We added to it constantly. Every batch of outreach taught us something new to ban. The list grew, the results improved, and after a few months, the list had become far more valuable than the prompts themselves.

Hemingway wrote about this in 1932. He called it the Iceberg Theory:

“If a writer of prose knows enough about what he is writing about he may omit things that he knows. The dignity of movement of an iceberg is due to only one-ninth of it being above water.”

What you don’t say is the seven-eighths underneath. The reader feels the mass even when they can’t see it. That’s how you craft marketing that truly engages your audience.

There’s a reason AI content all sounds the same, and it’s not that the models are bad writers.

AI collapses the starting point.

When a million marketers open Claude and type “write me a LinkedIn post about X,” the model begins from the same place for every single one of them. Same training data. Same defaults. Same pull toward the same frames, the same openers, the same conclusions.

The output is commoditised because everyone is starting in the same position.

Rory Sutherland has a line for this. “The opposite of a good idea can also be a good idea.” His example is low-cost airlines. The legacy carriers were all competing on the same axis, comfort and service, each trying to be a marginally better version of the same thing. Ryanair won by starting from the opposite end of the spectrum entirely. They won on cost.

Using AI means you start at the same place as everyone else. That’s why we’ll see all content start to converge. AI means everyone starts in a red ocean of ideas.

That’s why I built a Claude Code skill for my content system called the “Opposite-Start.”

The purpose of the skill is to help you change the starting point of your marketing or content idea. I’ve found it an incredible asset to not get pulled into the sea of sameness.

Here’s how it works.

Step 1: Map the current conversation.

Before writing a single word, the skill searches across X, Reddit, LinkedIn, and the web for how people are currently talking about the topic. It clusters what it finds into 3-5 core themes, names sources where it has them, and rates each cluster by saturation, dominant take, growing, or niche.

Then it writes one paragraph of synthesis. The centre of gravity. The default position that every AI-generated post on this topic is about to converge toward.

This is the position you’re going to start somewhere else from.

Step 2: Generate 6 inversions.

Not lazy opposites. Six different types of inversion, each one a genuinely different starting position:

Reframe lens — flip the core mechanism. Ask what the dominant story is hiding. If the popular take is “outcome maxing beats token maxing,” a real reframe isn’t “tokenmaxing is fine.” It’s something like: “the outcome maxing narrative is giving CFOs a respectable reason to veto every AI experiment that hasn’t already proven its return — which means the teams getting shamed for burning tokens today are the only ones building a library of working use cases for 2027.”

Tension lens — find the real operator disagreement. Ask where credible people are making different calls right now. Not theoretical pros and cons. Actual split among operators you’d trust. Your reader is living in that indecision.

Hidden cost lens — price the second-order effect. Ask what the CFO will be asking about in 18 months that nobody’s tracking yet. Everyone’s watching the visible invoice. The real cost shows up later, somewhere else, in a number nobody’s building a dashboard for.

Leading indicator lens — flip the time horizon. If the dominant take lives in the present, move 18 months out. If it’s future-tense hype, move to what already happened. The signal is usually hiding in data nobody’s reading.

Category error lens — change the question. Same topic, different tension. The dominant take frames one conflict. You find a different conflict hiding inside it. “Should marketers use more AI” is a weak question. “Should marketing workflows still have humans in the default path” is sharper.

Counter-case lens — change the hero. Most content on a topic has a default protagonist. Flip who you’re defending. The 12-person agency out-shipping the 80-person one. The skeptic who quietly rebuilt the workflow.

Each inversion has to pass one check: could a serious practitioner defend this with real experience? If not, it’s a gimmick.

Step 3: Stick-test each one.

For each inversion, the skill names the tension it creates, who it speaks to and who it doesn’t, and the cost of being wrong, what does the reader have to decide differently if they agree?

Then it runs three questions. Would anyone actively disagree? Would a senior practitioner say “I hadn’t thought of it that way”? Can the writer actually prove it?

Each inversion gets scored strong, mixed, or weak. Then the skill picks one and explains why.

I ran Opposite-Start on a topic I’ve been thinking about for weeks: tokenmaxing for marketing.

The default take it surfaced was the one you’ve seen fifteen times on LinkedIn this month. Stop celebrating token burn. Start measuring outcomes. Outcome maxing beats token maxing.

The skill surfaced four inversions. The one it recommended:

The tokenmaxing vs. outcome maxing debate isn’t a productivity argument. It’s a vendor positioning war. The loudest voices pushing the outcome maxing frame sell workflow software that becomes more valuable if customers measure outcomes their platforms already track. What looks like a principled metrics debate is actually about who gets to own the AI line item in your budget: the foundation model providers, or the application layer.

That’s a genuinely interesting take.

The evidence is all public. AWU announcement dates. CEO LinkedIn posts. Pricing pages. And the enemies list is sharp — every vendor whose software conveniently measures the outcomes they’re telling you to optimise for. A good angle has enemies.

I also ran it for GPT5.5, which was released last week, it came up with the following:

The angle: “GPT-5.5 isn’t a new model — it’s a pricing reset. OpenAI doubled the per-token sticker price and told GTM teams to stop counting tokens.”

It scored highest because it’s timely (ties to yesterday’s specific launch), non-consensus (the whole press cycle is running benchmark tables and pricing-outrage takes — this one goes underneath both to a third explanation), and practically actionable (hits P3 directly: a cost-per-outcome framework your audience can take into their next CFO meeting). The anchor story uses OpenAI’s own press-call language — Brockman’s “more frontier AI available” paired with Pachocki’s “model progress has been surprisingly slow” — as the evidence that the vendor themselves signaled the reframe.

Runner-ups worth banking for future posts:

- Category error — “Spec clarity is the moat; model choice is a rounding error” — evergreen, runs any week

- Leading indicator — “Multi-model GTM stack, who owns routing wins” — better for X Article or podcast

- Hidden cost — “The integration tax” — needs 4-6 weeks for anecdotes to surface

To note, the above is just the high-level output, it creates a full brief for the recommended angle to turn into great content.

Now, the objection I’d have if I were reading this: “Kieran, this only works when there’s an existing public debate to map. What about topics without one?”

Fair. The skill is only as strong as the conversation it can find. For a topic with no real public discourse, Step 1 is thin and the inversions get shaky. The real use case is topics where a consensus is forming and you want to find somewhere else to stand before everyone else arrives at the same position.

Most topics worth writing about are in that bucket.

Install

It’s free. MIT licensed. Python standard library only — no pip install, no dependencies to manage.

git clone https://github.com/searchbrat/singleangle ~/.claude/skills/singleangle

You’ll need Claude Code and, for the best results, OpenAI and xAI API keys (for Reddit and X search, respectively). Without keys, the skill falls back to WebSearch-only mode — usable, but thinner.

Full setup: https://github.com/searchbrat/singleangle

#This skill can 100% be improved. How I do this is I run it and then immediately provide feedback to Claude on what worked and what didn’t work and then ask it to update and reship. This is likely 70%, I have a version I think is 100% but it’s more complex and tied into my overall content system. Will share via a paid post. But this one is REALLY good.

AI makes everyone’s starting point the same. Using AI to start at a totally different point is a great way to find differentiated takes.

What you don’t say is the first half of taste. Where you start is the second.

Until Next Time,

Happy AI’fying,

Kieran

The workers there were, by their own union’s admission, the worst in the American auto industry. Absenteeism ran at 20%. Cars rolled off the line with missing parts. Workers left empty bottles inside door panels to rattle and annoy future owners. GM closed it down.

Two years later, Toyota and GM reopened the exact same factory as a joint venture called NUMMI. Same building. Same equipment. They rehired over 85% of the original workforce, including many of the people specifically cited for the strikes and the sabotage.

Within one year, the plant matched Toyota’s best factory in Japan for quality. Absenteeism fell from 20% to 2%. The same workers GM had written off were producing some of the best cars in North America.

The only thing that changed was what Toyota put around them before they started work.

Toyota sent workers to Japan for three weeks before the factory opened. Not to teach them to work harder, to give them context, rules, tools, and a system for how work actually gets done. GM had given them none of that. Toyota gave them everything.

That’s the right approach to turn Claude Code into a powerhouse of a tool for your work.

To do it you need a starter pack that includes Context, Rules, Reach, Operations

Context — What Claude needs to know

Foundation files are .md files that live in a /foundation/ folder in your project. Claude reads them before any task runs.

These are different from CLAUDE.md, that’s the next section. Foundation files are the business intelligence layer. What Claude knows about your audience, your voice, your market, and how your customers buy.

I covered the four files in depth in my last post. The short version:

Audience Delight Profile. How your audience actually talks. The words they use in Reddit threads and sales calls. When Claude knows your audience says “slop” not “low-quality output,” and “ship it” not “deploy the solution,” everything it writes sounds like a peer instead of a vendor.

Creator Style. Extracted from your best-performing content. Patterns, not adjectives. Not “we’re conversational” — but “short sentences lead, fragments are welcome for punch, always lead with what the reader can do.” Something Claude can check a draft against.

Market Positioning Map. A living file. What territory you own, what’s contested, what you’ve ceded. Updated monthly. Every skill that touches messaging reads it automatically.

Customer Journey Intelligence. Where people actually stall and convert. If your nurture sequence doesn’t know the specific moment people drop off, it writes emails that address the wrong problem.

One thing worth knowing about how this works: skills don’t load all four files every time. Each skill reads the file headers and loads only what’s relevant. A LinkedIn skill loads Audience Delight and Creator Style. A launch messaging skill loads Market Positioning and Customer Journey. Build a new skill next month and it routes itself, no manual wiring.

One thing the foundation post doesn’t cover: these files decay. Your positioning shifts, a competitor reframes their messaging, your audience picks up new vocabulary. A foundation file that’s six months out of date is actively misleading Claude. Build a scheduled skill that audits them monthly, checks your positioning map against competitor homepages, flags audience language that’s gone stale, surfaces anything that no longer reflects reality. The below is just a snippet to illustrate how this works. I’ll do a full post on how agents keep my foundational layer fresh.

# Skill: Audit Foundation Files

## Steps
1. Read all files in /foundation/
2. For each file, check against live external sources:
   - Audience Delight: scan 3 competitor comment sections.
     Flag any new vocabulary not in the file.
   - Positioning Map: check competitor homepages.
     Flag any new claims that overlap with your owned territory.
   - Creator Style: check your 3 most recent published pieces.
     Flag any patterns that have drifted from the file.
3. Produce a short audit report — what's current, what's stale, what needs updating
4. Save report to /foundation/audit-[date].md
5. Agent to extract patterns, learnings and updates
6. Apply those to .md files in /foundation/

Rules — How Claude should behave

Two files do this job.

CLAUDE.md is your operating manual. The rules Claude follows in every session, what you’re working on this quarter, what voice and tone all output defaults to, where the foundation files live, what never to do.

Keep it under 60 lines. HumanLayer’s research on instruction budgets is clear: beyond a certain length, compliance drops. Shorter and specific beats longer and comprehensive.

Here’s a starter template built for a marketing leader.

# CLAUDE.md

## Who I am
[Name], [role] at [Company]. We [what the company does] for [who you serve].
Our stage: [e.g. Series B, 120 people, $18M ARR, expanding into enterprise].

## My marketing function
Team size: [X people]. Channels we own: [e.g. content, paid, lifecycle, PLG].
What we're responsible for: [pipeline, revenue, brand, product adoption — be specific].

## This quarter's priorities
- [Priority 1 — e.g. "Launch into mid-market segment, first campaign live by May"]
- [Priority 2 — e.g. "Reduce CAC by 20% through better top-of-funnel qualification"]
- [Priority 3 — e.g. "Build a content engine that generates 30% of inbound pipeline"]

## Where to find context
All foundation files live in /foundation/. Before any task, scan file headers
and load only files relevant to the work at hand.
- Audience and ICP context: /foundation/audience-delight.md
- Voice and tone: /foundation/creator-style.md
- Competitive landscape: /foundation/market-positioning.md
- How buyers actually move: /foundation/customer-journey.md

## How I work
- I think in outcomes, not outputs. Always tie recommendations to pipeline or revenue impact.
- I want strategic options, not one answer. Give me 2-3 approaches with tradeoffs.
- Flag assumptions. If you're working with incomplete data, say so.
- Be direct. No preamble, no summaries of what you're about to do — just do it.

## My key metrics
- [e.g. MQLs, SQLs, CAC, pipeline coverage, NRR — list the ones that matter to you]

## Current tools and stack
- CRM: [e.g. HubSpot]
- Analytics: [e.g. GA4, Amplitude]  
- Ads: [e.g. Google, LinkedIn]
- Content: [e.g. Webflow, Substack]

## Never
- Recommend tactics without connecting them to a metric
- Give me a single option when tradeoffs exist
- Use corporate filler: "leverage", "synergies", "best-in-class", "move the needle"
- Summarise what you're about to do — just do it

settings.json tells Claude what it cannot touch, your .env file, your secrets folder, any bash command that could cause damage. This is particularly important when you start to sync local file systems across team members so everyone has a single context layer for Claude Code (future post).

{
  "permissions": {
    "deny": [
      "Read(./.env)",
      "Read(./secrets/**)",
      "Bash(curl *)",
      "Bash(rm -rf *)"
    ]
  }
}

And the folder structure. Organise by workflow, by how work actually moves through your projects:

/foundation/          ← context layer
/campaigns/           ← one folder per campaign
/content/
  /linkedin/
  /newsletter/
  /blog/
/research/            
/.claude/
  /skills/

Reach — What Claude can connect to

Without MCPs, Claude only knows what’s in the conversation, what’s in your terminal, what you’ve described, copied, or typed. MCPs give Claude access to live data across your stack, which changes the quality of everything it produces. The advice is grounded in what’s actually happening in your business right now.

For your power-user Claude Code setup, there are 2 MCP buckets to connect.

a. Better marketing decisions, faster by connecting your live marketing stack

Connect your CRM, analytics, and ad platforms, and Claude stops giving you strategic advice based on your description of the situation. It reads the situation itself.

Claude pulls live data, crosses sources in a single conversation, and gives you recommendations grounded in what’s actually happening. Three prompts that show what this looks like in practice:

“Pull all deals that haven’t moved in 21 days and summarise what’s stalling by stage and suggest plays to unblock them.” → HubSpot MCP.

“Which of our active paid campaigns has a CAC above target this month?” → Google Ads MCP.

“Go to [competitor]’s pricing page, capture what’s there, compare it against the snapshot saved in /research/competitor-snapshots/, and flag anything that’s changed.” → Playwright MCP controls a real browser, navigates to the live page, and compares it against a stored snapshot. Pricing tier changes, new feature callouts, and messaging shifts.

Useful MCPs: HubSpot MCP (free for all HubSpot customers), GA4, Google Search Console, Playwright MCP for competitor monitoring, Slack for routing outputs to your team.

b. Hear what your customers are actually saying

Most companies are sitting on a goldmine of unstructured customer data they never have time to properly read. Sales call transcripts. Support tickets. Win/loss interviews. NPS responses. G2 and Trustpilot reviews. Every one of those is a customer telling you exactly what they think, in their own words.

Connect Claude Code to those sources and you can turn unstructured customer data into data that can help power your marketing & growth strategy. Instead of reading a sample of calls and hoping it’s representative, Claude reads all of them. Instead of guessing on what objections are killing deals, you get a ranked list from the last 90 days of transcripts. Instead of positioning built in a stuffy offsite, you get messaging pulled directly from the language your best customers use when they describe why they bought your product or service.

The questions that become answerable: What objections come up most in deals we lose versus deals we win? What do customers say in their first 30 days that predicts whether they’ll expand or churn? What language do our happiest customers use that we’re not using in our own marketing?

Key connectors: Notion or Google Drive MCP for call transcripts and research docs, Gong or Fireflies if your call recording tool has an MCP, web scraping via Playwright MCP for review sites.

Install your default MCPs to ensure Claude Code is ready to be a true strategic partner.

Operation — What makes it a powerhouse

Skills are your team. Each one is a SKILL.md file, a structured instruction set that tells Claude exactly what to do, which foundation files to read, and where to save the output. Where a prompt is a one-off request, a skill is a repeatable hire.

Here’s one a CMO can build to run a weekly pipeline review:

# Skill: Weekly Pipeline Review

## When to use
Run every Monday morning for a clear picture of pipeline health 
and where to focus this week.

## Steps
1. Load foundation context
   Read /foundation/audience-delight.md for ICP criteria.
   Read CLAUDE.md for current quarter priorities and target metrics.

2. Pull pipeline data via HubSpot MCP
   - All open deals by stage
   - Deals with no activity in the last 14 days
   - Deals closing this month vs. target
   - Any stage regressions in the last 7 days

3. Produce the review
   - Pipeline coverage vs. target (are we on track?)
   - Deals at risk — stalled, regressed, or closing soon with low activity
   - Recommended actions by deal — what needs to happen this week
   - One-line summary for leadership: are we on track this quarter?

4. Save to /research/pipeline-review-[date].md
   Post summary to #revenue-team in Slack

How do you decide what skills to build?

Let Claude surface what to automate

The best way to know what to turn into a skill is to look at what you actually do. Two skills make this work together.

The first runs at the end of every session. Before you close Claude Code, run the End of Session Brief, it captures what you worked on, what prompts you ran, and what outputs were produced, then saves a timestamped file to /foundation/briefs/.

# Skill: End of Session Brief

## When to use
Run before closing Claude Code at the end of any working session.

## Steps
1. Review the current session
   - What tasks were completed?
   - What prompts were run more than once?
   - What outputs were produced and where were they saved?
   - What worked well? What needed multiple attempts?

2. Write a structured brief covering:
   - Session date and duration
   - Tasks completed (one line each)
   - Repeated prompts — exact descriptions of anything run more than once
   - Outputs produced and their file locations
   - Any friction points or tasks that felt manual

3. Save to /foundation/briefs/[YYYY-MM-DD-HH-MM].md

Over time, /foundation/briefs/ becomes a real record of how you actually use Claude Code. The second skill reads it.

# Skill: Find Skills

## When to use
Run weekly to surface patterns worth turning into skills.

## Steps
1. Read /foundation/briefs/reviewed.md to get the list of 
   already-reviewed briefs. If the file doesn't exist, create it.

2. Scan /foundation/briefs/ for any briefs not in reviewed.md

3. For each unreviewed brief, look for:
   - Any prompt or task marked as repeated
   - Any task that required multiple attempts
   - Any output that was manually reformatted after saving

4. Identify patterns across briefs — tasks that appear in 
   more than one session are the strongest candidates

5. For each candidate skill:
   - Name the skill
   - Write a first-draft SKILL.md with steps, inputs, and outputs
   - Estimate weekly time saved if automated

6. Present candidates with their draft SKILL.md files
   Append reviewed brief filenames to /foundation/briefs/reviewed.md

Run Find Skills every Friday. In a month you’ll have a system that surfaces its own gaps.

Toyota didn’t send those workers to Japan to motivate them. They sent them to give them everything they needed to do the job well before the job started, the context, the rules, the tools, the system.

When context, rules, reach, and operation are all in place, the ordinary becomes powerful. Everyone who sets up Claude Code rushes into prompts. There is real value in building the scaffolding: a /foundation/ folder with your .md files and a 20-line CLAUDE.md at the project root, default MCPs and your starter skills package.

Until Next Time, Happy AI’fying

Kieran

Having Ogilvy’s principles codified into a grader that scores your copy in real-time does.

Everyone consumes expert content. Newsletters, podcasts, YouTube. With AI, it’s now possible to instantly turn that content into action.

As Naval Ravikant put it:

“Reading is not the same as understanding. Understanding is not the same as being able to use it.”

Here’s the shift: every piece of expert content is now a functional brief for Claude Code. A newsletter, a YouTube transcript, a podcast episode. This system can aggregate an experts lessons, extracts the best lessons, and turns them into a skill.md file you can run on demand.

Charlie Munger called this a latticework of mental models. He spent decades reading voraciously across physics, psychology, biology, economics, but he didn’t just read. He converted every major idea into a reusable mental framework he could apply to investing decisions. Warren Buffett read all the same books. Most investors of their era did too. Munger’s edge was turning that content into a system he built to convert it into something operational.

That’s exactly what this tutorial will do for you. Just faster. Plus I’ve included the system for you to download at the end of this article.

I built it. I pointed it at myself first.

Here’s what happened.

The Claude Code System: 3 Phases

The Skill Creator is a set of Claude Code skills that research any expert’s public content, extract their best lessons, score them, and turn the top lessons into reusable AI skills you can run on demand.

It runs in 3 phases.

Phase 1: Research

Step 1: Run /expert-lessons to research an expert and extract their top lessons

Point it at any expert and it searches across Substack, YouTube, X, podcasts, and web articles. For YouTube, it uses yt-dlp to find YouTube videos the expert appears in, even on channels they co-host where their name isn’t in every title. It downloads transcripts for the top 5 videos by engagement. It runs 4 Perplexity deep research queries to pull content from everywhere else.

Phase 2: Score and rank

From everything it finds, it extracts discrete lessons. Each lesson has to be actionable (something you can implement), discrete (one clear idea), and attributable (tied to a real source).

Then it scores every lesson on 3 criteria:

• Actionability: does it contain a concrete framework or is it just an observation?

• Clarity: is the methodology well-defined with extractable steps?

• Novelty: is this a unique insight or common knowledge?

It surfaces the top 10 in an interactive moodboard you open in your browser, filterable by source type and sortable by any score dimension. You pick the lessons you want to convert and export them as JSON.

Here’s the moodboard it created for my content and content I pulled in for the awesome Elena Verna. The moodboard itself is valuable. It can create you an entire moodboard of tactics aligned to any creator you admire.

Phase 3: Generate the skill

Step 3: Run /create-skills to generate skills from the selected lessons

Now you can pass your JSON to the skill that turns lessons into skills.

Before generating anything, the system analyses whether each lesson should become 1 skill or multiple focused skills. It asks: does this methodology contain distinct sub-frameworks? Are these steps sequential phases of one process, or independent strategies?

Then it scores each resulting skill on Repeatability, Ease of Implementation, Impact Potential, and Universality.

The highest scorer gets recommended. You confirm what you want built. It generates a markdown skill file, ready to run in Claude Code.

Here’s the system recommending what skill I should create from one of Elena’s top scored lessons.

I Ran It on Myself

The system extracted 7 discrete lessons from my public content. It scored them all.

I decided to follow the system and choose the first one:

Before generating anything, the system runs a decomposition step. It looks at each lesson and asks: does this contain distinct sub-frameworks, or is it one coherent process? Should this become one skill or several?

For the Quality Grader, it identified one clean skill and scored it:

Repeatability           9/10
Ease of Implementation  8/10
Impact Potential        9/10
Universality            9/10

Universality is what pushed it to the top. Most skills extracted from a marketing expert stay in marketing. This one works for any craft and any domain master. You pick the standard. The skill adapts.

So it generated the skill file and here is what it does:

1. Context gathering — asks what industry you’re in, what type of work you’re grading, and what outcome it needs to achieve

2. Pick a domain master — you choose an expert whose principles become the rubric (David Ogilvy for copywriting, Nancy Duarte for presentations, anyone)

3. Establish principles — researches the master and generates 10-15 principles, each with a name, definition, good and bad examples, and equal weighting toward 100 points

4. Submit work — paste text, point to a file, any format

5. Score and feedback — scores each principle (Strong / Adequate / Weak), gives an overall score out of 100, highlights the top 3 improvement areas by impact

6. Rewrite — generates a full rewrite targeting 100/100, with a change summary explaining what was fixed and why

I chose David Ogilvy. I submitted HubSpot’s homepage.

It scored 67/100.

Here’s the output:

Headline Does the Selling       5/7   ~ Adequate
Specificity Over Generality     5/7   ~ Adequate
Reader-First Language           3/7   ✗ Weak
One Big Idea                    4/7   ~ Adequate
Promise a Benefit               4/7   ~ Adequate
Simple Language                 6/6   ✓ Strong
Factual Persuasion              6/7   ✓ Strong
Long Copy Sells                 3/6   ✗ Weak
Clear Call to Action            6/7   ✓ Strong
Don't Be Boring                 4/7   ~ Adequate
Competitive Differentiation     2/6   ✗ Weak
Emotional Connection            3/7   ✗ Weak
Credibility & Authority         6/7   ✓ Strong
Visual-Copy Integration         5/6   ✓ Strong
Consistent Brand Voice          5/6   ✓ Strong

The top 3 weaknesses:

Reader-First Language (3/7). HubSpot’s hero talks about HubSpot, not the reader’s problem. “HubSpot’s Smart CRM” should be “Stop toggling between 12 tabs to find one customer’s history.”

Competitive Differentiation (2/6). Zero reasons to choose HubSpot over Salesforce, Pipedrive, or Marketo. Ogilvy built entire campaigns around competitive claims.

Emotional Connection (3/7). The case studies (pipeline from millions to billions, 300%+ fan growth) carry genuine emotion. They sit buried under 6 product feature descriptions.

The system then offered to generate a full rewrite targeting 100/100. Pretty incredible given the skill was automatically built from a single lesson of mine.

What This Actually Means

Any expert's public methodology can become a reusable AI workflow. The knowledge doesn't have to sit inert in a newsletter you'll never reread. It can become something you run. Every piece of content is now a functional brief.

Three design decisions worth knowing before you build this:

Lessons and skills are scored differently, and that's deliberate. Lessons are scored on educational quality: is this actionable, clear, novel? Skills are scored on operational potential: is this repeatable, easy to implement, high impact, universal? A great lesson doesn't always make a great skill. The two-stage scoring prevents you from converting lessons that sound impressive but don't run well as workflows.

Every generated skill is domain-neutral by design. Skills strip the expert's industry context. They start with a Step 0 that asks about your context: your industry, your audience, your goals. The expert's structural thinking is preserved. Their industry-specific assumptions are stripped. The same skill extracted from a marketing expert works for a fitness coach, a developer advocate, or a sales trainer.

Apply to your internal workflows. If I was going to give one piece of advice to make this more impactful for you, plug it into your internal systems e.g. email, slack, asana etc. Have it recommend skills based on repeated patterns so it can start to automate your work.

Now, the objection I’d have if I were reading this: “This sounds impressive but it’s for people who can build in Claude Code. I’m a marketer, not a developer.”

The skill files are markdown. Plain text with instructions. If you’ve written a Google Doc, you can read a skill file. The Skill Creator generates them for you, you don’t write them from scratch.

Munger spent decades converting other people’s best thinking into reusable mental frameworks. This does the same thing. Just faster.

The kit includes both skills, the moodboard template, and a full setup guide. Free, no email required.

[Download it here →]

Until Next Time,

Happy AI’fying

Kieran

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But this week’s launches feel historic, like, we’ll look back and remember this week as being a time when everything changed.

Let’s take a look at what launched over the past 24 hours and explain what’s happening, and what it …

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I’ve slept on Manus. I covered it on Marketing Against the Grain when it originally launched (fun fact, I was one of the first to cover Lovable, back then it was called GPT…

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But then the models got better, and prompting wasn’t such a hot topic for the average user.

The reality about prompting in 2026 is this - give the model better context, not better instructions.

T…

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Intelligence has always been a defining characteristic separating the elite worker from the average worker. Marketing is no different.

Smart marketers who knew the formula for success would win. They figured out the best email sequence to send, the paid advertising campaign…

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Below is a video ad I made to promote this Substack. It’s 1:44 minutes long. That’s too long for a video ad, but I wanted to stretch myself to 2 minutes to force more learning.

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With all those launches, it’s easy to miss what *actually* changed for how we market and grow our business.

In the following post, we’ll break down the biggest AI launches in 2025 - the category, the AI products launched in that category, why it was so imp…

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Let’s wrap up the year with how marketing can leverage from using AI next year.

Leverage is anything. How can you win? At the start of 2025, the advice was likely simple. Just use AI for some of your workflows. Very few marketers were using AI in a meaningful way.

Now, that’s changed. I’ve spen…

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These lesson is a small extract from a paid course I’ll be releasing early next year (Feb). It showcases how you can create a world-class content team via AI. I believe this will be one of the most v…

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As a subscriber of this Substack, you already know that :)

But, of all the complex challenges to solve, how we show the success of marketing might be the hardest.

There’s a reason the average tenure of a CMO is 18 months. Marketing is already hard to measure, which makes its assessment subjective.

However, it’s going to get harder.

Ove…

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