Last week I was editing an issue of Words Into Works when Claude, running as line editor, stopped on a paragraph and told me I'd written an anaphora (three sentences, each opening on the same two words).

The truth is I didn't know the word until the editor I'd built named it back to me. And if I'd used one such move without knowing its name, I'd surely used others. That's what nagged at me all week, until I did something about it: I went and found the book that names every last one of them.

The book was Mark Forsyth's The Elements of Eloquence, and its whole argument is that the lines you can't forget are rarely found by feel. Instead, they're built. Thirty-nine figures of rhetoric, each with a name and a repeatable shape, and one of them was mine by accident.

A technique I couldn't name was a technique I couldn't repeat on purpose.

Everyone Used the Window to Build

For a few weeks this summer, Anthropic released a new model called Fable 5, one that could hold an entire book in context at once, then shut the window on July 19 and put it behind pricing most people won't pay.

Everyone I knew spent that window building something new. I spent mine on the opposite move. Rather than point Fable 5 at Forsyth's book to learn from it, I pointed it at a skill I'd already built and asked the book to make the skill better.

Mine happened to be one of my editing skills, but nothing about the move depends on that. Hand a book to a skill you run and it stops teaching you and starts auditing the tool: which techniques it already uses without naming, which it's missing, where each belongs.

Here that meant handing Fable all thirty-nine of Forsyth's figures and the skill that line-edits every issue, then asking for the patch list. That's the move almost everyone skipped. Read a book for its own sake and you come away with a vague sense of better.

Point it at a skill you've already built, by contrast, and you come away with a list of exactly what to fix.

The Fix I Didn't Know I Needed

The anaphora was the least of what the audit found. The sharpest catch was a mistake I'd coded into the skill months ago.

The last pass I run on any draft is a voice check (one of the three passes I edit with instead of generating from scratch): it reads for the spots where the writing stops sounding like me and starts sounding like nobody. It had been marking every rule of three as one of those spots.

Forsyth's chapter on the tricolon says it was too blunt. The problem was never three. It was the balanced three, the kind whose items you could reorder without losing anything. A real tricolon swerves on the last element: it runs longer, or it breaks the pattern the first two set. "Lies, damned lies, and statistics." The third item is where the line earns its keep.

So I amended the skill to keep flagging the flat, shuffleable triad, and stop flagging the weighted one. (It wasn't the only patch; epistrophe was already in my copy editor as "echo." But the tricolon was the one that had been quietly wrong.)

A book can tell you what's missing, but it can't tell you which of the missing figures belong in your voice and which would only be ornament.

That call stays yours.

You Don't Need Fable 5 to Do This

By the time you read this, the window is shut. What it sharpened runs on every issue from here, on whatever cheaper model I point it at. Build a skill, prune the ones that drift, edit with the ones that work, and now, enrich them from the books you've already read.

My skill happens to edit sentences. Yours might do something else entirely. The move doesn't care what the skill does; it needs a skill you run often enough to want it sharper, and a book good enough to sharpen it.

The move never needed thirty-nine figures, either. Take a skill you already run and a book you own, and ask Claude to read the book against the skill and tell you what it's missing. The book becomes a patch list, and what you keep is the upgraded skill.

Forward this issue to whoever you know with a shelf of good books and a stack of good tools who's never introduced the two.

When Sir William Osler taught medicine, he made his students do something odd for 1890. He took them out of the lecture hall and stood them at the patient's bed. Osler, one of the four physicians who founded Johns Hopkins, had noticed that the students who diagnosed best weren't the ones who'd memorized the most. They were the ones who knew how to ask.

"Listen to the patient," Osler would tell his students, "he is telling you the diagnosis."

The answer was usually already in the room. Sitting inside the patient's own account: the aches, the timeline, the thing they almost didn't bother to mention. The doctor's real skill was asking the right questions until what the patient already carried came out.

That's the move almost nobody makes with AI.

We treat it as the thing that knows. So we hand it a blank page and ask it to generate. And then when what comes back is generic, we're disappointed because we briefed it with a fraction of what we actually know.

You know the rest. It just never makes it in.

The Generator Reflex

Point AI at a task, and get an output. That's the default, and it feels productive.

But the ceiling on that output is everything you didn't tell it. Everything you didn't know you needed to. The context that's so obvious to you that it never occurred to you to type it. The constraint you'd recognize in a second if someone named it, but wouldn't think to volunteer.

That's the expensive gap. Not the things you know you don't know. The things you know so well they've gone invisible.

And a vague brief fails you twice. Too little context and the model fills the space with industry-average best practice that doesn't fit your situation. Too much rigid instruction and it follows you off a cliff, executing exactly what you said when a good collaborator would have pushed back. You don't find out which one happened until the work comes back wrong.

The model knew plenty. You just never got what you know out of your own head.

Let It Play Osler

The shift is to stop asking AI to answer, and start letting it ask.

Thariq Shihipar, who builds with these tools every day, wrote the sharpest version of this I've read. He calls it finding your unknowns.

His frame is code, but the idea walks straight into any knowledge work. He borrows the old four-box model: known knowns, known unknowns, unknown unknowns, and the one that matters most here, unknown knowns. The things so obvious you'd never write them down, but would recognize the instant you saw them.

That last box is your accumulated judgment. Years of it. And it's stranded, because nothing ever asks you for it. A blank prompt certainly doesn't.

So flip the roles. The most useful thing AI can do at the start of a task is interview you. Play Osler, and pull the diagnosis out of you before it writes a word.

I Aimed It at the Page I Knew Cold

I was in the middle of writing this issue when I decided to stop describing the move and actually run it. So I pointed a blind-spot pass at my own newsletter's positioning. A page I'd been rewriting for a year (and quietly calling done), the one doc I'd have sworn had no surprises left in it. I asked it to find what I couldn't see, interview me on the sharpest thing, and not let me off the hook until I answered.

It asked me this:

I sat with that longer than I'd like to admit. Because I had an answer. I'd had it the whole time. It just never made it onto the page (I'd never once said it out loud), and the specifics stay between me and the doc. One question, and a doc I knew cold read like someone else had written it.

I didn't need a special tool for that. Two prompts do most of the work. One caveat first: AI can only interrogate what it can see, so paste the doc in, or run these where you've already been working on it. Aim a blind-spot pass at something it can't read and it'll invent generic ones.

First, the interview, for pulling out what you know but haven't said:

Interview me one question at a time about [what I'm working on]. Prioritize the questions where my answer would change the whole direction. Don't move on until I've answered.

Then, the blind-spot pass, for what you can't see because you're standing too close:

Tell me what I don't know I don't know about [what I'm working on].

Then ask me the single question I've been avoiding.

It can't hand you knowledge you've never encountered, and it can't decide for you. But it can surface what's already in you and put the gap on the table. You still have to answer honestly, and that's the whole difficulty.

The question only pays off if you don't dodge the uncomfortable one. It's the same deal Osler's students got. They still had to listen, and the patient only gives you the diagnosis if you let him finish.

So run it on the thing you're surest about. Not the problem you're stuck on. The doc you think you've already nailed. The pitch, the plan, the positioning you could recite in your sleep.

That's where the blind spots live, because that's where you stopped looking.

Before your next prompt, try Osler's move: don't tell it what you know, let it ask, and send this to someone who's still doing all the talking.

Last year, this newsletter nearly ended.

It was a normal Thursday. I was eating breakfast before work, half-reading my inbox, when an email stopped me: serious, precise, clearly not spam or a phishing attempt. A law firm, on behalf of the publisher, had a problem with how I'd been using books in my writing.

Scary is the honest word for it.

The short version is I'd spent almost a decade turning nonfiction books into summaries (and for a few months, prompts for LLMs), and that email made it clear I couldn't keep building on books the way I had been.

It was absolutely gutting. Books had been the raw material here since 2015, and for a few weeks, I thought I'd lost my foundation. But then I realized I'd misread what the foundation was.

It was never the books themselves.

It was the ideas behind them, the systems for putting those ideas to work. Take the books away and I still had the part that actually mattered: everything I'd already accumulated (notes, ideas, decisions, transcripts, summaries), and a way to make AI reach it.

That reframe became Words Into Works 3.0.

I've written more than forty issues since. And after reading some of them back this week, I saw something I couldn't see one Monday at a time: they keep circling the same five convictions. That's the operating system.

Here's the whole thing, and the issues that built each piece of it.

1. Your Past Work Is the Raw Material, if You Can Reach It

Start with the conviction everything else sits on: the knowledge you need already exists, and you've simply lost access to it.

I kept finding the same gap in different disguises. Meeting transcripts pile up and rot while you rebuild context from memory, the second brain you're proud of sits untouched for months (mine held 149 rated book summaries I almost never opened), full of notes Claude has never once seen, and your reading history stays an archive you browse instead of ammunition you load. Even your own chat history is a more honest record of your year than your journal, though you'd never think to open it.

These look like different problems, but they're actually the same one, and it isn't storage. It's retrieval. The material is already there; what's missing is the trigger to reach it the moment it matters.

That's the Accessibility Gap, and closing it is the whole job. Every system I've shared since the pivot is a bridge across it, a way to make AI load what you've already built before you ask, instead of leaving it where it's been sitting, unused.

2. The Blank Page Is the Smallest Thing AI Does

Most people meet AI at the blank page and stop there. Generate a draft, a first pass, something from nothing. That's the party trick, and it's the least valuable thing the tool does.

The leverage is on the work that already exists. Show it your worst draft (I once handed it forty half-finished slides at 11 p.m., three days before a webinar) and it turns into an art director, an editor, and a coach, depending on where you are in the project. Edit with it instead of just generating with it and you close the gap almost everyone leaves wide open, because everyone writes with AI now and almost nobody edits with it. When the output is technically correct but you're still staring at it, redesign what the output hands you next instead of rewriting the prompt. And when you can't afford the expert whose judgment you need, you can build the stand-in and get the feedback anyway.

None of it starts from zero. Every move takes something that already exists (a draft, an output, a decision you've already made) and puts AI to work improving it, pressure-testing it, or reacting to it.

The blank page is where AI is weakest, because it's the one place you've handed it nothing of yours to work with.

3. Taste Is the Last Bottleneck

Hand AI everything you've got, and one thing still stays yours. Once it clears the brief, something uncomfortable shows up: the output is competent and still not good, not wrong so much as generic. What separates "fine" from "yours" is taste, and that's the part you can't hand off.

But you can encode it.

Your taste is the last bottleneck, and the move is to reverse-engineer your own standard from the work you've already judged, then wire it in as a scorecard (mine runs ten criteria) the AI has to clear before a draft ever reaches you. The standard was always in your head, but now it runs on every output automatically.

And if that sounds like it takes more expertise than you've got, you're better at this than you feel. Competence with these tools compounds through reps inside them, not through the content in your feed, so the distance between how good you actually are and how good you think you are is usually wider than you'd guess.

4. Systems Beat Hacks, But Systems Rot

Encoding your taste that way is building a system, and systems are what I reach for first. A hack gives you a win once; a system gives it to you every time, which is why almost everything I build ends up reusable rather than a clever one-off. I compressed ten years of copywriting study into a single 34KB skill. I turned the best work buried in an old chat into a skill that keeps producing. I watched a five-step productivity system collapse into one step once I encoded the method and let AI run the rest.

But every system has the same flaw: it rots.

Every correction you save becomes a permanent rule, and enough of them quietly strangle the output until everything sounds like a committee wrote it (I've shipped that draft more than once). Skills you built months ago keep running against a world that moved on, drifting out of date while still shaping every decision the model makes for you. Building the system is the easy half. But maintaining it, pruning it, and knowing when to delete instead of add? That's the rare discipline.

5. The Real Game Is Loop Speed

Retrieval, editing, taste, systems: none of it pays off unless the loop underneath runs fast. Everyone obsesses over inputs: more tools, more prompts, more tips saved for a someday that never comes. But the thing that actually compounds isn't how much you take in. It's how fast you close the loop between trying something and learning whether it worked.

That's what AI upended. Feedback that used to be slow and generic is now instant and specific to your exact attempt, which bends the whole improvement curve: one percent better every day compounds to 37.78x over a year, one percent worse collapses to 0.03. The old trade-off (fast feedback or useful feedback, pick one) is gone.

So the leverage sits on the far side of the input, in what you do the moment after. The gap that stops books from changing you is the same gap that stops most AI content from changing you: you collect the insight and never run it. And the tips you keep saving don't close any loop until you grade them against the setup you've already built and change something.

Speed of the loop, not size of the input. That's the game.

A year ago, over breakfast, I was sure that email was the end of this newsletter. It turned out to be the start of the version you're reading now.

Losing the books forced me to find what was underneath them, and a year of Mondays turned that into five convictions and one idea holding them together:

AI is for what you've already done.

The raw material changed. The job didn't. That's year two.

If you're enjoying Words Into Works, send it to someone who still opens AI to a blank page.

Last Tuesday I had to build a slide deck for an upcoming Dreem webinar. The thing is, I only had one morning. Well, three hours, actually. I was flying out that afternoon, and Friday was a bank holiday. Plus, the webinar date was bearing down.

So I opened Claude, handed it the outline we’d built together the week before, and described what each slide needed to be. It rendered, I told it no, it adjusted, I told it no again, and three hours later I had twenty slides at 80%, done just as I left for the airport.

Later, I sent the mock-up to Marianne, our designer, who took my rough cut and made it sing: adding real product photography where I’d left grey placeholders, fixing the spacing, the type, the hundred small corrections that separate “fine” from “right.”

The part I kept turning over while I was doing it was none of it felt remarkable. But there’s an obvious way to read a morning like that, and I think it misses what actually changed.

Subscribe

The Wrong Story

A few years ago, that deck would have needed a designer from the first slide to the last.

And the deck was only one seat.

The webinar had others: someone running promotion, someone keeping the team in sync. These days, with Claude and the work the team’s already done, I can get into most of those seats, at least to 80%.

Not because I taught myself design or campaign management, but because every seat runs on the same foundation: the positioning, the ICP, the pain points we keep current, written down where AI can run them.

So here’s what I don’t think is happening:

I don’t think AI is turning knowledge workers into one-person teams, which is the backwards story everyone’s selling right now. The win, from my chair, isn’t doing everything alone. It’s handing a specialist a rough cut instead of a blank page.

Look at what that did for Marianne.

She didn’t spend her morning building a deck from nothing. She spent it making a built one better (the part I would have been hopeless at). The boring 80% was already done, so her time went to the 20% only she can do, and probably enjoys the most.

Anyone can start now.

And it isn’t only slides, either. Anywhere we can take the work to 80%, someone better than us gets back the hours they’d have burned starting from scratch. The more we do it, the more it feels like something we owe them.

That’s the real shift, and it’s the opposite of the AI story everyone’s bracing for: a team that starts further along every time, and spends its best hours at the top of its craft. The ceiling’s gone up. The headcount doesn’t have to.

Subscribe

Your Seats Aren’t Mine

This isn’t a marketing thing. A founder hands the pitch deck to a designer. A product manager briefs a writer for the launch copy. An engineer waits on a mock before building.

Everyone has a seat they keep handing off cold.

Find the one task you hand off most, and build the skill that gets it to a rough cut, the kind of head start a colleague would thank you for. It’s the same work it always was. It just starts further along now.

If this changed how you see your own job, send it to someone still handing off blank pages.

In 1980, an editor named Gordon Lish took a manuscript from a not-yet-famous writer and cut it roughly in half.

The writer was Raymond Carver. The book became What We Talk About When We Talk About Love. The spare, gutting style the literary world fell for, the one we now just call Carver, was substantially Lish's red pen. He cut some stories by nearly ha…

Read more

In 1886, a grieving widow bought an unfinished farmhouse in San Jose and started building. She didn't stop for 38 years.

Sarah Winchester had inherited the Winchester rifle fortune, and more money than she could reasonably spend. She hired architects, then dismissed them and designed the place herself: room by room, no master plan, no finish line.

By the …

Read more

This morning I was half-reading LinkedIn when a post stopped me. Björn Bergström, writing about the brand behind Diesel, with a creative format I wanted to steal for Dreem.

Six months ago that would have cost me a small ritual. Open Notion. Find the right database. Decide whether it's a task or an idea or just a maybe. Title it properly. Paste the link. Write enough context that future me wouldn't stare at it blankly.

Instead I typed one line to Claude, "Save this to my inbox," and pasted the link.

Done. Filed with the source and a note in my own words. I kept scrolling.

That used to be a string of separate decisions. This morning it was one sentence, and I made it without looking up from the feed.

If you run any kind of system to keep track of your work, you've performed that little ritual thousands of times.

Today, I want to show you what happens to it when something else runs the steps.

Subscribe

The Tax You Stop Noticing

For years I ran David Allen's Getting Things Done system in Notion, by hand. (If you don't know GTD, you know a version of it: you catch everything, clarify it, file it, review it, and do it. Five steps.)

It worked. And I trusted it. After all, that's the whole point of a system you've run for years: you stop questioning it.

But trust hid a cost.

I was the engine. Every input had to be caught by me, clarified by me, filed by me, resurfaced by me at the right moment, then done by me. Five steps, and I was every one of them.

Most days that's fine. The tax is small per transaction. (Thirty seconds to file a thing. A few minutes to triage. Twenty minutes on a Sunday to review.)

But it's the volume that gets you. A hundred small rituals a week, each one a tiny act of admin standing between you and the actual work.

And the volume only climbs.

Every team I know is expected to move faster and run more in parallel now, because the tools made it look easy.

In truth, we're probably doing more than we ever have, which means there's more to catch, more to sort, more to resurface.

In my experience, the engine was always me, and that engine was getting buried.

What's Left After the Collapse

I didn't reorganize my system. In fact, I stopped running most of it. What used to be five steps is three now, and I only do the last one by hand.

It starts with catching. I have one inbox, a plain text file, append-only, that spans both my Dreem work and my Creative Force work.

Take the post from this morning, for example. I told Claude to save it to my inbox, which it did, and it then told me it would sit there untouched until I said the word:

What that reply doesn't show is the line it wrote into the file, in the exact format my system uses:

[2026-06-05] Try to make creative like this LinkedIn post | source: linkedin.com/posts/bergstrombjorn... | hunch: Björn Bergström post re OTB (owner of Diesel) — a creative format worth replicating

The date, the source, the note in my own words, all from the one line I tossed it. Whatever I hand it, it keeps, structured and filed in the format my system uses, while I do nothing.

When something's a clear commitment rather than a maybe, I skip the inbox. “Add to tasks,” I write, and Claude writes it into my Tasks database in Notion with full context in the page. Catch, clarify, route, one sentence. Things go to Tasks, to Backlog, or get marked Paused when they're on hold or waiting on someone else.

The second part used to be a weekly ritual. Every Sunday I'd sit down and review the whole system. I don't anymore. Now it happens on demand. For example, last week I asked what to start before a 1:1 with a colleague and Claude floated a webinar landing page I needed to build. It checked my calendar, caught that I had fifteen minutes, not the hour I'd assumed, and talked me out of it.

Which leaves the one step that never moved. The doing. Nothing files the work for me, sits in the meeting for me, or makes the call. The collapse stops exactly where the real work starts.

Which is right where I want it.

Granted, my system isn't seamless, and I won't pretend it is; it's only as good as what I hand it. A clear note routes itself. A lazy one ("look into the pricing thing") still has to be clarified later, by me or by Claude asking what I meant. The collapse needs the context there at the moment of capture; without it, the sorting just moves downstream.

Subscribe

Make It Yours

Today's skill is the routing layer I built to do this. It turns add this to tasks into a correctly-filed, correctly-tagged entry without me touching Notion or my calendar. It's inspired by GTD principles (though it's not affiliated with or endorsed by the David Allen Company), and it's wired to the way I name things, not the book's.

Rather than rebuild my system, look at your own, and find the steps you're still doing by hand. Pick the one you repeat most often (the filing, the sorting, the Sunday review) and hand that one to Claude this week. Then see what collapses.

The ritual I described at the top, open Notion, decide, title, paste, context, I haven't done it in months. If this made you notice your own version of it, send it to someone who's still doing all five steps by hand.

Grab the routing skill below.

Read more

The most useful thing Claude told me last month wasn’t in a video I’d watched.

The video was Simon Pittman’s walkthrough of building a knowledge base in Claude. I’d half-watched it on my phone the night before and flagged it to deal with later. The next morning I did just that: I pulled the transcript (I have a skill for that now) and handed it to Claude…

Read more

Two weeks ago I was handed what felt like an impossible task: wireframe six lifecycle emails for Dreem. Not write them. Wireframe them. Layout. Design. Responsive HTML. Everything.

I’m not a designer. I’ve never written a line of code, much less an email template. But I had two weeks to go live: Marcin needed the templates in one so he could wire up the …

Read more

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 t…

Read more

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. …

Read more

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

Read more

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