Last month, I listened to a podcast that changed how I think about a project I’m working on. (I’m talking in a very specific, “I know exactly where this applies” way.)

And my first thought when I heard it wasn’t to save it. It was: where does this go so it actually shows up when I need it?

I opened my knowledge folder to figure that out.

What I found was the same setup I’d built three months ago and written a three-part series telling you to build too: 330 frameworks in one alphabetical file, 130 benchmarks in another, a routing index that hadn’t been updated in two months, and a CLAUDE.md instruction that said “load both files at the start of every session.”

Nothing had changed since the day I built it.

None of it had been touched, tested, or questioned since March.

I’d solved the graveyard problem for my Notion archive. Then I’d built a new one out of the fix.

Subscribe

The 415-Line File

Let me show you what “load both files at the start of every session” actually looked like.

My FRAMEWORKS.md was 415 lines long. Alphabetized. Every named mental model, principle, or methodology I’d ever extracted from a book summary, listed one line at a time.

First Principles Thinking sat twelve lines above the Focusing Illusion. The AIDA copywriting formula sat next to the Ackerman Bargaining Model. Bulking Macronutrient Ratios (from a bodybuilding book I’d summarized in 2022) sat right there alongside them.

Every session carried all of it. Writing a Dreem marketing email? Claude had my bodybuilding protein ratios in context. Journaling about a personal decision? Claude had loaded 40 copywriting frameworks I didn’t need. The instruction said “load everything,” so it loaded everything.

BENCHMARKS.md was 130 statistics organized by domain, loaded alongside the frameworks whether or not the session called for them. Ogilvy’s headline stats in one file, the copywriting principles they belonged with in another.

And then there was TOPIC-INDEX.md, a routing layer I’d built to point Claude at the right book summaries for a given topic. There were 26 categories, but I hadn’t updated it since March 29th.

I’d read and summarized new books since then, but the index didn’t know they existed. The setup wasn’t broken. It was working exactly as designed. The problem was the assumption underneath it:

A Knowledge Base Is Something You Maintain

That was the assumption I’d never examined. I’d built a system that required regular attention, then walked away from it for two months.

Under the old system, that podcast would’ve gone into FRAMEWORKS.md as two more lines in the alphabetical list. If it surfaced at all, it would’ve surfaced in every session, alongside everything else.

I started over with the layer on top of the book summaries: the extraction files that were supposed to make the knowledge usable. The goal was simple:

I replaced FRAMEWORKS.md and BENCHMARKS.md with ten pillar files, one per domain: Copywriting & Persuasion, Decision-Making, Marketing & Growth, Writing Craft, Productivity & Systems, Habits & Behavior Change, Mindset & Resilience, Personal Finance, Philosophy & Purpose, Leadership & Business.

Each one is a wiki-style reference with prose entries organized by cluster, not a list of one-liners. Frameworks and benchmarks live together (Ogilvy’s headline stats next to the copywriting principles they support). The total across all ten is around 430 concepts, drawn from the broader canon of each field, not just the books I happened to own.

The curation filter is what separates this from a dump. If a concept wouldn’t survive outside the book it came from, it stays in the book summary.

First Principles Thinking, Loss Aversion, the WRAP Process: in. Bulking Macronutrient Ratios, the Ackerman Bargaining Model: out.

Subscribe

One File, Loaded on Demand

The routing changed. The CLAUDE.md no longer says “load both files every session.” It says “load the relevant pillar when work calls for it.”

For instance, last week, I was writing copy for Dreem lifecycle emails and needed to make sure I was hitting the fundamentals: subject line mechanics, CTA placement, the sequencing principles that separate a drip campaign from spam.

Under the old system, Claude would’ve loaded 415 lines of everything—bodybuilding macros, decision-making biases, the full alphabetical run. Instead, it loaded the Marketing & Growth pillar.

One file sharing the concepts I actually needed, organized by cluster, with the benchmarks sitting next to the principles they support. One file, loaded on demand.

The context window carries what the session needs. That podcast from last month? It went into the Marketing & Growth pillar. Two concepts earned their spot. The rest stayed in the archive.

My knowledge folder has stopped being a library I visit, and instead, it’s become the thing I wanted Notion to be three years ago: a place where what I’ve read shows up in the work, without me having to remember it was there.

Subscribe

If you enjoyed this read, the best compliment I could receive would be if you shared it with one person or restacked it.

Last week I was on a call with a retailer that runs four studios, shoots around 180 products a day, and has been refining their creative direction for over a decade. They’d recently overhauled their ecommerce photography with more relaxed poses, more elevated styling, and a specific aesthetic they could feel but had never written down.

They were exploring AI-generated content platforms like Dreem, and every question they asked was the same question wearing different clothes. Can the output match our color accuracy? Will the fabric drape look right? Does the model’s pose fit the direction we’ve moved toward?

They weren’t asking whether AI could generate good content. They knew it could. Rather, they were asking whether it could generate content that met their standard. The one built over 15 years of shoots, tens of thousands of product images, and a creative team that can spot what’s off in half a second.

That standard exists. It’s just never been codified. And why should it have been—the people who hold it were always in the loop. Every image passed through someone who knew. Now, for the first time, they’re looking at a production pipeline where that person might not touch every asset. And the question becomes:

Where does the taste go?

Everyone’s focused on model quality, but the bottleneck is somewhere else entirely. It’s the same question I’ve been asking about my own work lately.

And last week, I got an answer I wasn’t expecting.

Subscribe

1,458 Files and a Verdict I Didn’t Expect

I have a Claude skill that turns my newsletter issues into Substack Notes. It knows the structural templates, the note types, and the rhythms. And for months, every note has hit the brief.

But last week I looked at a batch and couldn’t shake the feeling that something was off. The content was right. The structure was right. I just wouldn’t have written them that way. I couldn’t name what was wrong. And that was the problem:

I had taste I’d never articulated.

So I gave Claude my entire content archive. Every piece of content I’ve published over 6 years: 1,458 Note pages, to be exact. LinkedIn posts. Tweets. Words Into Works issues. Substack Notes. More than enough for Claude to reverse-engineer the sentence-level patterns that make something sound like I wrote it.

What came back was a scorecard:

• 10 criteria, each scored 1 to 5. Do my sentences vary in length the way they do in the archive? (My average is 16.7 words, but 42% are 10 words or fewer.)

• Do paragraphs stay short? (1.2 sentences per paragraph. That turned out to be the single strongest structural marker across everything I’ve written.)

• Do I open sentences with “But” and “And”? Do I anchor claims in a specific number, a name, a date?

The scorecard measures whether writing sounds like me. The same principle applies to any standard you’ve built through repetition: the instinct that tells you a strategy deck is missing something, the ear that hears the difference between your good sales calls and your average ones.

Taste is accumulated judgment. And accumulated judgment is what most AI workflows leave out.

I tested it on the 6 Substack Notes from last week’s issue, and scored each one against the 10 criteria.

They averaged 25.7 out of 50.

The verdict: “Generic. Could be anyone.”

The same four criteria failed across all six.

• I start sentences with “But” constantly in my own writing; the skill never did.

• I use parenthetical asides (the kind that feel like thinking out loud) in almost everything I publish; the notes had zero.

• I anchor examples in specific time markers: “last Tuesday,” “six months ago.” The notes floated in a vague present.

• And I use fragments as structural punctuation: three-word sentences that land a point. The notes had none.

The skill had been matching my brief for months, but it had never matched my taste. And I couldn’t see the gap until I had a rubric that could name it.

So I added the scorecard as a gate in the skill itself. Now, after every Note is written, it gets scored. If it falls below 35, the skill rewrites targeting the criteria that dragged it down. The notes don’t reach me until they’ve cleared the bar.

That retailer’s creative team has the same problem at a different scale. Their taste lives in 15 years of shoots, in the way a stylist adjusts a collar, in the half-second judgment call a creative director makes before approving an image.

Now they’re looking at a pipeline where the room gets bigger. And the question is the same one I asked about my Substack Notes: how do you enforce a standard that’s never been codified?

You build the scorecard.

Subscribe

The Standard You’ve Never Written Down

Everyone reading this has an archive.

It doesn’t look like 1,458 markdown files. It looks like:

• Pitch decks you’ve refined over three years.

• Brand guidelines you’ve iterated on quarterly.

• Twelve months of client presentations where you know which ones landed before anyone gave you feedback.

• A portfolio where some pieces feel like you, and others feel like they could be anyone’s.

You already know what good looks like. You just haven’t written it down.

Today’s skill is a Taste Scorecard Builder. Give Claude your archive, whatever that looks like for you, and it builds the rubric, tests it against your recent output, and shows you where the gaps are.

Download it here:

Read more

Last week I ran a voice audit on a newsletter draft. The skill flagged 12 findings: five fatal, four warnings, three notes. Each one quoted the offending text, named the rule it violated, and suggested a rewrite.

Claude delivered it as paragraphs.

I scrolled through the output trying to hold the findings in my head while deciding which ones to fix first. Re-reading the same passage three times to confirm what the suggestion actually was. Losing my place. Then, back to the top.

The skill was doing exactly what I’d asked it to do. The output was correct. The suggestions were useful. But I was the bottleneck, parsing text instead of making decisions.

I realized:

The logic was fine. The surface was wrong. But a wall of correct text is still a wall.

Let me show you two examples of how I’ve started fixing this.

Subscribe

Two Surfaces, Same Principle

The first thing I redesigned was one of my Claude skills that always giving me blocks of text. Every week, I scan Reddit for content ideas for this newsletter. Threads from r/ChatGPT, r/ClaudeAI, r/ClaudeCode, among a few others. The skill reads the top posts from the past seven days and tells me what’s worth paying attention to.

The first version gave me paragraphs. “Here’s an interesting thread about prompt chaining. It has 340 upvotes and the top comment raises a point about...” But I often stopped reading halfway through the third one because I couldn’t tell which ones mattered without reading all of them.

So, I redesigned the output as a table.

Now, each thread gets a row with a relevance score, a one-line summary, and a tag: hook seed, problem evidence, reframe material, or supporting source. Four categories. That’s it. If a thread doesn’t fit one of them, it doesn’t matter how many upvotes it has.

The scan, which I set up as a Scheduled Task in Claude, runs inside the same session where I’m working on the upcoming week’s newsletter. All I need to do is review the table, pick the threads that fit, and I’m already in the context where I’ll use them. The output is a decision I make in passing.

Another example:

At my day job, I run content for Dreem. Every Monday, I need to know what happened in the past seven days: what prospects are saying, which stories are worth telling. The raw material is scattered across Slack, and Chorus, among other channels. Inside are dozens of conversations, prospect calls, internal updates, and more.

Far too much to scan in a table.

So, I built a weekly digest that scans all sources, cross-references recurring themes, and outputs a full HTML page. There’s a top-3 highlight box at the top, and individual story cards below, each with the evidence trail, a suggested channel, and an urgency rating.

Every output should be designed around the next step it needs to create. When you do that, parsing disappears. You stop reading and start deciding.

So, the next time AI gives you something correct but hard to use, don’t refine the prompt. Redesign the output. What’s the next thing you need to do after you see it?

Make the output look like that decision.

Subscribe

If you enjoyed this read, the best compliment I could receive would be if you shared it with one person or restacked it.

Last week, I noticed something I couldn’t explain.

My Claude outputs were getting worse.

A newsletter draft that hedged where it used to be direct. A LinkedIn post that sounded like it was written by committee. Subtle enough that I blamed the new Opus 4.7 model for a week.

Then I opened my files.

They Were Arguing With Each Other

MEMORY.md files, style guides, three skills I’d been refining for months. I started reading them side by side.

The MEMORY.md said to avoid em dashes entirely. The style guide said to use them sparingly. One skill said to default to commas and colons. Another said nothing about punctuation at all, which meant Claude was free to ignore the other two. That was one rule. I found a dozen more like it, each file carrying its own version of the same guidance, each updated at a different time, each slightly different.

And Claude tried to follow all of them at once, splitting the difference into output that followed none of them well.

I realized that every single contradiction came from me doing the right thing. I’d finish a session, notice something off in the output, and give feedback. Claude would save it to a MEMORY.md file. I’d update the skill. A few days later, I’d refine the style guide. Each correction landed where it should have. But the corrections piled up in different files, in different words, at different times.

Subscribe

The Trap Is That You’re Paying Attention

This didn’t happen because I neglected my setup. It happened, ironically, because I couldn’t stop improving it.

I treated my Claude setup the way I treat any professional skill: more reps, more feedback, better results. With AI, that inverted. Every correction persists. Every preference you save becomes a constraint Claude juggles alongside every other preference you’ve ever saved. The constraints don’t expire. They don’t get deprioritized. They just accumulate until Claude is honoring forty instructions that no longer agree with each other.

The people most likely to hit this are the ones who care the most about their setup. You read the articles. You watch the YouTube videos. You refine after every session. You treat your system like a craft. And the system rewards you by slowly turning your output into something that sounds like it was written by a committee of every version of your preferences from the last six months. I’d micro-optimized myself into mediocrity.

Fewer Instructions, Better Output

I’ve now deleted the voice guidance from two skills and one folder’s MEMORY.md file entirely—the style guide already had it. Three files of punctuation rules became one. And I stopped saving every correction as a persistent rule. Some feedback is for this session, but not every session.

I ran the same newsletter prompt that had been giving me committee-voice all week. The draft came back direct, clean, and with one voice. This wasn’t because I’d learned something new about prompting, but because I’d removed six months of micro-corrections all pulling in slightly different directions.

Simplicity was the optimization I’d been looking for. I just had to stop optimizing to find it.

Subscribe

If you found this useful, the best thing you can do is forward it to one person who’s been refining their AI setup and wondering why it’s not getting better. That’s how this grows.

Last week, two articles about Claude landed in my inbox on the same day.

Both came from people I trust, which is usually enough to get me reading. I opened the first one right away and read it all the way through. Both were good, and not just “saved for later” good.

I opened the second in a new tab, and got halfway through, before something pulled me away. I don’t recall what, but I told myself I’d come back to it that afternoon. I didn’t. Then I told myself I’d finish it over the weekend. I didn’t.

In fact, it’s still open in a tab I can see from here. And somewhere along the way, maybe the second time I scrolled past it, maybe the third, I admitted what I already knew.

I wasn’t going to read it. I was never going to read it.

The Tab Pile

You’ve done this. Maybe it’s a tab. Maybe it’s a bookmark folder called something like “AI Tips” with forty items in it.

Maybe it’s a read-it-later app you’ve stopped opening because the count is north of 200.

And if you’re anything like me, the last time you opened one of these articles, you nodded at half of it, told yourself you’d come back to the other half, and closed the tab.

But you haven’t.

This isn’t a discipline problem. You make time for other reading. You get through books, newsletters, client docs.

What you don’t get through is the stack of “here’s how I use Claude” articles, because every one of them describes someone else’s setup, someone else’s Monday morning.

You read it, and nod along, but you can’t figure out what to actually change. You know the advice is good, but it doesn’t know you.

At least, not yet.

Subscribe

The Filter

The thing standing between you and better output isn’t another article.

It’s something that can read your setup and compare what you’ve already built against what the article prescribes.

In fact, if you’re using Claude Cowork, you’ve been encoding your thinking for months.

Global instructions. CLAUDE.md files. Skills you’ve written and tweaked. Folder structures. Context files.

All of it is a record of how you work and what you’ve already decided matters. That’s the filter.

The problem is, if you’ve never compared it to anything, you can’t see what’s covered, what’s missing, or what’s a behavioral nudge versus a real gap.

And until you see the overlap, every new article feels either redundant or overwhelming, and you end up doing the same thing:

Saving it for later.

Running the Filter

That second tab is still open.

But the next article I opened went somewhere.

The idea was simple: what if I could share an article with Claude, and let it tell me, based on everything it already knew about my setup, what I actually needed to build?

Last week I tested it on Ruben Hassid’s excellent read, “How to Stop Hitting Claude Usage Limits.”

Of the 23 habits in the article, 15 were already handled. Five were behavioral nudges, useful but not system-changing, and three were actual gaps.

Here’s what those three turned into, in the same session.

• My ABOUT.md file was 2,067 words; the skill flagged long context files as a known usage drain, trimmed it to 1,390, and pulled a separate skills inventory.

• I had no rule for targeted edits, so Claude was rewriting full documents when I asked for one paragraph.

• I had no session efficiency rule, so after 15+ exchanges context was getting expensive. Now there’s one.

I got three system changes from one article.

Today’s skill is the templatized version of what I ran. Upload it to Claude, drop in a URL for any article, and it does three things in sequence:

• First, it reads the article you submit

• Then, it reads your setup: global instructions, CLAUDE.md files, your skills folder, your context files, your folder structure.

• Finally, it grades each item in three categories: Already Doing, Partially Doing, Not Doing.

Before it builds anything, it pauses and asks which gaps you actually want to close. You choose. Then it works.

The skill ships with a README.md, so your Claude setup knows how to implement it.

Here’s the skill:

Read more

Read more

Read more

My colleague, Emil, sent me a screenshot on Slack last week.

It was his knowledge folder—a structured directory of everything he’d fed into his Claude Code setup.

Podcast summaries converted to MD files. A benchmarks file with 200+ statistics organized by domain. A frameworks reference with 150+ entries, alphabetized and sourced. And, most importantly, a …

Read more

A friend messaged me this week.

He had an assignment due and knew AI could help, but every approach he tried felt wrong. His LLM of choice, ChatGPT, was either too generic, or kept missing the point entirely. He spent an hour prompting and nothing was usable.

So, I asked him one question: “If you had a budget, who would you hire to help with this?”

He paus…

Read more

Last week, Anthropic published a labor market report. The headline finding was predictable.

The one beneath it wasn’t.

Knowledge workers are exposed. Writers drafting briefs. Analysts summarizing reports. Customer service reps fielding first contact. The roles that live inside a laptop are the roles AI is already touching.

But the finding that stopped me was quieter.

There’s a massive gap between what AI can theoretically do and what it’s actually doing. In some categories, AI could handle over 90% of tasks. In practice, it’s covering a fraction of that.

The gap is closing. Nobody knows how fast.

Most career advice lands on “learn AI.” That’s not wrong. It just doesn’t tell you anything you can act on.

So, I built a prompt to find out. Then I ran it on myself. And what came back was uncomfortable.

The Wrong Question

Most people are asking “will AI take my job?” Nobody can answer that honestly right now.

The more useful question:

Which parts of my job are already being automated, and which become more valuable because of it?

The Anthropic research makes a distinction most coverage misses. It separates theoretical capability from observed deployment—what AI could do versus what it’s actually doing in professional settings.

That gap is where your window lives.

If you’re in a knowledge role, some of what you do every day is already commoditized. Not soon. Now. Writing a first draft. Summarizing a document. Answering a routine brief.

These are tasks AI handles at scale, which means their value as skills is falling fast.

The roles that require judgment and accountability—where someone owns the outcome—aren’t shrinking. They’re getting rarer. Which means they’re getting more valuable.

The problem is that most people’s job descriptions don’t distinguish between the two. Everything gets listed at the same level. The commodity tasks sit next to the leverage tasks with no signal about which is which.

That’s what this issue’s prompt fixes.

What The Prompt Actually Does

Here’s what I got when I ran it on myself.

The audit looked at my Claude skill library—fifteen Claude tools I’ve built to automate my own recurring workflows—and flagged it as a liability.

The uncomfortable version, direct from the output? I’d automated myself into commodity territory and called it a competitive moat.

I’m not done figuring this out. But I moved the GTM work to the top of how I describe myself, and stopped leading with the tools I built. That felt like the right first move.

The prompt below takes two inputs.

1. Anthopic’s research, attached as a PDF.

2. And your About Me page (which I wrote about last week here).

That second input is what separates this from asking Claude “is my job at risk?” That question gets you a generic answer about your industry.

Feeding it your actual responsibilities, your specific skills, and your real context, gets you a list of tasks you should stop leading with, and one you’ve been underselling.

The prompt runs a “Career Audit” across four sections.

• The Exposure Audit splits your role in two. On one side: writing first drafts, summarizing briefs, answering routine requests—tasks AI now does for anyone with a laptop. On the other: the judgment calls, the strategic bets, the moments where someone has to own what happens next.

• The Deployment Gap is where the opportunity lives. The research shows a large, closing gap between what AI can theoretically do and what’s actually deployed in professional settings. The prompt finds the friction point specific to your situation—the niche you can occupy before your peers do.

• What To Drop, What To Lead With is the career pivot section. One specific skill or achievement on your current profile that has a shortening shelf life. One under-emphasized detail that should move to the top because it represents something AI can’t replicate.

• The One Move is exactly that. One specific action you can take this week. Not a learning plan. Not a career overhaul. One specific thing, tied to your actual profile, that closes the gap between where you are and where you should be positioning yourself.

The hard constraint running through all four sections: if Claude’s answer could apply to anyone else in your industry, it’s wrong.

The prompt says so explicitly.

Every output has to reference your specific profile. That’s the mechanism that makes it useful rather than reassuring.

Here’s the prompt:

Read more

It's Friday afternoon. I'm at Billund Airport, and my gate hasn't been announced.

I’ve just spent the week in Denmark with the Dreem and Creative Force Sales and Marketing team for two days of strategy sessions covering enterprise positioning, pricing architecture, and go-to-market campaigns.

Every meeting was recorded. I have hours of transcript—decisions made, ideas floated, action items buried in the middle of someone else’s sentence.

And I have no idea what to prioritize on Monday.

You can capture everything and still leave with nothing actionable. The transcript exists. The insight doesn’t. Everything you captured is a knowledge asset. Most people treat it like a filing cabinet.

Closing that gap usually means a Sunday night of manual review, fragmented notes, and a Monday that starts two steps behind. This week I treated the transcripts as a source and let Claude do the synthesis.

I pasted three URLs and got a full week plan before my flight boarded.

Three URLs. One Week Plan.

Today’s prompt needs two things. First, your meeting notes. If they live in Notion or Google Docs, paste the URL directly and Claude pulls them up.

Second, a page that describes who you are and what you’re responsible for. Without it, Claude summarizes the meeting. With it, Claude plans your week.

That responsibilities list is the filter. Every action item, decision, and idea from the room runs through it. Things that don’t touch your job get dropped. Things that do get scored across five dimensions—impact, urgency, alignment, dependency risk, and effort—and ranked.

Notice what’s in that output. Not just what to work on, but why it’s first, what done looks like by Friday, who owns what, and which meetings to schedule. In between, it extracts every project and task, maps them to your role, and ranks them with next actions for each.

Claude also flags things it can’t answer from the transcript alone. Who owns the migration timeline. Whether a specific list exists before briefing a campaign against it. Whether a verbal agreement made two months ago still holds.

Most AI outputs tell you what they know. This one told me what it didn’t. Those gaps were the most important thing to resolve before Monday.

One more thing:

If you’re using Notion, ask Claude to save the plan as a page in your workspace. It lands where your work lives, not in a chat window you’ll never open again.

Here’s the prompt:

Read more

Upgrade to Paid

Last Wednesday, I got this DM.

This reader is reacting to Creative Dispatch—Creative Force’s biweekly newsletter, which I write. To them, it looks like I’ve spent years studying tech journalism.

But I haven’t.

I’m a marketer. I write Creative Dispatch using a prompt workflow I’ve spent months refining (an editor-in-chief, a research assistant, a copyeditor—all AI, all trained to my spec).

That DM described the output of a single conversation, one that started with a LinkedIn post I couldn’t explain, and ended with a mental model I can now use before the next one.

What no one could see was the process behind it.

The Bar Went Quiet

A few weeks before that DM, a colleague was onboarding a Creative Force customer. They’d gone for drinks at the hotel bar afterward when the customer asked: “Any crazy AI stuff I need to know about?”

My colleague dropped his orange puffer jacket on the floor, pulled out his phone, and opened Dreem. Four photos. A few taps. Eight minutes later: a mannequin flat lay, two virtual model shots, and a 5-second product video—all from a jacket crumpled on a hotel floor.

The bar went quiet.

I wrote a LinkedIn post about it that evening. Just the story, the images, and one line that felt right: “For the first time, the answer was yes.”

It became one of the best-performing posts I’d ever published.

The problem is I didn’t know why. And if someone had asked me to write another post like it, I’d have been guessing.

I’ve written before about borrowing professional judgment you know you’re missing. This is the other problem—when you don’t know what you already know.

That bothered me enough to spend an evening trying to find out, asking AI to teach me how a top advertising agency thinks about content that spreads.

What I got back was the mental model practitioners use before they make decisions. The questions they ask. The principles they’re pattern-matching against without even realizing it.

When I tested my post against it, my post was already hitting the principles that matter most.

And the gaps it revealed were just as useful.

Subscribe

How to Borrow Curiosity You Haven’t Built Yet

When you don’t know a domain, you don’t just lack answers. You lack the right questions. And you can’t form the right questions from a place of not knowing.

Ask AI “how do I write viral content?” and you get generic output shaped by generic assumptions. The answer is only as good as the question, and if you’re starting from scratch, you don’t yet know what to ask.

So, today’s prompt doesn’t ask AI for expertise. Rather, it asks AI to hand you the practitioner’s question set first.

That single move changes everything. You’re not asking from your own knowledge—you’re borrowing someone else’s curiosity before you’ve developed any of your own. Questions you wouldn’t have thought to ask, now sitting in front of you.

Next, you answer them, but only for your specific situation. Your context poured into someone else’s structure. That’s where your existing knowledge becomes useful. (If you want to turn what you discover into reusable tools, that’s a separate step.)

Then you stress-test it against something from your world. A post, a campaign, a decision. Something that produced an unexpected result. The gap between what the framework expected and what you actually produced is where the learning lives.

In my case, that artifact was the hotel floor post.

The Shape of Expertise

Not years of study. The output of a single conversation, applied once, on an evening when I wanted to understand something I’d stumbled into.

That’s what this prompt produces. Not expertise. The shape of expertise. From the outside, the difference is invisible.

Pick one domain where you’re currently operating on instinct. Run the prompt before your next piece of work in that space. See what questions you haven’t been asking.

If this issue made you think differently about how you use AI, share it with someone who’d find it useful.

The best ideas spread the same way good content does—because someone recognized something true and passed it on.

The Domain Discovery Prompt

Here’s the prompt:

Read more

Read more

Upgrade to Paid

I turned extended thinking on and watched Claude work.

Mid-response, the reasoning window showed it pulling in a conversation from three weeks earlier—a framework I’d mentioned once, now surfacing unprompted to inform the answer I was getting.

I hadn’t asked for that context.

Claude recognized the relevance anyway and brought it forward.

That’s when it hit me:

If Claude can surface old conversations this way, it can do the same with the nonfiction books I’ve read.

And that realization upended everything I’d built.

You see, I’ve been writing book summaries since 2015. I built a whole identity around it. “The book summary guy.” Two hundred summaries published on my blog. People wrote to thank me for making books accessible, for doing the distillation work they didn’t have time for.

But I was solving the wrong problem.

The summary wasn’t the point. It was a workaround. A bridge between “I read this” and “I can access this when I need it.” I built the summary as infrastructure for a memory problem that just became obsolete.

The craft I built my name on just became obsolete.

The Summary Was a Workaround for a Problem That Just Disappeared

Here’s how we used to read books:

Finish one, export highlights, spend an hour turning them into a summary. Five key takeaways, the core framework, a few quotes. Then file it away.

I built that summary to be the bridge. A reference layer I could search when I needed the idea six months later.

In practice, I almost never searched it. By the time I needed Thinking, Fast and Slow, for example, I’d forgotten I had notes on it.

That retrieval problem just disappeared. Now you can read a book, export your highlights, and paste them into Claude with one instruction:

“Please remember these ideas from [Book Title] and reference them when relevant topics come up.”

The highlights become active context, traveling with you, not filed away. No summary. Just the raw highlights, ready to surface when you need them.

In practice? Six months from now, you’re in a tough conversation with a stakeholder. You don’t remember which book had the framework you need.

But Claude does.

You Can’t Feed Claude Every Book You’ve Read (And You Shouldn’t)

That raises an immediate question: which books?

You only want the ones with value for your future thinking. The ones you actually want Claude to reference when the moment comes up.

I needed a way to filter my reading list, so I decided to test a prompt. I connected Claude to my Notion Reading List, containing every book I’ve finished since 2021.

Then I ran the prompt.

Claude returned nine books, organized into clusters. Switch, The Dip, Positioning. Books I’d referenced in conversations without realizing the pattern.

But the exclusions surprised me more.

Getting Things Done didn’t make the cut, as I already live in a GTD system. Claude called it “water I swim in.”

The prompt also filtered out books I’d rated 5 stars as “high rating, low utility.” That’s when the pattern became clear: The test isn’t “did I get it?” It’s “did I use it?”

Once you get the list, save it to Claude’s memory. (And if you want to go deeper? Feed in your actual highlights from those books.)

Here’s the prompt to filter your own reading list.

Read more

Upgrade to Paid

I recently got a Claude Pro account for work.

Creative Force offers to pay for it for all employees, and the timing made sense as I needed to connect Claude to my work tools. Work Notion. Work Gmail. Work Google Drive.

So I logged into the new work account.

The interface looked identical. Same layout. Same “New Chat” button. But when I started typing, something felt off. I asked a simple question about newsletter structure, the kind of thing my personal Claude would answer in my voice, with my preferences already baked in.

The response came back formal. Structured. Helpful, but generic.

My personal Claude account—the one I’m writing this issue in right now—knows how I write. It knows I run Words Into Works, that I’m working on Dreem, that I prefer direct feedback over diplomatic hedging. It knows my voice, my projects, how I think through problems.

My new work account knew none of this.

I was looking at weeks of setup. Every conversation would have started from scratch. “I prefer conversational tone, not formal.” “Here’s what I’m working on.” “This is how I structure feedback.” Re-teaching lessons I’d already taught.

Unless I could extract that context systematically before I switched.

In this issue, I’m going to share a prompt you can use to extract all your preferences and context from one tool and move it to another (and why that matters more right now than it has in years).

Why Switching Tools Is Getting Harder

If you think my situation is isolated, it’s not.

More people are switching tools AI right now than at any point in the last two years. For example, OpenAI and Anthropic recently dropped competing models within minutes of each other: GPT-5.3-Codex and Claude Opus 4.6.

Developers are already testing both, trying to figure out which handles their codebase better. That means running multiple accounts simultaneously, jumping between tools to chase the latest capabilities.

But it’s not just developers chasing new models.

The landscape itself is fragmenting. You used to pick one AI tool and use it for everything. Now you need multiple. Personal Claude for your side projects. Work Claude for your day job. Maybe ChatGPT for certain tasks. Each one lives in its own silo.

The first time I asked my work Claude to help me refine some copy for a Creative Force email, it gave me generic marketing language. Formal. Corporate. “We’re excited to announce...” The kind of writing I specifically trained my personal Claude not to produce.

I stopped mid-read. This wasn’t going to work. “I write conversationally, not formally. I prefer direct feedback, not diplomatic hedging. Here’s the tone I’m going for.” Ten minutes of setup before I could get back to the actual work. That’s one task.

I estimated it would take weeks to rebuild the context my personal account has. Not days. Weeks. How I structure feedback. How I approach problem-solving. My active projects. My communication style. Hundreds of conversations that taught the AI how to work with me.

The context was still here. It was just trapped in my personal account, and there was no export button.

So I built a prompt to extract it.

Use This Prompt If You’re Switching Tools

Run this prompt in your current AI account before you need to switch. It analyzes your conversation history by looking at patterns in the corrections you’ve made, the praise you’ve given, and how you’ve steered interactions over time.

It extracts what makes an AI tool feel like it knows you: how you communicate, how you write, what you’re working on, and where you need help. Plus technical stack, pet peeves, and expertise gaps.

Then it creates two outputs:

• Custom Instructions Format. A 300-500 word block ready to paste into your new account’s settings. Written in first person with behavioral guardrails (”I prefer direct feedback,” “I don’t use bullet points unless I ask for them”).

• Memory Chat Format. A 200-300 word first message you paste into a new chat. Written in first person with explicit phrasing designed to trigger the model’s long-term memory (”I want you to remember that I write conversationally, not formally”).

Takes ten minutes to run. Saves weeks of retraining.

Here’s the prompt:

Read more

Upgrade to Paid

Last week, I was in Riverside.fm, scrubbing through episode 1 of Dreem Studio Sessions.

I’ve watched My First Million and Marketing Against the Grain. I’ve studied their pacing, noted their visual rhythm, catalogued their hooks. But staring at my timeline, the thought loop started: “This drags. But where? I don’t know what I’m doing. What does good even look like?”

I could see what professionals do—tight cuts at natural breath points, pattern interrupts when energy sags, curiosity-driven titles that promise specific value. But I didn’t understand the decision-making framework behind those choices. Your version might be different.

Maybe you’re designing a deck and can’t tell if the layout reads as “clean” or “amateur.” Writing sales copy and guessing whether the hook will land. Running a meeting and uncertain if you’re wasting time. You’ve studied the examples. You know what good looks like. But when you look at your own work, the judgment evaporates.

Here’s how I closed that gap using AI to simulate the professional judgment I’m still building.

1. Gemini to Get Producer-Level Feedback in 30 Seconds

I’m new to podcasting. No producer’s instinct for what to cut or why. The prompt assigns Gemini a specific professional role—a Senior Podcast Producer who’s edited 200+ episodes. Not generic feedback. Domain-specific pattern recognition.

It provides benchmark standards: audit against podcasts like My First Million and Marketing Against the Grain for pacing, visual rhythm, and strategic positioning. Without those reference points, the feedback stays vague.

And finally, it forces timestamped, actionable output.

The prompt requires Gemini to identify exact moments where professional judgment would intervene, like “2:47-3:12: pacing sags, tighten transition” or “5:30: visual lull, add pattern interrupt.”

I upload the raw edit, run the prompt. Thirty seconds later: specific technical instructions I can feed directly into Opus to generate visual assets and revised cuts. The shift: from “this drags somewhere” to “cut 15 seconds at 2:47, overlay at 5:30.”

Specific. Actionable. Done.

Next I needed a headline worth clicking.

2. Claude to Write Headline That’ll Drive Clicks

Before the Skill, I’d stare at a finished episode and try headlines based on instinct. “Creating AI Models Without Real People”—too dry. “The Future of Virtual Models”—too vague. “How We’re Using AI for Product Photography”—who cares? I’d cycle through ten options, unable to tell which would perform.

After: “Why Your Next Top Model Shouldn’t Be a Real Person” (Clarity: 22, Curiosity Gap: 24, Searchability: 14, Brand it: 14, Shareability: 11).

I built a Claude Skill extracts the episode’s core tension, identifies searchable keywords, selects an emotional angle (curiosity outperforms), and scores candidates across five criteria.

I feed Claude the transcript, and three scored options appear. Now I can choose between headlines I know will work instead of guessing between headlines that might. (I’ve written about how I use Claude Skills here and here.)

Editing the episode is half the work. Getting people to watch it is the other half.

3. Gemini + Claude to Turn Clips Into LinkedIn Posts

Claude can’t watch video. Ask it to write a LinkedIn post about a clip and you get generic observations about what podcast content usually looks like. Useless.

Gemini, on the other hand, can watch video. But I’ve trained Claude to write in my voice. The breakthrough was to make Gemini the eyes, Claude the voice.

Here’s what I do:

I upload a clip to Gemini with the facts the post must contain (voice-to-text collapses this from five minutes to 30 seconds). Gemini translates what it sees into a Master Prompt: “At 0:15, the guest leans forward when explaining the cost-cutting breakthrough. At 0:42, visible reaction shot showing surprise.”

Claude takes that visual breakdown, combines it with my voice patterns from past work, and writes the post. The result references specific moments I never had to describe manually. I make a few tweaks, tag relevant people, and it’s ready. You can see an example here.

Eventually Claude will analyze video natively and this becomes obsolete. Until then, Gemini gives Claude the eyes it needs.

Borrow the Judgement You’re Building

You’re likely not launching a podcast. But you don’t need to wait until you’ve developed professional judgment.

You can borrow it while you’re building it.

The tools transfer: identify the professional judgment you’re missing, then build a prompt that simulates it.

Get all three below:

Read more

I recently wrote about how I turned 10 years of copywriting study into a 34KB Claude skill (here).

That issue was about starting with what you know. This one is about discovering what you’re missing.

I asked Claude one question. The answer surfaced a gap in what I’d already built—something I’d missed for months.

Here’s what happened.

What AI Saw That I Didn’t

The question I asked was:

Based on my current goals, projects, and areas of responsibility what Claude Skills would be most valuable for me to build right now?

Claude surfaced five recommendations. I scrolled through them. Each one tied to a specific area of responsibility.

Newsletter repurposing for Words Into Works. Podcast episode titles for a podcast I’m launching next month. Webinar landing pages for Dreem partnerships.

I stopped at the first one. Content Repurposing—wait, I thought I already had this covered.

It aligned with my newsletter’s positioning. Plus, it would repurpose finished issues into Substack Notes, and get more mileage from content I'd already created.

Still, I needed to make it specific. Not generic repurposing across fifteen platforms. Something focused that would actually work.

That’s when the collaboration started. AI saw the opening. I shaped it into something that fit my actual situation.

Here’s how it works.

Ask, Build, Test, Refine

This process works for any skill you want to build whether that’s content tools, analysis frameworks, decision-making systems.

Here’s the exact workflow I follow:

Read more

Upgrade to Paid

Your calendar shows what you committed to. Your AI conversations show what you were actually wrestling with.

Those are often two different things.

Every week, you solve problems, think through decisions, draft and revise and untangle, and then close the tab and move on. By Friday, you remember the outputs, but the thinking disappears.

Last week, I tried something different. Instead of reviewing my calendar or task list, I asked Claude to review our conversations and tell me what I’d actually been wrestling with.

The answer stopped me.

The Signal You’re Sitting On

A calendar tells you where your time went. A task list tells you what you finished. Neither tells you what you were actually thinking about.

Your AI conversations do.

They capture the questions you asked before you knew the answer. The problems you circled back to three times without realizing it. The tensions you talked through but never resolved. The half-formed idea you tested on Tuesday and forgot by Thursday. The frameworks you tried against a real decision. The book idea you attempted to apply and couldn’t make fit. This is pre-decision thinking: before you had to defend it.

I originally built today’s prompt to solve a simpler problem: I wanted to surface a potential newsletter topic to write about each week. My conversations with Claude are full of half-formed ideas, and I figured something worth developing might be buried in there.

So, last Friday, I pasted the below prompt into Claude and asked it to review the previous seven days. I wanted patterns: recurring themes, unresolved tensions, contradictions, and one “linchpin” insight that would have the greatest ripple effect if addressed.

Here’s what I got:

Most of it I already knew. But one line stopped me:

You haven’t fully internalized that you are the product’s differentiator—not the prompt length.

I read it twice. Then a third time. This wasn’t new information; I’d read about this, the creator as the product, not the output. But I hadn’t seen it operating in my own decisions until Claude named it.

In fact, I’d spent weeks debating whether my paid issues of Words Into Works “earned” their place behind a paywall. Whether the prompt was long enough, useful enough, elaborate enough. Claude traced that anxiety to a faulty metric: I was measuring value by what I delivered, not by how I think.

That’s not something a calendar review would have surfaced. And it gave me something to test: this week, I’m publishing a shorter issue (the issue you’re reading). No elaborate prompt. Just a framework for thinking differently about a tool you already use.

How to Run It Yourself

This took a few iterations to get right.

But the breakthrough came from adding constraints: require direct evidence for every claim, flag contradictions instead of smoothing them over, and force a single “linchpin” insight, like the one pattern that, if addressed, would have the biggest ripple effect.

If you want to try this yourself, one requirement: you need chat history. Turn on memory in your LLM of choice and give it at least a week’s worth of real conversations. The richer the input, the sharper the signal.

Here’s the prompt.

Read more

Upgrade to Paid

Read more

Upgrade to Paid

Read more