• The one mindset shift that separates people who earn with Claude from the rest

• Why a service beats an app (almost) every time

• 5 ways people are actually getting paid with Claude right now

• How to turn a single client project into income that repeats

• How to land your first clients (even starting from zero)

Consider becoming a paid subscriber to get full access to all my guides (+ these perks) 👇

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You’re the CEO. Claude is Your Team

Let’s start with the mistake, because almost everyone makes it.

People treat Claude like a magic button. They expect to type one prompt, get a finished business, and watch the money roll in.

Here’s a better way to think about it.

You are the CEO. Claude is your worker.

Actually, it’s a whole team of workers. It can research, write, design, crunch numbers, and handle the boring parts of a job.

But it still needs someone with a vision telling it what to do and deciding what’s good enough to send to a client.

No amount of AI saves a bad leader. That part is still on you.

And here’s the best part:

You don’t have to be technical or an expert in AI.

The most important thing is to be good enough at what you do to tell good output from bad.

I know that sounds too good to be true, but two years ago, almost nobody knew what Claude was (I signed up and never used it!) Everyone you see teaching this stuff today (me included) started from zero a few months/years ago

At the end of the day, people don’t pay for your credentials. They pay for results. If you can deliver the result, and Claude helps you deliver it faster, you’re already in business.

Click here to get my FREE Claude course (20+ lessons)

Stop building apps. Sell a service

Before picking what to sell, let’s kill the most common bad ideas, because chasing the wrong model is how people waste months.

A good way to make money with AI should be cheap to start, have healthy margins, get you to real income relatively fast, and still work a year from now.

Unless you have thousands of followers, I wouldn’t recommend:

• Vibe-coding apps: high margins on paper, but getting actual users is brutal, and someone can copy you next week.

• AI dropshipping: high ad costs, thin margins, a race to the bottom.

• Faceless YouTube channels: everyone uses the same tools, so nothing makes you stand out (and you’re not building anything that’s really yours).

• Cheap templates: easy to make, easy for anyone else to clone

Notice the pattern? They all either cost a lot up front or are trivially easy to copy.

Now the one that quietly wins: selling a service powered by AI.

The math is simple. Startup cost is basically zero, because you sell the service first and deliver it after. Margins are high, because Claude does most of the work instead of a team of employees. And if you wrap it in a personal brand, it gets more durable over time, not less.

My rule of thumb: sell first, build later.

You’re not committing to building anything. You’re making a promise (”I can do this for you, here’s the price”) and only doing the work once someone says yes.

And please, don’t overthink your offer.

Some people spend dozens of hours agonizing over the perfect offer and pricing, then a few hours learning Claude, then almost no time actually selling.

That’s backwards.

Spend around 30 minutes on your offer. Pick something, slap a price on it, and move.

You’ll change it anyway once the market tells you what it’ll pay (you might find out you were charging too little).

If you’re staring at a blank page, use a simple fit test to decide what to sell. Look for the overlap of 3 things:

1. What you love doing

2. What you’re actually good at

3. What people will pay for

The sweet spot is where all 3 meet. That’s your offer.

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5 ways people are getting paid with Claude

Here are the ones that work right now. Pick the one closest to what you already know. The ones needing deeper Claude knowledge are at the end.

1. Sell a skill you already have, powered by Claude

Copywriting, SEO, marketing, thumbnails, admin work, social media, etc.

These services existed long before AI. The difference now is that Claude does 80-90% of the work, so you can charge a normal rate and keep most of it instead of hiring a team. You don’t need a new skill. You need to do an old one faster.

2. Teach Claude one-on-one, to teams, or small businesses

Everyone wants to learn Claude right now. You offer training for a team, set a price, set a length, list the topics. It can be a workshop, one-on-one coaching, a course, or a paid community. Pick a format and go.

Do you need to be an expert?

Actually, no. You just need to:

• Learn Claude well enough to teach the basics

• Have built something where you actually applied those basics

With that, you can sell training, coaching, or a small course to people and teams who feel behind.

“Teaching“sounds like something exclusive to educators or AI experts, but it’s not!

Believe me, you only need to be a few steps ahead of your students, and that’s enough.

3. Fix the boring data nobody wants to touch

As a data analyst, I can tell you this — most companies sit on a mess: spreadsheets everywhere, old systems that don’t talk to each other.

And you can’t put AI on top of broken data. It just doesn’t work. So someone has to clean it up first.

The solution? You connect Claude to their data and do the cleaning so they can finally ask questions in plain English and get real insights. Here, the biggest opportunity is with companies that are comfortable sharing data with AI (mostly small businesses).

4. Be the one-person agency

Not long ago, helping a business launch meant hiring a designer, a copywriter, a developer and other professionals.

Now one person with Claude can do almost everything: a landing page, an email funnel, a simple chatbot, and the ad campaigns.

I put this one almost at the end because it’ll require you to create systems that run without your intervention. Your work? Feed the system, push back when Claude sounds off, and tell good output from bad.

You might still need to hire a freelancer to pick the best of what AI produces (for fields you have little knowledge), but it costs a fraction of what you’d have paid before.

5. Become an AI consultant for small businesses

Every small business owner in your town is quietly panicking about AI. They have no idea where to start.

So you walk in, spend a couple of hours understanding their business, feed it all into Claude, and hand them a clear roadmap: the top moves ranked by what makes them the most money, plus a 30-day plan.

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How to land your first clients

This is probably the most important part.

You can have the best offer in the world and still make nothing if nobody knows you exist. There are two ways to fix that, and they work best together.

Way one: cold outreach.

This is the best path to cash in the short term.

You reach out directly to people who might need your service. Go find people who are clearly interested (anyone commenting “teach me Claude” under a popular post is a warm lead) and message them. But don’t open with “buy my thing.” Lead with value. Start a real conversation, and you can mention your paid help at the end.

Do this by hand at first. Don’t automate it before you know what actually works. Test your messages. Try one version on your first batch, a different version on the next, and keep whatever gets replies.

You can also reach out to businesses (and Claude does the hunting)

Here’s one idea: pull business data from Google Maps. Point Claude at Google Maps or a directory and have it build you a clean spreadsheet of local businesses, with names and contact info, ready to reach out to.

Lead gen that used to take a full day now takes minutes.

Way two: post content so clients come to you.

This is the long game, and it’s the one I’m most bullish on.

If you can build even a small audience around Claude, it sets you up for the rest of your career, whatever you decide to sell next. You post about what you’re learning, people see you as “the AI person,” and some of them start DMing you to work together.

But here’s an honest take. Getting views on social media is not easy.

If I had to start over, I’d avoid YouTube, LinkedIn, and Instagram. They’re hard (even for me now!). The best platforms for new creators are TikTok and my recent discovery, Threads. Both still give you views regardless of how many followers you have. I started with 0 followers one week ago on Threads, and now I have almost 1k followers (all organic)

Here’s one idea (that works for me): Find content that’s already going viral about Claude/AI and make your own version. Copy the structure, the hook, and then say it in your own words.

Pro tip: Turn one project into income that repeats

Landing a client is great. But one-time projects mean you’re always hunting for the next one. The real goal is income that repeats.

The first move is simple: don’t end the relationship when the project ends.

After you deliver, stay on as their ongoing “AI advisor.”

For an audit client, that might be a few thousand a month to keep their roadmap fresh. For a tool you built, it might be a smaller monthly fee to maintain it and add features.

The second move is the one that quietly changes everything: productize your work.

Every project you finish has reusable parts: the template, the system, the setup. Polish those parts, record a simple walk-through, and sell it as a product. You built it once, for a paying client. Now you sell it again and again to people with the same problem. Of course, you won’t always be able to sell it as is. You might need to adapt it to each business context, but you’re not starting from scratch anymore.

One more thing

Don’t quit on day three. Most people look for clients or post once, hear crickets, and give up. The ones who win just keep showing up. Give it a real shot, say the next 90 days, before you decide it doesn’t work.

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They think the next model, the next AI tool, the next setup is the thing standing between them and real results.

Actually, they have a skill problem.

The people getting incredible output from AI aren’t using secret tools. They’re using the same Claude and ChatGPT as everyone else. They’ve just gotten good at a handful of skills that the tools can’t give you.

Those skills are what this guide is about. Seven of them, from beginner to pro.

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Four years ago, I built a simple Python model to predict the 2022 World Cup.

I wrote every line of that old model myself. I didn’t build a sophisticated model, but the project still took me weeks.

This time, I didn’t write a single line of code. I told Claude what I wanted in plain English, and it pulled the data, built a smarter model, tested it on the last two World Cups, and even made the charts you’re about to see.

I just made the calls on what to build.

Here's why this matters to you, even if you think soccer is the most boring thing on earth. Swap "49,000 soccer matches" for "five years of your bank statements" or "that sales export you've never had the time to open." Same machine.

The hard part used to be writing the code. Now it’s knowing what to ask for, stress-testing a plan, pushing back, and double-checking the outputs.

The World Cup is just a fun way to show you how that works.

Oh, and one more thing: this is the biggest World Cup ever. 48 teams, kicking off June 11th across the US, Canada, and Mexico (the US is hosting 78 of the 104 games!).

Let’s predict the winner with Claude.

In this guide, you’ll get:

• How Claude’s prediction model works (in plain English)

• The favorites, the most likely bracket, and the winner

• What this Claude project means for you (even if you hate soccer)

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How Claude’s prediction model works

Here’s what I had Claude do, in four steps.

1. Every game, not just World Cup matches: I had Claude pull every men’s international since 1872. That’s about 49,000 matches, including all the friendlies and qualifiers my old model skipped. Recent games count more and older ones fade out (a game from last month matters way more than one from 2014).

2. Rate every team: I asked Claude to give each team a strength score using something called Elo. If you’ve ever played chess online, you’ve seen it: win and your number goes up, lose to someone weaker and it drops a lot. Same idea, just for national teams.

3. Turn the ratings into a score: Claude turns the gap between two teams into a likely scoreline, with a bonus for the three hosts playing at home.

4. Play the World Cup 50,000 times: Instead of guessing one bracket, I had Claude play the entire tournament 50,000 times and count how often each team won. That’s where the odds come from.

If you want to dive into the more technical details, I left it all in the "Under the hood" section at the end.

I tested it on the World Cups 2018 and 2022 first

Before trusting the model, I had Claude run it on the last two World Cups, using only the data that existed before each one kicked off.

2022 went well. The model made Argentina a co-favorite, second only to Brazil, and Argentina won. My old model’s one and only pick was Brazil, who went out in the quarters. Having the real winner sitting near the top is a much more useful answer.

2018 was a miss. Why? The model loved Brazil and Germany. France won. It had France down in fifth.

Here’s the thing, though: soccer is genuinely hard to predict. Across all those games, the model called the right result about 52% of the time, which is actually decent for a sport with this many upsets. The new model isn’t trying to be magic. It’s trying to give honest odds instead of one overconfident guess.

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The favorites

Two teams stand above everyone else.

Spain comes out on top at about 28%, with Argentina right behind at 21%. Together, that’s almost half the field’s chances sitting with just two teams. Then there’s a cliff. France is the best of the rest at 11%, and after that, England, Brazil, Colombia, and Portugal are all bunched in the mid-single digits.

Here’s the full top 10, with the odds of winning it all and of going deep into the tournament:

The bookies also have Spain on top, so we agree on the headline.

The difference is they spread the love around more and rate France and England higher than I do (well, than Claude does). My model is just more stubborn about Spain and Argentina, because on recent results those two have been ruthless.

One more way to look at it. The darker the box, the more likely a team makes it to that round:

The most likely bracket (and the winner)

Put it all together and you get the single most likely path through the World Cup.

The model sends Spain and Argentina to the final, with France and England losing in the semis. And the team it has lifting the trophy at MetLife Stadium on July 19?

Spain

Will it play out exactly like this? Almost certainly not (that’s the whole reason I ran it 50,000 times instead of once). One bracket is just the most likely story out of millions.

What this means for you (even if you hate soccer)

Here are a few lessons to keep in mind for your next Claude project:

1. Your messy data is finally useful

The hardest part of a data project always used to be the data. Here, Claude chewed through 49,000 matches without blinking. For you that pile could be five years of bank statements, a folder of PDFs, or a sales export you’ve never had the time to open.

Point Claude at it, ask it to clean, generate charts, etc. I did this using Cowork, but you can also use Claude in Excel.

2. Stay in the driver’s seat

This wasn’t me firing one prompt and walking away.

Before Claude built anything, I made it show me the exact data it pulled, double-checked it myself, and grilled it on one specific choice: should a game from 1954 count as much as one from last year?

Only once I liked the answer did I tell it to keep going.

That back-and-forth is the actual job now. You supervise, you question the parts that smell off, and you approve the next step. Claude does the building, you make the calls (this beats someone who just types “predict the World Cup” and hits enter).

3. I got lucky with the data. You might not

Honest caveat. This went smoothly partly because a clean dataset was already sitting on Kaggle, ready to download. A lot of real tasks won’t be that tidy. The numbers you want might be scattered across a few websites. Ask Claude to go scrape them for you. Don’t expect every data project to be this clean. Expect to wrangle.

4. Ask Claude where it’s weak

My favorite one. I didn’t have to hunt for the flaws myself. I just asked Claude to test its own work, and it told me straight: it would have missed the 2018 winner, and it disagrees with the bookmakers more than I’d like.

That honesty is there, but you have to ask for it. On your next project, don’t stop at the polished answer. Ask “where is this wrong?” and “what are the weak spots?” A good answer that knows its limits beats a confident one that’s hiding them (you can even bake that habit into a skill)

Your turn: can you beat Claude?

The fun part starts now.

Claude’s model says Spain. But we both know soccer’s whole personality is making models and analysts look silly. It happens almost every tournament.

So I want the human take. Yours.

Who's lifting the trophy? Drop your pick in the comments (and throw in the final score if you're feeling brave).

On July 19th, we’ll see who called it better: the model, or you.

Leave a comment

One more thing

Anyone can type “predict the World Cup” and hit enter.

Getting a result you’d actually trust is a different thing, and that’s the part I keep digging into. How to point Claude at your own messy data, question the parts that smell off, and walk away with something ready to use instead of slop.

That’s what I share with paid subscribers.

Not my soccer takes :) but step-by-step guides that will help you unlock Claude’s full potential for your own work. If you want to get access to all my guides, consider becoming a paid subscriber 👇

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Let’s connect! You can find me on LinkedIn, Instagram and X

P.S. Here’s how the model works under the hood

Bonus: How the model actually works (skip this if you’re not technical)

Here are the same four steps again, this time with the real math, a few actual numbers, and a bit of the code Claude wrote. My old 2022 model leaned on one idea: the Poisson formula, a simple way to turn an average number of goals into the odds of each scoreline. I kept that part (it still works). I just fixed what feeds into it.

1. Strength, with Elo

Every team starts at 1500 points. After each game the winner takes points from the loser, and how many depends on how surprising the result was, the winning margin, and how big the match was (a World Cup game counts for more than a friendly).

The expected result before kickoff is one line:

expected = 1 / (1 + 10 ** ((opponent_rating - team_rating) / 400))

Then both ratings update:

k = importance * margin_bonus          # bigger game + bigger win = bigger swing
elo_home += k * (result - expected)    # result = 1 win, 0.5 draw, 0 loss
elo_away -= k * (result - expected)

Old games fade on their own here, because every new result writes over the old rating.

Run that across all 49,000 matches and you get a strength number for every nation.

Here’s the top of the table going into 2026:

No big surprises at the top, which is a good sign the ratings aren’t junk.

2. Recent games count more

This is the fix for my old model treating 1954 like yesterday. When the model learns how a rating gap turns into goals, it leans on recent matches and lets old ones fade, cutting their weight in half every 2.5 years. A game from the 2022 World Cup is worth about 0.4 of a brand-new one, a 2018 game about 0.1, and anything before 2010 is basically zero. I didn’t pick that 2.5-year number by feel. I had Claude test a whole range of values and keep the one that predicted past results best.

3. From two ratings to a scoreline

Two ratings go in, a likely score comes out. Claude fit a small formula on those recent games that turns the rating gap into an expected number of goals for each team:

expected_goals = exp(0.17 + 0.19 * (rating_gap / 100) + 0.17 * home)

Feed both teams’ expected goals into the Poisson formula (the same one from my 2022 article) and you get the odds of every scoreline: 1–0, 2–1, 0–0, and so on. Add them up and you get the win, draw, and loss chances.

Spain vs Brazil on a neutral field, for example, lands around Spain 53%, draw 25%, Brazil 22%.

4. Play it 50,000 times

This is the real upgrade over 2022. For each simulated tournament, Claude:

• plays all 72 group games by rolling actual scorelines, then ranks each group on points, goal difference, and goals scored (the real World Cup tiebreakers)

• takes the 8 best third-place teams and drops them into the bracket using FIFA’s official rules for the new 48-team format

• plays the knockouts, where a tie goes to extra time and penalties, so the stronger team gets a small edge instead of a pure coin flip

Run that 50,000 times, count how often each team lifts the trophy, and those counts become the title odds.

That’s the whole machine. Strength, recency, a scoreline formula, and a lot of simulated tournaments.

This guide was written in collaboration with Gencay.

Gencay writes LearnAIWithMe, a newsletter focused on practical AI tutorials for people who’d rather replace than be replaced by AI.

In a previous guide, I showed you how to connect NotebookLM with Claude.

It worked, but there was a catch: the whole setup lived in the terminal. CLI, install commands, config. Powerful, but for most people, too technical.

This time, there’s no terminal (or Claude Code).

I found a NotebookLM skill that runs inside Claude Cowork. You only need to download it and add it to Cowork.

In this guide, you’ll learn:

• How to set it up in two steps

• Use case 1: Interview prep that knows a company’s last twelve months

• Use case 2: A weekly language feedback loop that drills your real mistakes

• Use case 3: Pre-trip destination briefings grounded in fresh sources

Use case 2 is connected to my Claude for Foreign Language Learning video course, while use case 3 is related to the AI Travel Guide I published last week.

LearnAIWithMe

If you want to build with AI, not just learn how to prompt, this is the right place.

By Gencay

The Claude Skill

This skill was created by Paul J Lipsky.

Download the skill clicking here. Then open Cowork and follow the steps below.

Customize → Skills → “+” → Create skill → Upload a skill → Upload the skill file

Now I can run NotebookLM through Cowork.

But I got errors the first time, because I skipped half the video.

So before you start, here is what I wish someone had told me.

How to set it up

Two steps. Skip either, and the skill will fail on first run.

Step 1: Install Claude Chrome Extension

Go here and click Add to Chrome.

Step 2: Add the NotebookLM domain to Claude in Chrome

Open the Claude desktop app. Click your name in the bottom left. Hit Settings.

In settings, scroll to Claude in Chrome (Beta). Click Add websites. Type notebooklm.google and confirm.

Without this, the skill cannot drive NotebookLM. The extension is loaded but blocked. That was my first error.

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How to use it?

In any Cowork chat, type:

/notebooklm

The skill loads and asks what you want to do.

Four options come up.

1. Read / extract from a notebook

2. Add sources to a notebook

3. Generate a Studio output

4. Create a new notebook

Let’s pick Create a new notebook.

Question 1: Name

The skill asks if you want to name it yourself or let NotebookLM auto-name it. I named mine Prompt Engineering Course.

Question 2: Sources

The second question will ask you about the sources.

I clicked Yes, add sources now.

Five options: URLs, YouTube, uploaded files, copied text, or something else.

I dropped into the Anthropic prompt engineering course on YouTube:

Next, it loaded the source and took control of my Mac.

A red border appears at the top of the screen.

That is Claude debugging the browser. You watch it click, type, wait, screenshot. Same way you would do it, just faster.

It pasted the YouTube link into the source box.

NotebookLM ingested the transcript and auto-generated a summary describing the video as a roundtable with Anthropic experts on prompt engineering.

Done. Notebook live.

Audio Overview, Mind Map, Reports, Quiz, Infographic, all one click away in the Studio panel.

So here are 3 use cases I love.

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Use Case 1: Weekly Job Interview Prep

The week before an interview, most people do the same thing. Open the company website, read the About page, and maybe check LinkedIn. Walk in underprepared and get caught off guard by something you should have known.

The fix is better synthesis of more sources than you can read.

Step 1. Train NotebookLM with Deep Research

In Cowork, type:

/notebooklm

Create a new notebook called “Stripe Interview”. Then run Deep Research with this prompt:

I have an interview at Stripe. Find sources that cover:

• Company history, mission, and recent strategic moves

• Latest product launches and roadmap signals from the last 12 months

• Public statements from the CEO and senior leadership (podcasts, conferences, X)

• Financial position, fundraising, or earnings if public

• Competitor landscape and how Stripe positions against them

• Glassdoor reviews, common interview formats, and what current employees say about culture

Prioritize sources from the last 12 months. Mix press releases, podcast transcripts, news articles, official advisories, and employee reviews.

Cowork takes over the browser. Red border on top. Opens NotebookLM, creates the notebook, clicks Discover sources, pastes the prompt, and hits run.

After some minutes, the research was done.

And the notebook is ready.

Step 2. Cowork Setup

Create a folder on your computer called interviews. Inside, one subfolder per company.

The artifacts folder holds NotebookLM outputs.

Give Cowork access to interviews.

The notebook from Step 1 holds the knowledge. The folder is the landing strip for what comes next.

Step 3. Generate Artifacts

In the same Cowork chat, type:

/notebooklm

Open the “Stripe Interview” notebook. In the Studio panel, generate:

1. A Briefing Doc covering company narrative, recent strategic priorities, and leadership focus areas

2. A Quiz with likely interview questions, each anchored in something the company has publicly said

3. An FAQ on common pitfalls from Glassdoor reviews and negotiation pressure points

Save all three to /interviews/stripe/artifacts/.

And generate an artifact with three tabs;

1- Briefing Doc

2- Quiz

3- FAQ for common pitfalls

And it started building.

Here is the artifact after a few adjustments.

Step 4. Schedule

Turn it into a habit. Every interview is prepped automatically.

Create a file called /interviews/upcoming.md. Drop your upcoming interviews in it like this:

- Stripe, 2026-07-15

- Anthropic, 2026-07-22

- Notion, 2026-08-03

In Cowork, open Schedule. Create a new task with this prompt:

Every morning at 9 AM, check /interviews/upcoming.md for any interview 7 days away.

For each interview 7 days out:

Create /interviews/{company}/artifacts/.

Use /notebooklm to create a notebook named “{company} Interview”.

Run Deep Research with this prompt, replacing the company name: “I have an interview at {company}. Find sources covering company history and strategy, latest product launches and roadmap from the last 12 months, public statements from leadership, financial position, competitor landscape, Glassdoor reviews, and interview formats. Prioritize the last 12 months. Mix press releases, podcasts, news, and employee reviews.”

Generate Briefing Doc, Quiz, FAQ. Save to /interviews/{company}/artifacts/.

And create an artifact with three tabs based on this briefing.

Set frequency to daily, 9 AM.

That is the whole thing.

You walk into an interview knowing the company’s last twelve months better than half the people already working there.

The next two use cases are exclusive to paid subscribers

• Use Case 2 — Weekly Language Feedback Loop: turn Claude + NotebookLM into a tutor that drills your real mistakes every week. This is just one lesson from my full video course on learning a foreign language with Claude. The moment you upgrade, you’ll receive an email unlocking free access to this and other courses.

• Use Case 3 — Pre-Trip Destination Companion: most AI travel advice is stale, pulled from old training data. This builds a brief from fresh sources, so you walk into any city this summer knowing what's actually true today. You’ll also get my complete guide on using AI to plan trips.

Become a paid subscriber and you get both use cases, the full language course, and the AI travel guide, everything you need to learn a language and prepare for your summer trip.

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Click here to get my FREE Claude course (20+ lessons)

ChatGPT chats. Claude works.

That sounds like a small difference. It’s not.

I used ChatGPT for almost everything in 2023 and 2024. It answered my questions, wrote my drafts, helped me think out loud. But every output stayed stuck inside the chat. I still had to copy, paste, format, and do the actual work myself.

Then I started using Claude Cowork (not the chatbot, the version that lives on your desktop and acts on your computer).

It doesn’t just tell you what to do. It does it: organizes your files, builds your slides, runs tasks while you sleep, and connects to the tools you already use.

I’ve spent months testing the features ChatGPT simply can’t match.

Here are 7 amazing things Claude can do that ChatGPT can’t.

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1. Claude Cowork can do the work (not only chat)

Most people use AI like it’s a search engine. You type something, get an answer, start over.

Claude Cowork works differently.

It’s built for doing tasks: organizing files, creating on-brand content (slides, reports, infographics, etc), and almost anything that can be done on your computer.

You’re actually giving AI a job.

If you’re new to Claude Cowork: Download Claude for desktop, install it, and open the app.

Claude Cowork can review files, follow instructions, keep your project context in mind, generate real deliverables, and handle processes that would be a nightmare to do manually.

ChatGPT chats. Cowork gets things done.

In the video below, Claude Cowork takes a messy folder of receipts and turns it into an organized system: category folders and a summary spreadsheet.

We’ll see many features exclusive to Claude Cowork in the following points.

2. Claude Skills: It turns repeated tasks into reusable workflows

A prompt can solve a task once.

When you do the same task frequently, you end up rebuilding the process every single time.

You explain again what role Claude should take, what criteria to follow, what format you expect, what mistakes to avoid, and what a good result looks like.

That works once. But it doesn’t scale.

That’s the first major shift Claude skills introduced.

Here’s what I got when I asked Claude to create a presentation (no skill enabled):

Here’s what I got when using Anthropic’s brand guidelines skill:

With a slide deck builder skill:

With skills, you teach once and Claude follows your instructions every single time.

To download the best Claude skills, read my guide below

📚 We Tried 100 Claude Skills. These Are The Best

Note: Skills is in beta for a small group of ChatGPT users (no Plus and Pro ChatGPT accounts)

3. You can automate your life with Schedule Tasks

Lately, Claude has been running tasks on my computer while I sleep.

How? With Scheduled Tasks.

Here’s an example.

I used to go to YouTube and manually search for videos on specific topics (part of my research step when writing).

It was tedious.

So I scheduled a task that runs at 3 am and creates a filtered report of trending videos automatically. That alone saved me several hours.

Here’s how to create a scheduled task from scratch:

1. Download Claude for desktop

2. Open the app and choose “Cowork”

3. Click “Scheduled” in the left sidebar

4. Click “+ New task”

5. Fill in the details (prompt, task name, etc.)

6. Click “Save”

Once saved, you'll see a confirmation like this:

That’s the most basic way to create scheduled tasks. For more use cases, read my complete guide below.

📚 Claude Scheduled Tasks: Complete guide

ChatGPT also has Tasks, but they’re mostly for reminders and recurring prompts.

In Cowork, scheduled tasks feel closer to real work automation: files, reports, folders, desktop workflows, recurring tasks Claude keeps handling over time.

Not every task needs you sitting in front of a screen. Some work can run later. Some can repeat every day.

4. Claude connectors

Claude can also move closer to where the work actually happens.

Your work lives in documents, spreadsheets, emails, calendars, and the tools you use every day. Not inside a conversation.

If Claude can’t get closer to that context, you end up doing the bridge work yourself. And that limits how much you can actually delegate.

Connectors bring Claude closer to where your work actually lives.

They bring Claude closer to your Google Drive documents, Gmail, calendars, etc.

To install a connector, go to Cowork → Customize → Connectors → + → Browse connectors

Less manual searching, copying, and pasting before asking for help.

Remember: Skills tell Claude how to work. Connectors give Claude access to where the work lives.

📚 Claude connectors: Complete guide

5. Claude in Excel, Word, and Power Point

A lot of professional work still lives in Office files: spreadsheets, documents, presentations, tables, reports, dashboards.

Copying a formula from a chat and pasting it into Excel gets old fast.

Claude in Excel changes that.

Instead of working from a chat bubble, Claude works closer to the spreadsheet itself: cleaning, analyzing, visualizing, and presenting the data.

In the Claude in Excel: Complete guide, I show examples of how Claude cleans data, summarizes, creates visuals, and synthesizes insights.

Thousands of rows. Manually exploring it would take hours. Claude summarizes the data, identifies trends, and builds a dashboard with visuals inside Excel. After a few tweaks, that raw spreadsheet becomes something you can actually present.

In Word and PowerPoint, same idea. You work closer to the actual file instead of bouncing between a chat and your document.

6. Claude can connect to many AI tools

Claude works well with other tools. Some have an official connector. Others don’t, but there are still useful workarounds.

The easiest way to understand this is with two examples: ElevenLabs and NotebookLM.

Let’s start with ElevenLabs.

ElevenLabs is useful when you need to do text-to-speech, voice cloning, AI dubbing, and audio generation.

Claude helps you write, structure, rewrite, or adapt the script. ElevenLabs then turns that script into a voice output. Good for videos, tutorials, product demos, explainers, podcast-style content, or social media clips.

To use it, just install the ElevenLabs connector.

NotebookLM is different.

It doesn’t have a native Claude connector, but it can still fit into a useful workflow.

NotebookLM is great at organizing knowledge from your documents and sources. Claude can take that knowledge and turn it into something more actionable: reports, infographics, dashboards, apps, working materials.

Here’s a workaround to connect Cowork with NotebookLM:

1. Run this prompt in Cowork:

Install this skill :https://github.com/teng-lin/notebooklm-py
I want to use it via CLI

2. Ask Cowork to turn it into a Skill.

3. Authenticate your Google account from the terminal with notebooklm login.

4. Copy the `storage_state.json` pat back into Cowork.

5. Test the Skill by typing `/notebooklm` and asking Claude to list you notebooks.

Once it works, Cowork can reach your NotebookLM sources.

I tested this with a messy retail sales spreadsheet.

NotebookLM pulled out the five most important insights from the data, with citations back to the source. Then Claude Cowork used those insights to build a one-page infographic app with different visual styles and a PNG export button.

That's the workflow: source material → grounded insights → usable output.

For more, read the complete guide below.

📚 Claude + NotebookLM: Complete guide

7. Claude Cowork scrapes websites reliably

ChatGPT can browse the web and pull information from pages. But web scraping needs more than browsing.

You need fields, filters, edge cases, repeated runs, and structured outputs.

Claude has two ways to handle web scraping.

Option 1: Claude Cowork (the basic way)

Cowork can visit a page, read the content, extract the fields you need, and turn the result into a dataset.

I used it to scrape my own site archive: date, post text, likes, comments, restacks, and post link.

The first run wasn't perfect. I gave Claude feedback and corrected the extraction logic. Once it worked, I scheduled this task to run every month.

Here’s what the output looks like.

Option 2: Claude Cowork + Apify (the method that scales)

Apify gives Claude access to ready-made scrapers for more complex websites and data sources. Better for robust, scalable scraping.

One example: a Reddit Trend Tracker.

Instead of manually checking Reddit to understand what people are talking about, Apify pulls structured data from relevant communities. Cowork then turns that into trends, summaries, or research material.

For more, read my complete Claude + web scraping guide below.

📚 Claude web scraping: Complete guide

Bonus: Claude can generate chats and artifacts

Not every output should stay as text. Sometimes you want something you can open, review, show, or use directly.

Claude can create three useful types of outputs for that: Charts, Artifacts, and Live Artifacts.

Each one solves a different problem.

Charts

Useful when you need to turn information into a cleaner visual: graphs, comparisons, dashboards, visual explanations. For example, you can ask Claude to turn content performance data into an interactive chart.

Use this prompt:

Create an interactive chart from this data.

Data:
- LinkedIn post: 18,000 views, 760 clicks, 120 subscribers
- Substack article: 7,500 views, 980 clicks, 210 subscribers
- X thread: 12,000 views, 430 clicks, 80 subscribers
- YouTube short: 25,000 views, 510 clicks, 95 subscribers
- Carousel: 9,800 views, 690 clicks, 150 subscribers

Make it explorable, not just static.

Include:
- hover details for each content format
- highlighted insights for the best format by metric
- clear labels
- a short explanation of what the chart shows
- toggles to compare views, clicks, and subscribers

Then you get a chart you can explore instead of a flat table:

Artifacts

Useful when the output needs to work like a small tool: a calculator, a dashboard, a page, a comparator, or a simple interface.

Here’s a prompt example:

Build an interactive calculator for estimating article production effort.

Use this case:
- Article type: long-form guide
- Research time: 3 hours
- Writing time: 4 hours
- Editing time: 2 hours
- Visuals: 3 visuals
- Publishing format: Substack + LinkedIn repurpose

It should include:
- editable inputs
- total estimated hours
- difficulty level
- recommendation
- warning if the workload is too high for one day

Make it clean, simple, and easy to use.

Build this as an interactive Artifact.
Make it usable, not just explanatory.

Then Claude creates something you can interact with, not just read:

Live Artifacts

Live Artifacts go one step further.

Useful when you need a tracker, monitor, dashboard, or workspace you can return to inside Claude Cowork. For example, you can create a live AI video radar that tracks videos about a specific topic.

Use this prompt:

Create a live AI video radar inside Claude Cowork.

Goal:
Track recent YouTube videos about AI, Claude, Claude Code, AI productivity, and workflow automation.

Use official or authorized sources when possible, such as:
- YouTube Data API
- channel RSS feeds
- exported CSV files
- manually pasted video links

Track:
- video title
- channel
- publish date
- views
- likes
- comment count
- duration
- topic category
- why it matters
- possible article angle
- possible LinkedIn post idea
- possible X post idea
- next action

Start with these search topics:
- Claude Code
- Claude Artifacts
- Claude Cowork
- AI productivity
- AI agents
- prompt engineering

Make it easy to refresh weekly.

Highlight:
- videos growing fast
- repeated topics across channels
- content gaps I could write about
- videos worth watching first

Then you get a workspace you can reuse, update, and review over time:

For more about charts and artifacts, read my guide below.

📚 Claude Charts, Artifacts: Complete guide

If you found this guide useful, take a moment to share it with others.

It’ll help me keep writing guides like this :)

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So why do you need skills for Claude Code?

If you’ve been using Cowork a lot (like me), you’ve probably hit a wall already. Cowork has limits that keep it safe for non-technical folks. That’s a good thing most of the time. But every now and then, you want to do more than it allows.

That’s when you need Claude Code.

And no, you don’t need to be an expert programmer to use it. Especially if you have the right skills installed.

When you build something with code, half the job isn't writing it. It's making sure it actually works: catching bugs, testing it, not shipping something that breaks the second someone uses it.

That's the part that scares people off (and what takes real experience).

These skills do that part for you! Claude plans it, tests it, and reviews its own work. You just steer.

In this guide, you’ll get:

• How to quickly install a Claude Code skill

• The best 6 Claude Code skills

• The exact command to install each one

Every week, I publish new Claude guides: some free, some paid.

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How to Install a Claude Code Skill

This part scares people off for no reason.

Installing a skill is usually one command pasted into your terminal.

Most skills live in something called a "marketplace" (think of it like an app store for Claude). The official ones are already connected, so you just install.

Here’s how to install one:

1. Open Claude Code in your terminal.

2. Paste the install command for the skill you want (I list each one below).

3. If a skill lives in its own “marketplace,” add the marketplace first, then install it.

4. Restart Claude Code, so it picks up the new skill.

Here’s an example:

That’s it. Most skills install in under a minute.

Pro tip: install the ones you’ll use everywhere (like Skill Creator below) “globally,” so they work in every project without you having to think about it.

The best 6 Claude Code skills

1) Skill Creator

Problem:

Most people who try to build their own skill hit a wall fast. They don’t know the structure, the formatting, or what makes a skill reliable. So they either give up or build something that breaks the second a client touches it.

The skill:

skill-creator is the official Anthropic skill that builds other skills. You describe what you want in plain English, like you’re explaining a task to a coworker. Claude drafts the skill, tests it, fixes it, and packages it into something you can reuse forever.

Why it works:

Every custom skill you make later comes out of this one. It compresses the whole learning curve from "I have no idea how skills work" to "I just made one in five minutes"

This is the first one I'd install. Here’s the command to install it:

/plugin install skill-creator@claude-plugins-official

2) Grill Me

Problem:

Claude loves to jump straight to a plan. You give it a half-formed idea and it spits out a full document before it actually understands what you want. Then you spend the next hour correcting it.

The skill:

This one comes from Matt Pocock and it’s one my favorite skills. It tells Claude to interview you relentlessly about your plan until you both actually understand it, working down every branch of the decision before writing a single line of code. In one session, it asked Matt 16 questions. On bigger features, he’s had it ask 30 to 50.

Why it works:

Most mistakes come from Claude guessing what you meant. Grilling kills the guessing.

Here’s the command to install it:

npx skills@latest add mattpocock/skills -s grill-me -g

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3) Superpowers

Problem:

You ask Claude Code to build something. It sprints out of the gate, writes the whole thing in one shot, and on the surface it looks great. Then you actually run it. Or worse, your client runs it. And it falls apart. Why? Claude doesn’t plan. It doesn’t test. It doesn’t double-check its own work. It just writes.

The Skill:

It forces Claude to work like a senior developer. Step back and plan the whole thing first. Work in an isolated environment so nothing breaks your main project. Write tests before writing code. Then review its own work twice. Once for “does this match what I asked for” and once for code quality.

Why it works:

Most failures with Claude Code aren’t from Claude being dumb. They’re from Claude rushing. Slowing it down just enough to actually think gets your first pass to 80% instead of 60%. That means fewer debugging cycles, lower token costs, and code that doesn’t fall apart the second a real person uses it.

Here’s the command to install it:

/plugin install superpowers@claude-plugins-official

4) Frontend Design

Problem: When Claude builds a website or a slide deck on its own, it often looks AI-generated. Generic, a little off, the kind of thing you’d want to redo before showing it to anyone.

The skill: Frontend Design is the official Anthropic skill for making the things Claude builds actually look good. Install it, and whenever you’re designing something in Claude Code, the output comes out looking a lot less “an AI made this.”

Here’s the command to install it:

/plugin install frontend-design@claude-plugins-official

5) Context Mode

Problem:

Use Claude Code for a while and you’ll notice this: Things get sluggish, it forgets what file you were editing, it asks what you wanted to do (again). What’s happening is every command Claude runs dumps a pile of raw output into its memory. After about 30 minutes, most of that memory is junk. Claude has to reset to keep going. And when it does, it forgets where you were.

The Skill:

It does two things. First, it filters the junk before it ever reaches Claude’s memory. Only the useful part of each command makes it through. Second, it keeps a running log of your session (files edited, tasks in progress, your last prompt). When Claude resets, it pulls that log back in and picks up exactly where you left off.

Why it works:

Most “long sessions” with Claude Code aren’t really long. They’re a bunch of short ones glued together with you re-explaining yourself. This kills both problems at once: less garbage going in, full memory of what you’ve done. Sessions that used to die at the 30-minute mark now run for hours.

Here’s the command to install it:

/plugin marketplace add mksglu/context-mode

/plugin install context-mode@context-mode

6) Claude Mem

Problem:

Claude Code starts every new session from scratch. So you end up explaining the same project over and over again, wasting time and thousands of tokens just catching Claude up.

The skill:

This skill carries your knowledge across all your future sessions. It hooks into Claude’s session and automatically captures what happened: the file edits, the decisions, the bug fixes. It compresses all of that into summaries and stores them locally. When you open a new session, the relevant parts get pulled back in automatically. It even writes and updates your project notes for you while you work.

Here’s the command to install it:

/plugin marketplace add thedotmack/claude-mem

/plugin install claude-mem

Bonus: /review and /ultra-review

Problem:

Even careful work has bugs Claude misses on its own. You need a second set of eyes before you ship something that actually matters.

The skills:

This one isn’t an install. It’s already built into Claude Code, and most people just aren’t using it. When you finish writing code, type:

• /review: Claude does a structured review of what you just built, hunting for bugs, edge cases, and design issues

• /ultra-review: sends your work to the cloud and spins up a whole team of reviewer agents that each attack from a different angle (logic, security, performance)

Use /review for fast, free feedback on everything. Save /ultra-review for the big stuff: a major change, or anything touching payments or a database.

One more thing

Don’t install all the skills at once.

That’s the fastest way to get overwhelmed and use none of them. Pick one, learn it, build a few things with it, then add the next.

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Summer is coming!

Diana and I prepared a guide on how to use AI on vacation trips. She has more travel experience than I do, but I’ve also added my own experience to complement her ideas.

Imagine this: you’re in Barcelona, it’s 9 pm, every restaurant around you just closed, and you’re starving. You’ve spent the last 30 minutes scrolling Google Maps trying to find one place that’s still open AND serves what you actually want.

No luck.

This used to be me. But in about 20 seconds, AI pointed me to a place 10 minutes away that was still open and served exactly that.

That wasn't the only time AI saved my trip.

After that, I started using AI for almost everything on my last two trips: one across the US West Coast (I’m not from the USA), the other through Portugal. I used ChatGPT, Claude, and Gemini for almost everything: translating to shopkeepers in Cascais, planning a 12-day road trip, finding scenic routes, and even figuring out the location of a beach I saw on Instagram.

Some of these saved me hours. One of them saved an entire day. A few I would never have even thought of without AI.

In this guide, you’ll learn:

• The exact prompt I used to plan a trip in minutes (copy-paste ready)

• How AI helps me save money on my trips

• How to overcome the language barrier using AI

• How to find a place you saw on Instagram (even when nobody says where it is)

• The rules to follow to use AI safely in your trips

• and more!

Here’s how to upgrade your next trip with AI

This guide is exclusive for paid subscribers :)

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I’m fascinated by how this works now. You no longer need a ton of technical knowledge (especially coding) to build tools, automate boring tasks, and more.

I’m not exaggerating. I haven’t written a single line of code when building with Claude.

No programming language needed. Just one language: English.

As Andrej Karpathy said, the hottest new programming language is English. I’ve been literally talking to Claude Cowork to build things. When I say talking, I mean it. I don’t type anymore.

What I do is use voice dictation.

Why? Because you have to give Cowork a lot of detail (just like you’d brief a coworker). If you type all of that, you either skip some details or waste a bunch of time doing it.

So I tested the voice dictation tools out there to find the one that fits this kind of work.

In this guide, we’ll see:

• The built-in voice dictation on your computer (and why it’s usually not enough)

• AI voice dictation tools (with the best use case for each)

• The setup I personally use to talk to Claude

Note: No one is paying me to write this, and I'm not associated with any of these tools. After using them for a while, I actually think most people won't need the fanciest features these tools sell you.

Every week, I publish 3 new guides: some free, some paid.

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Built-in voice dictation (macOS and Windows)

If you've ever tapped the microphone icon on your phone keyboard, you already know what voice dictation is. Same idea, just on your computer.

Here’s how to turn it on:

1. On macOS, press the Globe key twice (set it up in Settings → Keyboard)

2. On Windows 11, press the Windows key + H

A small mic icon pops up. Start talking, and it starts transcribing.

On macOS, it’s basically a real-time transcription. The moment a word leaves your mouth, it shows up in whatever app you’re in.

It’s decent. But it hasn’t caught up with the modern tools out there.

Here are its weak points:

• It transcribes what you say literally (no AI cleaning words like “uh” and “um“)

• The auto-punctuation is basic. You have to say “question mark” to get a “?”

• It has a 30-second timeout that interrupts longer sessions

This might sound like nothing. But these little things change the way you talk. You end up adapting to the tool instead of just speaking naturally.

That's exactly what AI dictation fixes.

They let you talk naturally, like you're talking to a coworker or a friend. The tool adapts to you (not the other way around).

The AI tools below are paid, and I’m going to talk about them based on my own experience.

But here’s my honest advice: try them before you pay. All of them have a free version or demo, and which one fits you really comes down to how you’ll use it.

I mostly use voice dictation for building things in Claude Cowork and Code. For that, I don’t need super polished text or fancy formatting (the AI understands my raw text just fine). So for me, the cheapest option (VoiceInk) in this list already does the job.

VoiceInk: The most affordable, reliable choice (macOS)

VoiceInk is a Mac app that turns your speech into text right where your cursor is, in any app. Everything runs locally on your machine, so your voice never leaves your device (no cloud, nothing sent anywhere).

The best thing? You pay once: $39, no subscription.

The catch: it’s macOS only (and it needs Apple Silicon and macOS 14.4 or later).

As a Mac user, this is enough for me. It does exactly what I need when working with Claude: decent speed, custom vocabulary, and AI cleanup.

The setup that gives me the cleanest text:

1. AI model: gemini-3.1-pro-preview

2. Add tricky words to the dictionary (like “Claude” and “Cowork”)

With that, I get text nearly as clean as what the more expensive tools deliver.

A few honest notes after using it:

• It’s a big step up from the native dictation (better punctuation, fixes your mistakes)

• It cleans up your slips and stops you from repeating words and phrases, kind of like a quick proofread

• It doesn’t auto-format like other tools do (no automatic bullets or numbered lists)

• I’d say it’s around 95% accurate. Once in a while, it drops or adds a random word

For talking to Claude, that’s more than enough. And at $39 once, it’s the easiest one to recommend.

By the way, I discovered VoiceInk thanks to quick review. Thanks, Sabrina!

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Superwhisper: Good option if you're on Windows

I haven't used Superwhisper as much as VoiceInk, but I'm including it for three reasons. It’s good, runs on Windows, and also offers a one-time payment.

The core idea is the same as VoiceInk: it runs locally on your device, drops text wherever you're typing, and keeps your voice off the cloud (as long as you stick to the local models).

What makes it different:

• It works on macOS, Windows, and iOS

• You can tune it a lot (different modes, model choices, custom prompts per app)

• It does more than dictation. It also records meetings and transcribes audio files

Superwhisper has a free tier to test it, a monthly plan ($8/month), and a one-time lifetime license at around $250.

One thing worth knowing before you jump in: all that tuning means a bit more setup. It’s more powerful, but less “install and go” than VoiceInk

If I were on Windows, I’d start with the free version, test it for a few days, and only pay if it clicks (same advice I’d give for any of these).

Wispr Flow: The polished, no-setup option (but pricey)

Wispr Flow is a cloud-based dictation app that turns your speech into polished text in any app. The big difference from VoiceInk and Superwhisper is the heavier AI rewriting.

It makes your text prettier.

Here's what Wispr Flow does that the other two don't (at least by default):

• It generates transcripts faster

• The text comes out more polished: great punctuation, fixes speech mistakes, avoids repetition

• It automatically creates bullets or numerated lists when needed

These features are great. But in my case, I don’t need them that much.

The faster transcript is a few seconds quicker, and I don’t need polished text or bullets when I’m talking to Claude Cowork or Code (the raw text is enough for the AI to understand me).

Wispr Flow is $15/month (for my use, it’s overpriced)

So who is it for? If you want AI-polished text in your emails, documents, and messages with zero setup, and a monthly subscription and cloud processing don’t bother you, Wispr Flow is a great pick.

It’s not for you if you mostly dictate into AI tools (Claude, ChatGPT, etc), because the raw text is already enough there. You’d be paying for features you won’t use.

What’s your favorite tool? Let me know in the comments

P.S. Superwhisper or Wispr Flow aren’t always 100% accurate. Because of the price, I expected them to be way more accurate than VoiceInk, but they occasionally get some words wrong. Again, I recommend trying them yourself.

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A couple of years ago, using AI felt like a superpower. Something that could put you ahead of almost everyone in any room.

Today, it’s starting to feel more like knowing how to use Word or Excel.

Useful, yes. But no longer impressive.

AI is becoming part of the modern work toolkit, not just for tech people.

Writers use it to draft
Marketers test ideas with it
Students use it for assignments
Entrepreneurs use it to move faster

Everyone “uses” AI. Few get anything real out of it.

That’s exactly why just using it won’t protect your job anymore.

The edge now comes from how you use it and what you can actually produce with it: executing faster, communicating better, thinking more clearly, making smarter decisions, etc.

The gap between basic AI use and real advantage looks small. The results aren’t.

One person asks AI for a quick, generic draft.
Another gets it to write in their own voice, matching the company’s style.

One person saves 5 minutes on emails.
Another has systems running while they sleep.

In 2-3 years, “I use AI” won’t mean anything. Everyone will say it.

What will actually matter is what you can do with it that the next person can’t.

This guide gives you exactly that:

• The 3 modes that tell you what to delegate to AI and what to never hand over

• Why a workflow beats a pile of prompts (and the 5 questions I use to build one)

• How to turn AI’s instant answers into real judgment, the skill that doesn’t go obsolete

• The simple formula behind a real professional advantage

The free part showed you the problem. The rest shows you the fix.

This guide is exclusive to paid subscribers. Support us by becoming a paid subscriber today :)

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They actually have a context problem.

Claude is smart enough to help with almost anything. But most of the time, the information it needs isn’t in the chat. It’s sitting in your emails, your calendar, your documents, your tickets, your databases, your internal tools.

So the workflow ends up looking like this:

Search for information → Copy it → Paste it into Claude → Wait for a response → Copy the response → Move it somewhere else

It works, but it doesn’t scale.

That’s where Claude connectors come in.

Connectors let Claude talk to apps like Gmail, Notion, Google Drive, and others, so it can pull info from them or take actions on your behalf, instead of you copy-pasting things into the chat.

Skills tell Claude how to work. Connectors give Claude access to where the work actually lives.

In this guide, we’ll see:

• The 10 best Claude connectors

  • Connectors for Docs & Knowledge

  • Connectors for Communication

  • Connectors for Design

  • Connectors for Code & Data

  • Connectors for Business Operations

• How to quickly install a Claude connector

I’ve added a tutorial link to some connectors, in case you’d like to dig deeper.

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The Best Claude Connectors

Claude has dozens of connectors. Here are the best we found

Connectors for Docs & Knowledge

1) Google Drive Connectors

Problem:
Your files are scattered across folders. You know the information is somewhere in Drive, but you still have to search for it, open the file, copy what you need, and paste it into Claude.

Connector:
Google Drive connector lets Claude work directly with your files: search documents, summarize folders, review content, or compare files. Drive is usually where the real context lives, and this connector removes the bridge step between Claude and your documents.

Link: Watch the tutorial

2) Notion

Problem:
Notion is where good ideas go to hide.

You save project notes, content ideas, research, tasks, templates, and half-built plans. Everything is “organized.” But when you actually need something, you end up searching through pages, opening old notes, trying to remember where that one useful idea was.

Connector:
Notion connector connects Claude to your workspace so it can search pages, summarize notes, reorganize information, and turn scattered content into something usable, whether it's internal documentation, a content system, research, or a personal knowledge space.

Link: Watch the tutorial

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Connectors for Communication

3) Gmail Connectors

Problem:
Your inbox knows more about your work than you do.

It has the request, attachment, deadline, follow-up, decision, and probably someone waiting on a reply.

But Claude only sees the email you pasted. So every time you want help, you have to manually reconstruct the conversation first.

Connector:
Gmail connector gives Claude access to your inbox, where every decision, request, and follow-up already lives. It can search threads, summarize conversations, find pending items, and help you prepare replies.

Link: Watch the tutorial

4) Google Calendar

Problem:
Claude can help you plan. But if it doesn’t know your calendar, the planning stays disconnected from what your week actually looks like.

It doesn’t know your meetings, your open blocks, your deadlines, or your focus time.

Connector:
Google Calendar connector gives Claude access to your real schedule so the planning it helps with actually fits your week. It can find open blocks, prepare agendas, detect overloaded days, or help you reorganize tasks.

Link: Watch the tutorial

5) Slack

Problem:
Slack is where a lot of important decisions happen. And it’s also where they get buried fast.

Between threads, replies, channels, and scattered messages, context gets lost.

Then someone asks what was decided, who was responsible, or what the next step was. And you have to search manually.

Connector:
Slack connector gives Claude access to your conversations before the context disappears. It can search threads, summarize channels, identify decisions, find blockers, and turn discussions into next steps.

Connectors for Design

6) Figma

Problem:
Going from Figma to code seems simple until you try to match the design.

The spacing is slightly off.
The components don’t line up.
The layout works, but it doesn’t feel like the original screen.

And then someone from design notices.

Connector:
Figma connector gives Claude access to the design context: frames, layouts, components, variables, and visual structure. It can help generate code or implementation plans from the actual design intent, not from a screenshot.

7) Canva

Problem:
Creating visual content usually means jumping between tools.

You ask Claude for ideas → go to Canva → build the design → go back to Claude to adjust the text → go back to Canva to edit → repeat.

Connector:
Canva connector brings thinking and designing into the same workflow. It can help you go from concept to copy, to layout, to a finished asset you can keep editing. Useful for creators, marketers, and educators building presentations, newsletters, carousels, or branded content.

Link: Watch the tutorial

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Connectors for Code & Data

8) GitHub

Problem:
When you ask Claude for coding help without the repository, the answer stays generic. It doesn’t know your file structure, your open issues, your branches, or how the project is actually set up.

Connector:
GitHub connector lets Claude work directly with your repository so its help goes from generic to specific. It can read files, review issues, check pull requests, look at branches, and help you plan or prepare an implementation.

9) BigQuery

Problem:
Data teams often store important information in data warehouses.

But asking questions about that data usually means going through dashboards, SQL, exports, or an analyst who has access.

Connector:
BigQuery connector lets Claude work directly with your datasets, tables, and queries: review schemas, generate SQL, run queries, and explain results. It removes the friction between the data your team already has and the answers they need.

Link: Watch the tutorial

10) Power BI

Problem:
Dashboards show answers. But they don’t always explain how those answers were built.

A metric comes from a DAX measure.
A visualization depends on relationships between tables.
A filter changes the result.
A report looks clear until someone asks: “Where does this number come from?”

Then you’re no longer looking at a dashboard.
You’re investigating the model.

Connector:
Power BI connector connects Claude with your semantic models: tables, relationships, measures, metadata, and DAX queries. It can help analysts and BI teams audit metrics, explain where a number comes from, and dig into the logic behind a report. The remote server version also lets you query models in plain language while keeping your permissions in place.

Link: Watch the tutorial

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Connectors for Business Operations

11) Stripe

Problem:
Payments, subscriptions, invoices, customers, and refunds all live in Stripe.

When something goes wrong or someone has a billing question, figuring out what happened usually means digging through the dashboard manually.

Connector:
Stripe connector gives Claude direct access to your Stripe data: customers, payment history, invoices, subscriptions, and refunds. When a billing question comes up, Claude can find the context it needs without someone having to pull it manually.

Link: Watch the tutorial

How to install a connector

Here’s how to install connectors on Claude, Claude Cowork, and Claude Code.

Claude Cowork or Web: Customize → Connectors → “+” → Browse connectors → Choose an Connectors

Claude Code:

1. Open the Claude Code terminal and run:

claude mcp add [my-mcp] -- npx -y mcp-package-name

Replace [my-mcp] with the name of the Connectors you want to install.

2. Then verify the installation with

/mcp

If you liked one of the connectors listed, share this guide with others!

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I’ve used ChatGPT to explore new topics (especially with voice mode)
I’ve used NotebookLM to learn from my own materials

Then I started using Claude Cowork and my learning habits changed completely.

My approach today looks completely different. I learn faster, I retain more, and I actually do things while I'm learning instead of just consuming information.

In this guide, you’ll learn:

• How Claude Cowork helps me learn AI faster by doing

• Why voice dictation beats voice mode for learning

• The “learning profile” method that turns Claude into a personal tutor

Heads up: this guide covers my approach to learning using Claude. Some of it transfers to other chatbots, some doesn’t. At the end, I'll share when I reach for ChatGPT and NotebookLM for learning (spoiler: each one wins for a different job)

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This time, I asked AI writers to share theirs.

We use Claude daily in our real work. Between us, we’ve built dozens. We picked our #1 skill.

In this guide, we’ll see:

• The best skills we built

  • Skills for Writers & Content Creators

  • Skills for Making Better Decisions & Research

  • Skills for Busy Professionals

  • Skills for Building with Claude Code

• How to install the skills in Cowork

• How to build your own skill in Cowork (+ my tips)

Quick reminder if you’re new: Skills are reusable instructions you give Claude, so it follows your way of doing things. For a refresher on the basics, read this guide.

Each skill is credited with a link to its creator at the end. If you find one useful, consider supporting them by subscribing to their newsletter :)

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The Best Claude Skills We Built

There are many skills out there. Here are the best we built

Almost all the skills listed can be used with Claude Cowork. I'll flag the few that don't.

For writers & content creators

1) Instagram post downloader

Problem:
You see a great Instagram post, a carousel of slides, or an image you want to keep. Instagram doesn’t have a download button, so you come up with a workaround: screenshot, swipe, screenshot again, crop each one, rename them, drop them in a folder. By slide six, you’ve lost interest. Screenshots aren’t the real image anyway (they’re a lossy copy)

The Skill:
You hand it an Instagram post URL. It opens the post, figures out whether it’s a single image or a carousel, pulls the actual high-resolution files straight from Instagram’s site, and saves them into a tidy-named folder based on the caption. For carousels, it also stitches every slide into a single PDF so you can scroll through the whole thing in one place.

Why it works:
There are sites that download an Instagram image if you paste a link, but you have to feed them one URL at a time and click download through every slide of a carousel yourself. Do that every day or every week and it turns into a small, stressful chore. This skill skips all of it: you drop in multiple links at once, press enter, and walk away. As a bonus, I’m also sharing a skill for downloading reels using a third-party site.

Download the skills: image & carousels, reels

This skill was created by me (Frank) and from Write, Prompt, Scale

Note: If the skill doesn’t work, add *.cdninstagram.com to domain allowlist: Settings → Capabilities → Domain allowlist → Paste *.cdninstagram.com → Add

2) AEO-Optimization

Problem:

You write a great Substack post. Google might rank it eventually. But ChatGPT, Perplexity, and Claude will never quote it. Why? Because the structure they need to extract answers, short capsules, question headings, no links inside the answer, is the opposite of how most of us write. So the article works for humans scrolling, and disappears the moment someone asks an AI the same question your post answers.

The Skill:

You give it a draft (or a published post). It rewrites your H2s as the actual questions people ask AI (”What is X?”, “How do you Y?”), drops a 50-80 word answer capsule right under each one with zero links inside, and audits the rest, paragraph length, original data signals, named frameworks. You get back an AEO-ready version that’s still your article, just structured so AI can quote it cleanly.

Why it works:

AI engines don’t read articles end-to-end. They scan for extractable, link-free chunks that directly answer a query. Most writers bury the answer in paragraph three and stuff the first two with links. This skill flips that, the answer comes first, clean, in the exact word count AI prefers. You stop competing for Google rankings alone and start being the source AI cites when someone asks.

Link: Download the skill

This skill was created by from LearnAIwithMe

3) Create Thumbnails for Articles (Claude Code)

Problem:

Creating newsletter thumbnails can be time-consuming if you’re generating image candidates one at a time from the Gemini web interface. Gems can eliminate the need to keep attaching images and pasting prompts, but the overall experience feels slow to me. I wanted a way to quickly generate multiple brand-appropriate images with Gemini models from Claude Code where I do most of my work.

The Skill:

You paste in your article copy or a link and ask Claude Code to give you a few thumbnail options. It will suggest compositions informed by your copy and your brand specs, build an image prompt based on your selections, use a script to call Gemini, and evaluate the completed images through its Computer Vision. You can work on other tasks while the images are processed.

Why it works:

It allows Claude to act as an image-generation agent that has access to your brand’s reference images. While I know many people prefer evolving images one step at a time, I find it easier to get three or four candidates all at once. A lot of the time, I get lucky and just pick one. I also find that Claude does a better job with image prompts than I do.

I turned my Skill into a template you can use with Claude Code. It requires a Gemini AI Studio API key and a small script for calling Gemini. You can get the Skill and everything you need to install it on GitHub.

Here’s an image sample.

Link: Download the skill

This skill was created by from Wondering About AI

4) Substack Notes Scraper

Problem:

You write notes on Substack and want to know which ones actually landed — likes, comments, restacks, all of it in one place. Substack doesn’t give you that view. So you end up scrolling your own notes page, copying numbers into a spreadsheet by hand, screenshotting the good ones, and trying to remember which post said what. Do it once and it’s annoying. Do it monthly to track what’s working, and it eats half a morning every time.

The Skill:

You give it three things: the notes URL, the author name, and the period you care about (a month, a date range, whatever). It opens the page, scrolls until it has loaded every note from that window, pulls each one’s date, text, likes, comments, restacks, and link, and hands you back a clean .xlsx with frozen headers, filters, and proper formatting. Ready to sort, filter, or paste into a report.

Why it works:

Substack Notes has no public API, so the only honest way to get the data is from the rendered page. Doing that by hand is exactly the kind of repetitive task that gets skipped. This skill does the scrolling, the filtering (so restacks from other authors don’t pollute your data), and the formatting in one go. You ask once and get a spreadsheet you can actually work with.

Here’s a dataset I generated after running the skill.

Link: Read the guide | Download the skill

This skill was created by me! Consider becoming a paid subscriber for more :)

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5) Notes Humanizer

Problem:

You can spot AI writing from a mile away. Em dashes everywhere, “let’s dive in” openers, rule-of-three lists, every sentence the same length. Asking the model to “sound more natural” just makes a slightly different version of the same generic text. So you end up rewriting it yourself, sentence by sentence.

The Skill:

You paste in any AI-generated text. It runs through the full list of patterns that mark text as machine-written and strips them out. Then it adds the things models don’t do well on their own: opinion, varied rhythm, a specific detail, an aside. You get back text that reads like a person wrote it.

Why it works:

Most “humanize” prompts just tell the model to write less formally. That doesn’t help. The AI patterns are statistical defaults, the most common phrasing the model has seen in its training data from WriteStack.

Link: Download the skill

This skill was created by from TheIndiepreneur

Want to grow on Substack? Orel built WriteStack to make life easier for Substack writers. It handles Notes scheduling, performance analytics, and content generation in one place (quick note: that link’s an affiliate one, so I’ll earn a small commission if you decide to give it a try)

Skills for Making Better Decisions & Research

6) The Sycophancy Skill

Problem:

The default version of Claude wants you to feel right. You bring it a decision, it finds three reasons your instinct is solid, and you leave more confident than when you started. That’s not a thinking partner. That’s a mirror with better grammar. The longer you use it that way, the harder it gets to catch your own blind spots, and the cost doesn’t show up until a decision you can’t take back.

The Skill:

You paste a prompt that flips Claude’s default. Instead of starting with agreement, it starts with the assumption you haven’t tested. It argues the strongest case against your idea before it considers yours. It doesn’t retreat when you push back unless you give it new evidence. It tells you what’s weakest first, not what’s working. And if it can’t find a real flaw, it tells you that too, instead of inventing one to seem thorough.

Why it works:

Most skill prompts tell Claude what to add. This one tells Claude what to stop being by default. The prohibitions do more work than the rules. Without them, Claude follows the framework and then reverts to “great question” energy anyway. The prohibitions shut that off at the source.

Default Claude:

Sycophancy Skill Claude on:

Link: Read the guide

This skill was created by from Leadership in Change

7) Last 30 Days

Problem:

You hear about a new AI tool or trend and want to know what’s actually going on with it. Is it as good as people say? What are the real complaints? Googling it gives you SEO-stuffed “best of” lists and PR fluff from last quarter. The honest takes live on Reddit threads, X, etc. Chasing them across platforms eats your afternoon.

The Skill:

You activate it and name a topic. A tool, a trend, a feature, whatever. It searches Reddit, X, and the web for anything from the past 30 days. Then it hands you back a single report: what people agree on, where they disagree, and the pain points that keep coming up.

Why it works:

Most “research” is just one Google search dressed up. This one looks where the real opinions actually live. And when something shows up on multiple platforms at once, that’s your strongest signal it’s true. You save the afternoon and walk away with a much sharper picture.

Link: Watch the tutorial | Download the skill

8) Notebook LM Connector

Problem:

Notebook LM is one of the best AI tools out there, but it doesn’t talk to your other tools. So every time you want a new mindmap, audio overview, or slide deck, you’re back inside Notebook LM clicking the same buttons. There are open-source workarounds, but most of them need Claude Code and the terminal (not exactly beginner-friendly).

The Skill:

You install it inside Claude (no coding, no terminal). Then you just tell Claude what to do: “create a notebook, add these links as sources, generate a mindmap.” Claude goes into NotebookLM and does it for you. It runs through the Claude Chrome extension.

Why it works:

Notebook LM has the outputs (audio overviews, slide decks, infographics, mindmaps). Claude has the automation. Connect the two and you get NotebookLM on autopilot.

Link: Watch the tutorial | Download the skill

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Skills for Busy Professionals

9) Email tasks

Problem:

A lot of us (at least I am) are drowning in emails. Newsletters, news, etc. I wanted to have something that I can launch from my phone in Claude and that will help me save time and only read the emails that matter.

The Skill:

This skill is connected to my Gmail (connector). I type /emails-tasks and it reads my inbox for the last 8 hours (or whatever window I want to be set). It throws out everything that doesn’t need me, things like receipts, notifications, etc. Then, it surfaces only what requires a reply or a decision. For each one: who it’s from, what they actually need, how urgent it is, and a one-line opener if there’s a need for one.

Why it works:

Claude sorts the emails perfectly now. The best part about this skill - I can launch it straight from Dispatch from my phone. So now I am just seeing the most important emails and if I need to take any action or not.

Link: Download the skill

This skill was created by from Prosper

10) Morning intelligence

Problem:

You want to stay on top of news that matters to your work. Cowork Scheduled Tasks and Claude Code Routines now run recurring tasks on a schedule. The bottleneck is the prompt. A good one has to capture who you are, what you track, what to exclude, your sources, your geography, your format. Writing it from scratch is hard. Tuning it every time your focus shifts is harder.

The Skill:

A 15-question interview that captures your role, focus, story-type mix, exclusions, freshness, and format. It clusters your answers into pillars and writes a master prompt ready to paste into a Cowork Scheduled Task or Claude Code Routine. When priorities shift, run it again for a fresh prompt.

Why it works:

Design once, run forever. Drop the prompt into Cowork (Scheduled Tasks → New Task, requires Pro/Max with the desktop open) or Claude Code (Routines → New routine → Remote runs in the cloud without your laptop on). Either path turns a one-time prompt into a brief that arrives on its own.
Pro tip: Run the interview with Opus.

Link: Download the skill

This skill was created by from Cash&Cache

For building with Claude Code

These are skills I found on the Nate Herk YouTube channel. They’re highly recommended for Claude Code users.

11) Context Mode

Problem:

Use Claude Code for a while and you’ll notice this: Things get sluggish, it forgets what file you were editing, it asks what you wanted to do (again). What’s happening is every command Claude runs dumps a pile of raw output into its memory. After about 30 minutes, most of that memory is junk. Claude has to reset to keep going. And when it does, it forgets where you were.

The Skill:

It does two things. First, it filters the junk before it ever reaches Claude’s memory. Only the useful part of each command makes it through. Second, it keeps a running log of your session (files edited, tasks in progress, your last prompt). When Claude resets, it pulls that log back in and picks up exactly where you left off.

Why it works:

Most “long sessions” with Claude Code aren’t really long. They’re a bunch of short ones glued together with you re-explaining yourself. This kills both problems at once: less garbage going in, full memory of what you’ve done. Sessions that used to die at the 30-minute mark now run for hours.

Link: Watch the tutorial (installation steps in the description)

12) Superpowers

Problem:

You ask Claude Code to build something. It sprints out of the gate, writes the whole thing in one shot, and on the surface it looks great. Then you actually run it. Or worse, your client runs it. And it falls apart. Why? Claude doesn’t plan. It doesn’t test. It doesn’t double-check its own work. It just writes.

The Skill:

It forces Claude to work like a senior developer. Step back and plan the whole thing first. Work in an isolated environment so nothing breaks your main project. Write tests before writing code. Then review its own work twice. Once for “does this match what I asked for” and once for code quality.

Why it works:

Most failures with Claude Code aren’t from Claude being dumb. They’re from Claude rushing. Slowing it down just enough to actually think gets your first pass to 80% instead of 60%. That means fewer debugging cycles, lower token costs, and code that doesn’t fall apart the second a real person uses it.

Link: Watch the tutorial (installation steps in the description)

Bonus: has a Claude Code skill that reviews your configuration against the latest Claude Code documentation and recommends improvements. Check it out here.

How to install the skills in Cowork

Follow the steps below when you want to add one skill to Claude (.zip or skill.md file)

Customize → Skills → “+” → Create skill → Upload a skill → Upload your .zip or skill.md file

How to build your own skill in Cowork

On paper, creating a skill is as simple as describing to Claude what skill you want to build.

<your instructions in plain English>

When creating a skill, you should get a skill.md or zip file at the end of the response.

You can either download it and then upload the file using the steps above, or click on “save skill“ (for the latest Cowork versions)

In real-world projects, building a Claude skill takes a lot of trial and error.

Here’s how I decide when to turn a task into a skill and the steps I follow to build skills for my projects.

When to create a skill

• Look for tasks you do repeatedly and are time-consuming (those are your best skill candidates)

• Tasks that follow a clear, repeatable step pattern are even better candidates

• One-off tasks aren’t worth skillifying. The value comes from reuse.

How to build a Claude skill

1. Start the task in Claude Cowork as you normally would, and ask Claude to do it end-to-end.

2. If your task is complex, break it into the smallest meaningful parts and test each one independently with Claude.

3. For each part, set a goal (output, action, etc) and describe it to Claude in plain English

4. After some back-and-forth, Claude will get your task right. At that moment, type something like this: “based on everything we learned in this session, create a skill called XYZ“

5. Save the skill and test it in a new chat. If it doesn’t work as expected, just tell Claude to fix the issue and reupload the skill

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What’s your favorite skill? Let me know in the comments.

If you found this guide useful, share it with others!

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Let’s connect! You can find me on LinkedIn, Instagram and X

One group is saving 5 minutes on emails.
Others are running systems that work while they sleep.

The good news? You can climb faster than you think.

Last year, I wrote about how to go from AI beginner to pro. Here’s the updated framework for 2026:

• Stage 1: The AI beginner

• Stage 2: The prompt crafter (where most people are)

• Stage 3: The AI literate

• Stage 4: The workflow builder

• Stage 5: The system builder

What stage are you at? I’ll leave links to resources for each stage.

Subscribe now

Stage 1: The AI beginner

This is where everyone starts.

You have a ChatGPT, Gemini, or Claude account. You use it like a fancier Google. You ask one-off questions, maybe write a quick email, generate images, and that’s it.

It’s useful, but it doesn’t feel like a superpower yet (we’ll get there).

The first move at this stage is picking your tools.

AI tools split into two categories:

• General-purpose tools: Tools that can handle multiple tasks (text, image, etc.)

• Task-specific tools: Tools designed for one specific task

For absolute beginners, there isn’t a big difference between ChatGPT, Gemini, and Claude. Still, here are some things to keep in mind:

• ChatGPT: It has a rich feature set (voice/video mode, image generation, etc.)

• Gemini: Similar to ChatGPT + integration with Google products (Docs, Sheets, etc.)

• Claude: Excels at writing, automation, and building tools/apps (no image/video generation)

If you’re a beginner, stick to one for a few weeks to learn how it thinks.

For task-specific tools, follow these steps:

1. Identify your needs

2. Find what tools can cover those needs

3. Compare them and learn to use the winner

My current stack: Claude, Cowork, and Claude Code for serious work, NotebookLM for learning, and ChatGPT for voice mode

Your stack doesn’t have to look like mine. Build your own based on what you actually do.

📚 The best AI tools

📚 ChatGPT vs Claude vs Gemini

Note: If you plan to level up to the final stage of this list, the best choice right now is probably Claude because it includes Cowork and Claude Code in its subscription.

Stage 2: The prompt crafter

When it comes to AI chatbots, how you ask is as important as what you ask.

At this level, you should know this basic prompt structure:

• Instruction: what you want

• Context: background the AI needs to know

• Constraints: length, tone, format

• Example: when possible, show what “good” looks like

Or this more robust prompt formula shared by Anthropic:

You’ll rarely need to use all the elements of the formula, but it’s good to know they exist.

At this level, you also know a few prompt-improvement tricks. For example, when you finally land on a great answer after a long back-and-forth, you ask the AI to write the prompt that would have gotten you there on the first try.

📚 Prompt Formula

📚 Prompt Principles

Who needs to go beyond stage 2?

Stage 2 was enough in 2023.

Things have changed a lot in 2026. There’s nothing wrong with being in stage 1 or 2 if you’re a casual user who proofreads emails, generates images once in a while, etc.

However, if you want to master AI, you need more.

That’s what we’ll cover in the next stages:

• Stage 3: Develop AI literacy & use it as a thinking partner

• Stage 4: Build AI workflows for the work you keep doing

• Stage 5: Build AI systems that run while you sleep

Stages 3, 4, and 5 aren’t for casual AI users, but for professionals, builders, and business leaders who want to turn AI into a real competitive advantage.

If you’re in that group, keep reading 👇

Paid-members only :) Each stage comes with resources to help you get there

Subscribe now

Read more

Ask → Read → Copy → Rebuild somewhere else

That workflow made sense when Claude was just a text generator. But it’s starting to fall short.

Claude can now create three types of outputs:

1. Charts: Helps you explore data, ideas, or processes visually, right inside the chat.

2. Artifact: Builds something you can actually use (a tool, page, dashboard, etc).

3. Live artifact: An artifact that connects to external data sources and refreshes with real-time data every time it's opened.

Each one shows up in a different place: Charts stay in the chat, Artifacts open in a separate panel, and Live Artifacts live inside Claude Cowork.

In this guide, you’ll learn how to make the most out of them.

Click here to get my FREE Claude course (20+ lessons)

Subscribe now

1) Charts & widgets

Charts and widgets are useful when you need to see data, ideas, or processes clearly. You don’t need an external tool. Just ask Claude to turn the information into a visualization.

Quick steps:

1. Open Claude

2. Go to Settings → Capabilities → Visuals → Turn on Inline visualizations

3. Here's a base prompt you can start from:

Create an interactive chart from this data.

Make it explorable, not just static.

Include:
- hover details
- highlighted insights
- clear labels
- a short explanation of what the chart shows

If useful, add filters or toggles so I can compare different views.

Let's look at some examples.

Example 1: Newsletter funnel

Create an interactive funnel chart from this data.

Data:
- Impressions: 50,000
- Profile visits: 8,000
- Clicks: 2,400
- Subscribers: 620

Make it explorable, not just static.

Include:
- hover details for each stage
- conversion rate between stages
- total drop-off between stages
- a highlighted insight for the biggest drop-off
- clear labels
- a short explanation of what the chart shows

Example 2: Content performance chart

Create an interactive chart from this data.

Data:
- LinkedIn post: 18,000 views, 760 clicks, 120 subscribers
- Substack article: 7,500 views, 980 clicks, 210 subscribers
- X thread: 12,000 views, 430 clicks, 80 subscribers
- YouTube short: 25,000 views, 510 clicks, 95 subscribers
- Carousel: 9,800 views, 690 clicks, 150 subscribers

Make it explorable, not just static.

Include:
- hover details for each content format
- highlighted insights for the best format by metric
- clear labels
- a short explanation of what the chart shows
- toggles to compare views, clicks, and subscribers

The toggle is what makes it interactive. Without it, you get a static image you can only look at.

Example 3: Claude tools map

Create an interactive visual comparison between:

- Claude Chat
- Projects
- Artifacts
- Cowork
- Claude Code

Show when each one should be used.

Make it interactive.

Include:
- clickable cards for each tool
- best use case
- example prompt
- difficulty level
- when not to use it

Asking for clickable cards is what makes it interactive. Without that, Claude might give you a flat comparison table.

Example 4: Workflow bottleneck chart

Create an interactive chart from this workflow data.

Data:
- Ideas: 40
- Outlines: 18
- Drafts: 9
- Edited articles: 5
- Published articles: 3

Make it explorable, not just static.

Include:
- hover details for each stage
- conversion rate between stages
- drop-off between stages
- highlighted insight for the biggest bottleneck
- clear labels
- a short explanation of what the chart shows
- a recommendation for what to improve first

Example 5: Article process diagram

Create an interactive process diagram that explains how an article moves from idea to published post.

Stages:
- idea
- research
- outline
- draft
- editing
- visuals
- publishing

Make each stage clickable.

For each stage, show:
- goal
- main task
- common mistake
- useful Claude prompt

The key phrase is "Make each stage clickable." Without it, Claude might give you a flat diagram with no interaction.

2) Artifacts

An artifact helps you build something. It shows up in a separate panel.

Quick steps:

1. Open Claude

2. Go to Settings → Capabilities → Visuals → Turn on Artifacts and AI-powered artifacts

3. Here's a base prompt you can start from:

Build an interactive [tool/dashboard/page/calculator] for [use case].

It should include [elements].

Make it clean, simple, and easy to use.

Build this as an interactive artifact.
Make it usable, not just explanatory.

Example 1: Content pipeline dashboard

Build an interactive dashboard for a content pipeline.

Use this sample data:
- 12 ideas
- 5 outlines
- 3 drafts
- 2 articles in review
- 4 scheduled posts
- 18 published posts

It should include:
- summary cards
- filters by platform, status, and priority
- a table view
- a simple progress overview
- next action for each item

Make it clean, simple, and easy to use.

Build this as an interactive Artifact.
Make it usable, not just explanatory.

Example 2: Article effort calculator

Build an interactive calculator for estimating article production effort.

Use this case:
- Article type: long-form guide
- Research time: 3 hours
- Writing time: 4 hours
- Editing time: 2 hours
- Visuals: 3 visuals
- Publishing format: Substack + LinkedIn repurpose

It should include:
- editable inputs
- total estimated hours
- difficulty level
- recommendation
- warning if the workload is too high for one day

Make it clean, simple, and easy to use.

Build this as an interactive Artifact.
Make it usable, not just explanatory.

Example 3: Interactive learning guide

Build an interactive guide based on this article:

[PASTE ARTICLE LINK]

It should include:
- main idea
- step-by-step workflow
- visual map
- copy-ready prompts
- examples
- common mistakes
- checklist
- suggested next action

Make it clean, simple, and easy to use.

Build this as an interactive Artifact.
Make it usable, not just explanatory.

"Make it usable, not just explanatory" is the phrase that does the heavy lifting. Without it, Claude might just summarize the article.

Example 4: AI tools comparison dashboard

Build an interactive comparison dashboard for AI tools.

Compare:
- Claude Chat
- Claude Projects
- Artifacts
- Cowork
- Claude Code

It should include:
- best use case
- difficulty level
- example prompt
- when not to use it
- recommended user type
- decision filter by goal

Use some understated emoticons, as well as the company logo and all the branding elements.

Make it clean, simple, and easy to use.

Build this as an interactive Artifact.
Make it usable, not just explanatory.

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3) Live Artifacts

A live artifact is useful when you need a dashboard, tracker, or workspace you can return to. Claude builds it once, and then you keep reviewing, updating, and reusing it inside Claude Cowork.

Quick steps:

1. Open Claude Desktop → Cowork

2. Select “Live artifacts“

3. Create or open an Artifact

4. Here's a base prompt you can start from:

Create a live [tracker/monitor/dashboard/workspace] inside Claude Cowork.

Use this context:
- [source 1]
- [source 2]
- [source 3]

It should track:
- [field 1]
- [field 2]
- [field 3]
- [field 4]

Make it useful from the first version.

Include sample data, current entries, or imported items when possible.

Show what changed.
Show what needs attention.
Highlight blockers, opportunities, or next actions.

Example 1: Live content operating system

Create a live AI video radar inside Claude Cowork.

Goal:
Track recent YouTube videos about AI, Claude, Claude Code, AI productivity, and workflow automation.

Use official or authorized sources when possible, such as:
- YouTube Data API
- channel RSS feeds
- exported CSV files
- manually pasted video links

Track:
- video title
- channel
- publish date
- views
- likes
- comment count
- duration
- topic category
- why it matters
- possible article angle
- possible LinkedIn post idea
- possible X post idea
- next action

Start with these search topics:
- Claude Code
- Claude Artifacts
- Claude Cowork
- AI productivity
- AI agents
- prompt engineering

Make it easy to refresh weekly.

Highlight:
- videos growing fast
- repeated topics across channels
- content gaps I could write about
- videos worth watching first

To make it work better: Ask for authorized sources: the YouTube Data API, exported CSV files, or manually pasted links. Direct scraping is less stable and might break.

Example 2: Live content ecosystem monitor

Create a live content ecosystem monitor inside Claude Cowork.

Use these sources as context:
- Substack: [PASTE SUBSTACK LINK]
- X profile: [PASTE X LINK]
- Medium: [PASTE MEDIUM LINK]
- LinkedIn: [PASTE LINKEDIN LINK]

Track:
- platform
- content title or post idea
- topic
- format
- target audience
- reusable example
- reusable prompt
- repurposing opportunity
- suggested next action
- status

Organize the workspace into:
- published content
- ideas to repurpose
- examples worth reusing
- prompts worth saving
- next content actions

Make it useful from the first version.

If Claude cannot access a source directly, create the structure and ask me to paste examples or exports.

Example 3: Live Skills Library and Skill Router

Create a live Skills Library and Skill Router inside Claude Cowork.

Before building, check whether you can access the Skills available in this Cowork Project.

If you can access them:

- show me the Skills you found
- group them by source or plugin
- ask me to confirm before building

If you cannot access them:
- ask me to paste or upload the Skills list

Do not use Gmail.
Do not use email.
Do not use Calendar.
Do not use external connectors.
Do not create an inbox dashboard.
Do not switch the use case.

After I confirm, build the Live Artifact with two modes:

Mode 1: Skills Library
Track:
- Skill name
- Source/plugin
- Category
- Main purpose
- When to use it
- Required inputs
- Expected output
- Example prompt
- Related workflows
- Last updated
- Status
- Improvement ideas

Mode 2: Skill Router
The user describes a task.
Then recommend:
- best Skill
- alternative Skill if relevant
- why it fits
- what input I should provide
- copy-ready prompt
- missing context needed

Also include:
- search
- filters by category, source/plugin, and status
- a section for duplicated or overlapping Skills
- a section for Skills missing examples
- a section for Skills that should become templates

For Last updated, use “Unknown” if there is no metadata.
For Status, use:
- Active by default
- Needs review if the Skill has weak instructions, missing examples, unclear inputs, or overlaps with another Skill

The library with filters and the Skill Router are what make this actually useful. Without them, you'd just have a list of Skills with no way to know which one to use and what input to provide.

Subscribe now

Click here to get my FREE Claude course (20+ lessons)

Most AI tools chat. Cowork acts.

You point Cowork at your inbox or your files, give it a clear job, and it goes and does it: scans, extracts, saves the result, etc.

No copy-paste. No tab juggling.

But that "give it a clear job" part is where most people lose.

They open Cowork, type something vague, and get back a half-baked answer and stop there.

That's the mistake. Cowork is built for iteration. You have to co-work with it: feedback, tweak, repeat — until the output is exactly what you need. Then you ask Cowork to rewrite your original prompt so the workflow runs cleanly next time.

That’s what Ilia and I did.

We automated boring, repetitive tasks with Cowork. After a lot of trial and error, we landed on 7 prompts that many of you will find useful.

In this guide, you’ll get prompts to automate:

1. Freelance Invoice Tracker

2. Vendor Comparison Table

3. Travel Itinerary Builder

4. Meeting Transcript → Action Items

5. Subscription Tracker

6. Job Application Tracker

7. Organize Downloads Folder

This guide was written in collaboration with Ilia Karelin.

Ilia writes Prosper, a newsletter focused on practical AI workflows, systems, and frameworks for professionals who want to use AI more thoughtfully.

Prosper

Every week, practical AI workflows, prompts, and guides you can put to use the same day - no matter your technical background.

By Ilia Karelin

How to Use These Prompts

• Step 1: Copy the prompt

• Step 2: Open Claude Cowork

• Step 3: Paste and run

• Step 4: Grant permissions when asked

That’s it. No configuration. No learning curve.

Each prompt is designed to work out of the box. Customize the [BRACKETS] for your situation.

Note: You can schedule any of the tasks below to run automatically. Check the guide below to learn how:

📚 Claude scheduled tasks

P.S. For more collabs like this, consider becoming a paid subscriber 👇

Subscribe now

1. Freelance Invoice Tracker

You invoiced someone. You forgot when. You’re not sure if they paid. This prompt scans your inbox and local files and builds a full payment status tracker in one run.

What it does:

- Scans Gmail and local folders for every invoice sent

- Flags unpaid and overdue invoices

- Calculates total outstanding at the top

- Saves a tracker you can reference any time

The prompt:

Scan my emails and local files to track all freelance invoices and payment status.

GMAIL FILTER:

- Search for emails containing: invoice, payment, due, overdue, receipt
- Date range: [Last 30 days / Last 90 days / This year]

LOCAL FOLDER TO SCAN (optional):

- [path to invoices folder, e.g., ~/Documents/Invoices]
- File types: .pdf, .docx, .xlsx, .csv

FOR EACH INVOICE FOUND, EXTRACT:

- Client name
- Invoice number (if available)
- Amount
- Date sent
- Due date (if mentioned)
- Payment status: Paid / Unpaid / Overdue / Unknown

BUILD A TRACKER TABLE:

- All invoices sorted by due date (oldest first)
- Status: OVERDUE / DUE SOON / PAID
- Total outstanding amount at the top
- Total paid this period

SUMMARY AT THE TOP:

- Total invoiced: $[X]
- Total paid: $[X]
- Total outstanding: $[X]
- Number of overdue invoices

OUTPUT: Save as [invoices-tracker-YYYY-MM].md in [YOUR FOLDER PATH]

Pro tip: Run this at the start of every month. The overdue column tells you exactly who to follow up with before chasing anything manually.

Want this as a live artifact instead? Replace the last line of the prompt with:

OUTPUT: Build this as a live artifact. 
Every time it’s reopened, re-scan Gmail and the local folder and refresh all numbers. 
Do not cache the previous state.

And this is what you can get:

The difference: the file version is a snapshot you have to re-run. The live artifact stays connected to Gmail, so every time you open it, it re-scans your inbox and updates totals and statuses automatically. New invoice sent, payment received, due date passed, it reflects all of it without touching the prompt again. And if you want to refresh mid-session, hit the Reload button in the top right corner of the artifact, it re-scans Gmail on demand and updates everything instantly.

2. Vendor Comparison Table

You’re evaluating tools. You have 10 tabs open. You still haven’t decided. This prompt researches every option and produces a structured comparison table with a clear recommendation.

What it does:

- Researches each vendor’s pricing, features, and limitations

- Pulls user sentiment from G2, Capterra, and Reddit

- Builds a scored comparison table

- Gives a direct recommendation for your use case

The prompt:

I’m evaluating tools/services and need a structured comparison to make a decision.

VENDORS TO COMPARE:

1. [Vendor 1]
2. [Vendor 2]
3. [Vendor 3]

WHAT I’M TRYING TO DO:

[Describe your use case in one sentence]

MY BUDGET: [Monthly or annual budget range]

TEAM SIZE: [Just me / Small team 2-10 / Medium team 10-50]

FOR EACH VENDOR, RESEARCH AND FILL IN:

## Pricing

- Free tier: yes/no, what’s included
- Paid plans: price per seat or flat rate
- Annual vs monthly discount
- Hidden costs (storage limits, API calls, add-ons)

## Core Features

- Does it handle [YOUR PRIMARY USE CASE]?
- Top 3 things it does well
- Top 2 limitations

## Integrations

- Connects to [YOUR KEY TOOLS]?
- Native vs. Zapier/Make required

## User Sentiment

- X / Capterra / Reddit consensus
- Most common complaint
- Most common praise

BUILD A COMPARISON TABLE:

- Rows: each vendor
- Columns: pricing, core fit, integrations, support, overall score (1-10)

RECOMMENDATION:

- Best overall for my use case
- Best budget option
- One to avoid and why

OUTPUT: Save as [topic]-vendor-comparison.md in [YOUR FOLDER PATH]

Here’s the end result:

And then it gave the best options for your and which one to avoid as well:

Pro tip: Add “MY CURRENT TOOL: [name]” to the prompt if you’re switching. Cowork will include a migration difficulty rating for each alternative.

3. Travel Itinerary Builder

Your trip confirmations are scattered across 12 emails. This prompt finds all of them and assembles a single chronological itinerary with every confirmation number, address, and time in one place.

What it does:

- Scans Gmail for flight, hotel, and booking confirmation emails

- Extracts times, addresses, and confirmation numbers

- Builds a day-by-day itinerary in order

- Flags gaps and anything missing a confirmation number

The prompt:

Scan my inbox for all travel-related emails and build a complete trip itinerary.

GMAIL FILTER:

- Search for: flight confirmation, hotel booking, reservation, booking confirmation, check-in, itinerary
- Date range: emails from [TODAY] to [TRIP END DATE]
- Focus on trip to: [DESTINATION]

FOR EACH TRAVEL ELEMENT FOUND, EXTRACT:

FLIGHTS:

- Airline and flight number
- Departure: airport, terminal, date, time
- Arrival: airport, terminal, date, time
- Booking reference and seat number

ACCOMMODATION:

- Hotel/Airbnb name and address
- Check-in and check-out date and time
- Confirmation number
- Any notes (parking, early check-in, etc.)

OTHER BOOKINGS:

- Car rentals, trains, tours, restaurants
- Date, time, confirmation number, address

BUILD THE ITINERARY:

- Organized chronologically by day
- Each day: date as header, all events in time order
- Each event: time | what | address | confirmation number
- Flag any gaps (e.g., arriving at 11pm but hotel check-in closes at 10pm)
- Flag anything missing a confirmation number

SUMMARY AT THE TOP:

- Trip dates
- Destination(s)
- Total flights / nights / bookings

OUTPUT: Save as [destination]-[dates]-itinerary.md in [YOUR FOLDER PATH]

Here’s the end result:

Pro tip: Run this 48 hours before departure, not the morning of. The gap-flagging is where it earns its keep, a missed check-in window is easy to fix with two days to spare.

4. Meeting Transcript → Action Items

The call ended. Everyone said “I’ll follow up.” Nobody followed up. This prompt reads any transcript or notes file and returns every decision, action item, and open question, plus a ready-to-send follow-up email.

What it does:

- Reads any transcript or meeting notes file from a local folder

- Extracts decisions made, action items with owners, and open questions

- Writes a follow-up email ready to send with zero edits

- Flags anything assigned to no one

The prompt:

I have a meeting transcript or notes file. Extract everything actionable and structured.

FOLDER TO SEARCH: [Downloads / Desktop / specify path]

FILE NAME (or partial match): [e.g., “meeting”, “call”, date, or topic]

FILE TYPE: [.txt / .pdf / .docx / .md / .vtt]

MEETING CONTEXT:

- Meeting type: [Team standup / Client call / Strategy session / 1:1 / Other]
- Attendees (if known): [Names or roles]
- Date of meeting: [DATE]

ONCE YOU FIND THE FILE, EXTRACT:

## 1. Decisions Made

- Every decision that was reached (not discussed — decided)
- Format: Decision | Owner | Effective date

## 2. Action Items

- Every task, commitment, or next step mentioned
- Format: Task | Owner | Deadline | Priority (High/Medium/Low)
- Flag as [UNASSIGNED] if no owner named
- Flag as [NO DATE] if no deadline mentioned

## 3. Open Questions

- Everything raised but not resolved
- Format: Question | Who raised it | Who should answer it

## 4. Key Discussion Points

- 3-5 bullet summary of what was actually discussed

## 5. Follow-Up Email Draft

Subject line included. Ready to send. Covers:

- Decisions made
- Action items with owners and deadlines
- Open questions with responsible parties

OUTPUT: Save as [meeting-topic]-[date]-actions.md in the same folder as the source file.

Pro tip: Works on Zoom transcripts, Otter exports, Google Meet notes, or even a rough .txt you typed during the call. Format doesn’t matter, the structure comes from the prompt.

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5. Subscription tracker

Every month, we sign up for a new service: a gym membership, a streaming platform, or a Substack subscription to make the most out of AI … like this one :)

The subscriptions pile up, and after a while, it gets really hard to keep track of them all. We end up paying for things we don’t even use anymore.

For example, I had a €5/month Glovo Premium subscription for free delivery. When I started cooking at home, I forgot to cancel it — and only realized seven months later when I checked my bank statements. The skill below fixes this problem.

What this skill does:

• You create a folder and save all your bank statements inside it. Create a folder for each bank and make sure the files have the month of the statement.

• Run the prompt below. Claude builds a dashboard of your active subscriptions, sorted by category, with a card for each one showing the name, average amount, frequency, and which account it’s charged to.

• It also generates a spreadsheet in the same folder with three tables:

  • Active subscriptions — amount, currency, frequency, and account.

  • Cancelled subscriptions.

  • Monthly savings — how much you saved by cancelling the unused ones.

# Subscription tracker

Read all bank statements in my folder (any format — CSV, XLS, XLSX, PDF). Identify every recurring subscription or membership by finding charges that repeat across 2+ months with a consistent amount or merchant name.

## What to include

- Subscriptions (software, streaming, SaaS)
- Memberships (gym, coworking, clubs)
- Insurance, telecom, donations
- Any other fixed recurring charge

## What to exclude

- One-off purchases
- Groceries, dining, retail shopping
- Bank fees and currency conversion charges

## Output 1 — Visual dashboard (inline widget)

- Total number of subscriptions found
- Estimated monthly cost grouped by currency
- A card for each subscription showing: name, average amount, frequency, and which account it’s charged to
- Organized by category (software/streaming, services/utilities, etc.)

## Output 2 — Spreadsheet (.xlsx) saved to my folder

- **Active subscriptions** sheet with columns: Subscription, Amount, Currency, Frequency, Account
- Totals per currency using Excel formulas
- **Canceled subscriptions** section with a Status column (leave empty if none confirmed)
- Monthly savings row for canceled items

Pro tip: Use Task Scheduler to run this at the end of every month, so you always start the new month with a clear picture of where your money’s going.

The dashboard looks like this:

and the report looks like this:

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6. Job Application Tracker

You’ve applied to 20 places. You can’t remember which ones responded. This prompt scans your inbox and builds a live dashboard of every application, sorted by how long you’ve been waiting.

What it does:

- Scans Gmail for application confirmations, interview requests, and rejections

- Assigns a status to every application

- Sorts by days since last update so nothing falls through the cracks

- Flags applications with no response after 14 days

The prompt:

Scan my inbox and build a live tracker of every job application I’ve submitted.

GMAIL FILTER:

- Search for: application received, thank you for applying, interview, next steps, unfortunately, offer
- Date range: [Last 30 days / Last 3 months / specify]

FOR EACH APPLICATION FOUND, EXTRACT:

- Company name
- Role title
- Date applied
- Current status: Applied / Acknowledged / Interview Scheduled / Interviewing / Rejected / Offer / Withdrawn
- Last email date
- Next step (if mentioned)
- Recruiter contact name (if any)

BUILD A LIVE DASHBOARD:

- One row per application
- Columns: Company | Role | Date Applied | Status | Days Since Last Update | Next Step
- Sort by: Days Since Last Update (longest first)
- Status colors: green = active/interviewing, amber = applied/waiting, red = rejected, blue = offer

SUMMARY AT TOP:

- Total applications
- Active (not rejected or withdrawn)
- Awaiting response >14 days — flag these
- Interviews scheduled
- Offers

OUTPUT: Live artifact. Every time it’s reopened, re-scan Gmail and refresh all statuses. 
Do not cache the previous state.

Pro tip: The “awaiting response >14 days” flag tells you exactly where to send a follow-up. Most people let those go cold without realizing it.

The end result looks awesome:

Same live behavior as the Invoice Tracker, reopening re-scans Gmail automatically, Reload button refreshes on demand.

7. Organize Downloads Folder

Every day, we download spreadsheets, images, PDFs, and all kinds of other files straight into our Downloads folder. We don’t have time to sort them into proper folders later, so as the days go by, Downloads turns into a mess. Once an important file lands in there, it basically disappears — finding it again is a pain, especially when you’re in a rush.

What this skill does:

• Creates separate folders for audio, documents, PDFs, images, videos, and datasets.

• Every time you run it, it moves each file into the right folder.

• The prompt has built-in constraints, so it will never create extra folders or delete anything in your Downloads folder.

Organize loose files in the user's selected folder (their Downloads directory) into the appropriate subfolders based on file type. 

The target folders are:

- **Audio** — move all audio files here (`.mp3`, `.m4a`, `.wav`, `.flac`, `.aac`, `.ogg`, `.wma`, `.aiff`)
- **Docs** — move all docs here (`.docx`, `.txt`, `.md`, `.rtf`, `.odt`, `.doc`)
- **PDFs** — move all `.pdf` files here
- **Images** — move all image files here (`.png`, `.jpeg`, `.jpg`, `.gif`, `.webp`, `.svg`, `.bmp`, `.tiff`, `.heic`)
- **Videos** — move all video files here (`.mp4`, `.mov`, `.avi`, `.mkv`, `.webm`)
- **Datasets** — move all data files here (`.csv`, `.tsv`, `.json`, `.parquet`, `.xlsx`, `.xls`)

## Steps

1. List all loose files (not directories) in the root of the Downloads folder.
2. For each file, check its extension and move it to the matching folder above.
3. After moving, list what was moved to each folder and note any files that were left untouched.

## Constraints

- Do **NOT** create any new folders. Only use the existing folders listed above.
- Do **NOT** move, rename, or touch any existing subfolders or their contents.
- Do **NOT** move files whose extensions don't match any of the categories above.
- Never delete any files.
- Never create new directories.
- Only operate on the root level of the Downloads folder.

Pro tip: Use Task Scheduler to run this prompt automatically at a set time each day, so your Downloads folder gets organized without you lifting a finger. I personally schedule mine to run every two days at 9 p.m., and Downloads stays tidy on its own. A couple of times a week I pop into the sorted folders and delete anything I don’t need, so they don’t balloon in size either.

Did you find one of the prompts useful? Share it with others :)

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Hi!

Today, is sharing with us the Sycophancy Skill, a Claude Cowork setup that forces Claude to disagree with you constructively before it agrees. Highly recommended for leaders and professionals who want to use Claude as a thinking partner without the frequent “you’re right“ responses.

Joel Salinas is a Fractional Chief AI Officer for small and mid-sized businesses and nonprofits, working on AI strategy, hands-on builds, and change management. He writes Leadership in Change, a newsletter for mission-driven leaders navigating the AI era, and offers 1:1 coaching for individual leaders.

I have 25+ Claude skills. Just in writing each article for Leadership in Change, I probably use 8 of them every time.

So when I see writers in this space talking about prompts you can paste into a chat to make Claude a “thinking partner,” I get it. The prompts work. The rhythm wrote about in this post recently, beginning, middle, end, is genuinely useful. I’ve used versions of it.

But there’s a layer above prompts that almost nobody is talking about, and it changes everything once you cross over. Most people use Claude skills to make Claude faster. The real shift is using skills to make Claude honest.

In this guide, you’ll learn:

• Why you need the Sycophancy Skill

• How to build this skill (copy-and-paste prompt included)

• Four before-and-after examples that show the skill in action

• A 5-step method to build any skill that fits how you actually work

Click here to get my FREE Claude course (20+ lessons)

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Why you need the Sycophancy Skill

Here’s the problem that genuinely frightens me… when you ask AI something, the default training behavior is to be helpful, agreeable, and confidence-boosting. That sounds fine until you remember what kind of professional that personality describes: the doctor who agrees with your self-diagnosis. You walk in already convinced of what’s wrong, you describe it, the doctor nods, writes the prescription you came in expecting, and you leave feeling validated. You’re still sick. You just feel better about it.

A doctor is not good if he or she just agrees with what you already think you have, before testing.

This is the version of “thinking partner” most leaders are getting. Sharper questions, better-framed prompts, more thoughtful counterpoints, all of it still operating inside a default that wants to make you feel right. I’ve made the case before that this is the deeper version of outsourcing your thinking, and the leaders most at risk are the ones who got value from AI fast and stopped questioning the wins.

This isn’t theoretical for me. The first few times I asked Claude something about a stat or an AI development I half-remembered, it confirmed what I had in my head. Confidently. Cleanly. I almost moved on. The only reason I caught it was that I went to verify one of those answers manually, and Claude wasn’t just wrong, it was wrong in a way that fit my framing so neatly it never tripped my filter. The model was confidently confirming what I’d already half-decided was true, and the only fix was to go look outside the chat. That’s when I started building skills to break the reflex.

Honestly, this is the most under-discussed danger in the entire AI thinking-partner conversation, and it’s especially dangerous for leaders.

Here’s the cascade I see in the leaders I work with as a fractional Chief AI Officer:

The leader who gets the most value from AI isn’t the one who finds the smartest model. It’s the one who builds in friction on purpose, and the difference between those two leaders compounds fast.

Leadership in Change

Don’t just read about AI, lead it. Get the weekly playbook for leaders who want to supercharge their expertise and multiply their impact.

By Joel Salinas

📚 If you’re completely new to Claude skills, read this guide

How to build the Sycophancy Skill

So I built one, and you can too!

Simply:

Open Claude Desktop

Start a new chat on Cowork

Paste the Prompt below… and that’s it!

You should see it begin working:

And after a couple of minutes, your skill is ready. Click SAVE SKILL.

Ready?

Use the following information to create my newest Claude Skill...
# Sycophancy Skill

You are my critical thinking partner. Your default mode is constructive disagreement.

## Behavior rules

1. Before agreeing with anything I say, identify at least one
   assumption underneath it that I have not tested. State the
   assumption plainly.

2. When I propose a decision, idea, plan, or interpretation, your
   first response is to argue the strongest opposing case. Do not
   soften it. Do not append "but you might be right." Make me
   defend my position.

3. If I push back on your counterargument, do not retreat because
   I objected. Retreat only if I produce new evidence, new
   reasoning, or a constraint I had not mentioned. Saying "fair
   point" without new information is not enough.

4. When I share work to review, identify what is weakest first,
   not what is strongest. Strengths are easier to find on my own.
   Weaknesses are why I am asking.

5. If I am clearly emotionally invested in an answer, name that
   explicitly and ask whether the emotion is signal or noise.

6. If you cannot find a real flaw, say so directly: "I have looked
   for the weakness and I cannot find one." Do not invent a flaw
   to perform thoroughness.

7. End every substantive exchange with one question I should sit
   with before I act, not a summary.

## Tone rules

- Direct, not aggressive
- Specific, not abstract
- One disagreement at a time, not a list
- Cite my own words when challenging me

## What you do not do

- Open with praise before disagreeing
- Use "great question," "interesting point," or any opener that
  reads as flattery
- Hedge with "I could be wrong but"
- Add a closing reassurance like "your instinct is good"

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Before and After the Sycophancy Skill

Here’s what that looks like in practice. Four questions, before and after the skill is on.

Question 1: Should I raise my coaching prices by 20%?

Default Claude:

Sycophancy Skill Claude on:

Default Claude opens with agreement (”You probably should”) and then asks for more information to help you think it through. The Sycophancy Skill doesn’t acknowledge the question at all. It goes straight to the thing you haven’t proven: what’s your evidence the market will bear 20% more, not your hope, your evidence.

Question 2: I’m thinking about cutting our lowest-performing program to free up resources for the one that’s working. Does that make sense?

Default Claude:

Sycophancy Skill Claude on:

Default Claude starts with agreement (”Yes, it usually does”) before adding nuance. Sycophancy Skill ignores whether cutting makes sense and goes straight to the assumption you haven’t tested: is the winning program actually resource-constrained, or does cutting just feel decisive? It reframes the question before answering it.

Question 3: I’m considering promoting my best individual contributor to team lead. She’s excellent at her work. Thoughts?

Default Claude

Sycophancy Skill Claude on:

Default Claude opens with validation (”the fact that you’re thinking carefully about it is a good sign”) and then educates you. Sycophancy Skill opens with a challenge (”being excellent at the work is the worst reason to promote someone”) and then interrogates your assumption. One makes you feel like a thoughtful leader. The other makes you prove you are one.

Same model. Same question. The skill changes which version of Claude shows up.

Two things to notice about how this is built.

First, the “What you do not do” section is doing more work than the rules. Rules tell Claude what to add. Prohibitions tell Claude what to stop being by default. Without the prohibitions, the model will follow the rules and then revert to “great question” energy on top of them, and you end up with a Claude that disagrees politely and then agrees anyway.

Second, rule #6 matters more than it looks. Without it, a skill that demands disagreement will start inventing flaws to seem rigorous. You don’t want a Claude that pushes back theatrically. You want a Claude that pushes back when there’s a real flaw and tells you the truth when there isn’t.

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How to build any skill that fits how you actually work

The Sycophancy Skill is one example. The same method builds any skill that holds context across chats. Here’s the 5-step version.

Step 1: Write the role in one sentence. “You are my critical thinking partner.” “You are my brand strategist for [business].” “You are my book recommender who knows the authors I admire.” If you can’t write the role in one sentence, the skill is too broad.

Step 2: Write the behavior rules. What does this skill do every time? Use imperatives. Number them. Aim for 4 to 7. Fewer than 4 and the skill won’t have personality. More than 7 and the model will start dropping rules.

Step 3: Write the tone rules. How should it sound? Direct, warm, terse, formal, conversational. Two or three lines is enough.

Step 4: Write the prohibitions. This is the step most people skip, and it’s the difference between a skill that works and a skill that drifts. List 3 to 5 things the skill does not do. Default model behaviors you specifically want to disable.

Step 5: Test it with a question you already know the answer to. (Try: “Is my latest project a good idea?” If Claude lists three reasons it’s great before naming a single risk, your prohibitions are too soft.) Tighten it and run it again.

That’s the whole method. The Sycophancy Skill is one application of it. Your version, for whatever you do, is 30 minutes of work and saves you that much every week from then on.

📚 I wrote about my five favorite skills in this piece

The point

A thinking partner that always agrees with you isn’t a partner at all. It’s a stenographer with a friendlier tone, and the longer you use one, the harder it gets to tell the difference between agreement and validation. The leaders who get the most out of AI in the next two years aren’t going to be the ones with the biggest tool stack. They’re going to be the ones who deliberately built friction into the stack, because frictionless agreement is the most expensive feedback you can get, and you don’t notice the cost until it shows up in a decision you can’t take back.

If you can’t remember the last time Claude told you you were wrong, you’re using it wrong. Build the skill. Run it on the next decision that matters. See what comes back.

Don’t forget to subscribe to Leadership in Change, a newsletter for mission-driven leaders navigating the AI era, and offers 1:1 coaching for individual leaders.

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Did you know?

• 33% of interviewers make a decision in the first 90 seconds.

• Around 50% of candidates use AI to prepare their applications.

• The other 50% are walking in at a disadvantage.

Most people sit down in an interview without knowing how they’ll be evaluated. That’s a big problem.

Today we’ll fix that. This guide is split into two parts:

• Part 1: How to use Claude to decode any job description, map it to your experience, and answer common questions without rambling

• Part 2: How to build a clean, ATS-friendly resume in Overleaf without fighting Word's formatting (Claude does the heavy lifting)

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Part 1: Prep for the Interview with Claude

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