Big Idea

The argument in one line.

Local businesses are invisible to AI not for lack of reviews but for lack of cross-platform consensus, and the threshold for building that consensus is 250 strategically diverse documents, grounded in published AI training research.

Who This Is For

Read if. Skip if.

READ IF YOU ARE…

You run or own a local service business (plumber, contractor, lawyer, home services) and have wondered why AI tools never mention you when asked for recommendations.
You are an SEO professional looking to expand into AI visibility and want a concrete, research-backed framework to pitch clients.
You already have a website and reviews but have never audited what ChatGPT or Claude actually surfaces when someone asks about your business.
You want a scalable content production process that uses AI assistance with mandatory human review.

SKIP IF…

You are a national or global brand, as the framework is designed for local and service-based businesses.
You are looking for technical SEO such as schema or Core Web Vitals, as this is entirely about content strategy and platform diversity.

TL;DR

The full version, fast.

AI tools search the web in real time when asked about local businesses and build answers from whatever content exists, including years-old complaints, with no sense of recency. An Anthropic study found that 250 documents are enough to establish a detectable pattern in AI training data, even in models up to 13 billion parameters. The video presents two prompts: a brand audit that reveals what AI currently finds about you, and a content planning protocol that maps 250 pieces of diverse, platform-specific content across four buckets to build the cross-platform consensus that makes AI treat your brand as a trusted, memorized fact.

Free for members

Chat with this breakdown — free.

Sign in and you get 23 free chat messages on us — ask for the hook, quote a framework, find the exact transcript moment, generate a markdown action plan. Bring your own key when you want unlimited.

Create a free account →

Chapters

Where the time goes.

00:00 – 00:35

01 · The AI local business test

Pattern-interrupt hook: run the ChatGPT test right now. If you did not show up, the problem is not what you think.

00:35 – 01:04

02 · How AI actually finds businesses

Debunks the internal-database myth. AI searches the web in real time from Reddit, forums, Medium, and review sites with zero sense of content age.

01:04 – 01:57

03 · Why local businesses stay invisible to AI memory

Contrasts how AI handles well-known figures from memory versus local businesses via forced web search. Sets up the goal: get baked into AI training data.

01:57 – 02:21

04 · The Anthropic study

0.00016 percent of training data equals 250 documents and changed model behavior from 600M to 13B parameters. The defenses do not scale.

02:21 – 02:47

05 · Two practical tools preview

Introduces the two deliverables: the brand audit prompt and the 250 authority protocol.

02:47 – 04:47

06 · How low the threshold really is

Honest caveat: the study tested trigger injection not brand influence directly. But the principle holds: models learn from pattern and the threshold is low.

04:47 – 05:49

07 · Auditing what AI thinks about your brand now

AI pulls from Reddit and forums that feel authentic but are often years old. Most business owners are not checking this channel at all.

05:49 – 06:31

08 · The Model Training Data Risk Auditor prompt

Prompt forces AI to surface real conversations about your brand across all major platforms then patterns them into vulnerabilities and action items.

06:31 – 07:15

09 · Live audit demo for AJs Plumbing in Gary Indiana

Finds a target business with a weak review profile, runs the audit prompt inside Claude, and walks through the output live.

07:15 – 08:28

10 · Reading the audit results

Raw sentiment, reputation patterns, competitive landscape, platform-by-platform summary. Real case: a 4-year-old Reddit complaint still shaping AI answers today.

08:28 – 09:53

11 · From defense to building permanent AI authority

Finding the problems is defense. The 250 protocol is offense. AI looks for consensus across formats and platforms not just your website.

09:53 – 13:02

12 · Four content types that create AI consensus

Own content (foundation), professional platforms (triangulation), community and Reddit (authenticity), third-party validation (closes the loop). Diversity beats volume.

13:02 – 13:50

13 · Planning your content footprint with the 250 prompt

Second prompt generates the topical map: angles, platforms, headlines, tone, and length for every one of the 250 pieces.

13:50 – 16:46

14 · Scaling content production with AI and human review

Multi-step process: topical map, outline (Reddit and PAA-informed), full draft (structured prompt), human editor. 30 to 40 pieces per session. Human review is non-negotiable.

16:46 – 17:51

15 · The new opportunity for local SEO agencies

Every local client is being misrepresented in AI right now. Agencies that solve AI presence will not just be selling SEO, they will be selling the future.

Atomic Insights

Lines worth screenshotting.

AI does not have a private database of your business. It searches the web in real time and builds answers from whatever content it finds, regardless of accuracy or age.
A complaint from 2019 carries the same weight in an AI recommendation as a five-star review from yesterday because AI has no concept of content recency.
An Anthropic study showed 250 documents can change model behavior in both 600-million and 13-billion parameter models. The defenses do not scale with model size.
Getting into AI memory requires cross-platform consensus, not just a well-optimized website. AI looks for the same claim appearing across multiple trusted formats and sources.
250 identical blog posts on one domain count as one source to an AI, not 250. Diversity of platform and format is what creates the pattern.
Community content such as Reddit, Quora, and forum threads is the bucket AI leans on hardest for local service queries because it reads as authentic peer opinion.
Third-party validation such as press, directories, and chamber listings closes the consensus loop precisely because you did not write it yourself.
Producing 250 pieces of content no longer takes six months. With AI-assisted production and human review, an agency can batch 30 to 40 pieces in a few hours.
The biggest content mistake is asking AI to write a blog post without first generating an outline grounded in Reddit questions, Google PAA, and hyper-local detail.
Right now there is a window where consistent content can establish brand patterns in AI training data that will be much harder to break into once competition catches up.
SEO professionals can reframe their offer from ranking pages to controlling what AI says about a client when a customer asks for a recommendation.
Human review of every AI-generated piece is non-negotiable: hallucinated numbers or regulations in high-ticket or legal and medical content can destroy credibility instantly.

Takeaway

AI builds your reputation from whatever content it finds.

WHAT TO LEARN

When an AI is asked to recommend a local business, it searches the web in real time and trusts whatever content shows up across the most platforms, which means your oldest, worst content often speaks loudest.

AI tools have no concept of content age. A four-year-old complaint on Reddit carries the same weight as a five-star review from yesterday, so legacy negative content is an active liability.
AI treats cross-platform consensus as a trust signal: the same expertise appearing on your website, LinkedIn, an industry blog, and a Reddit thread reads as fact; appearing only on your website reads as noise.
Community-generated content such as Reddit and Quora posts is weighted especially heavily for local service queries because it signals authentic peer opinion. Leaving this channel empty means the most-trusted AI source says nothing about you.
The threshold for influencing AI training data is lower than intuition suggests. Research indicates patterns can be established with a few hundred high-quality documents, not millions of pages.
Generically prompted AI writing fails both readers and AI models because it lacks specificity. An outline informed by real Reddit questions, Google PAA results, and hyper-local detail produces content both audiences trust.
Human review of every AI-generated piece is not optional: fabricated numbers or invented regulations in legal, medical, or high-ticket service content destroy the credibility the content was built to create.
Auditing what AI currently finds about a brand before building new content reveals which platforms have legacy problems to address, not just gaps to fill.

Glossary

Terms worth knowing.

LLM (Large Language Model): The class of AI models such as ChatGPT, Claude, Gemini, and Grok that generate text by predicting likely continuations based on patterns learned from training data.
Training data: The large corpus of text and documents an AI model learns from before deployment. Content published on the web can be included in future training runs.
Real-time web search: When an AI model queries live web results at the moment of a user prompt rather than relying solely on its pre-trained knowledge.
Consensus (AI context): The pattern an AI sees when the same claim or entity appears across multiple independent sources, formats, and platforms, treated as higher-confidence fact than a single-source claim.
GBP (Google Business Profile): The structured business listing on Google Maps and local search results, distinct from AI recommendation systems which pull from broader web content.
Deduplication: A quality filter applied during AI training that collapses near-identical content, reducing the influence of repeated content from the same domain.
People Also Ask (PAA): A Google search feature showing related questions users ask on a topic, used here as a research signal for what content to create.

Resources

Things they pointed at.

03:27linkAnthropic and UK AI Security Institute and Alan Turing Institute study on training data influence

05:49linkAI SEO Mastery Skool community prompt catalog ↗

17:24linkClaude workflow follow-up video

Quotables

Lines you could clip.

00:48

“The question is not whether your business is in the AI. The question is what does the AI find when it goes looking for you?”

Clean reframe that lands the entire video premise in one sentence→ TikTok hook↗ Tweet quote

00:25

“AI has no concept of how old that content is. A complaint from 2019 carries the same weight as a five star review from last week.”

Counterintuitive, specific, emotionally activating for any business owner→ IG reel cold open↗ Tweet quote

14:57

“Google ranks individual pages. AI models learn patterns across the entire Internet.”

Tight contrast sentence, no setup needed→ newsletter pull-quote↗ Tweet quote

17:40

“You are not just going to be selling SEO anymore. You are going to be selling AI presence.”

Agency repositioning hook; strong closing line→ TikTok hook↗ Tweet quote

The Script

Word for word.

Read-along

Don't just watch it. Burn it in.

See every word as it's spoken — crank it to 2× and still catch all of it. The same dual-channel trick behind Amazon's Kindle + Audible.

analogystory

00:00Alright. Go to chat GPT right now and ask it to recommend a business in your niche in your city. Go ahead.

00:05I'll wait. Well, not really. Just pause the video.

00:08But if your business didn't show up, you have a problem, and it's not the problem you think it is. Most people assume these AI models have some kind of internal database with your business stored in it. I'm telling you, they don't.

00:20When you ask ChatGPT or Gemini or Groke or Claude or any of the tools for a local recommendation, it's going to search the web in real time and build an answer from whatever it finds. Reddit threads, forum posts, Medium articles, review sites, your own website.

00:33And here's what most people don't realize. The AI has no concept of how old that content is. A complaint from 2019 carries the same weight as a five star review from last week.

00:44So the question isn't whether your business is in the AI. The question is what does the AI find when it goes looking for you?

00:52And for most local businesses, the answer is almost nothing, which means the AI is either ignoring you completely or building its answer from content you didn't create and you don't control.

01:04Now think about why the AI has to search at all. If you go to chat GPT and ask it who Elon Musk is, there's no web search. There's no pulling from random sources.

01:14It knows. That information is baked into the model's training data so that AI doesn't need to go find it. It generates the answer from memory.

01:23Now that difference is everything. And right now, pretty much every local business is in the first category. The AI doesn't know you exist, so it searches the web and builds an answer from whatever it happens to find.

01:37But what if your business had enough high quality content across the Internet that the AI actually absorbed during its training? It wouldn't need to search anymore. It would just know you.

01:48It would know your business. You'd go from being something that AI has to look up and guess to being something that AI recommends from memory.

01:57I'm Caleb Alcu. I started my SEO agency back in 2016 and built it to 7 figures in three years, and I wanna talk about a study from Anthropic.

02:05That's the team that builds Claude. They just proved that the number of documents it takes to get into an AI training data and change its behavior, that's the important part, is way lower than anyone expected.

02:18It's 250 documents. That's it.

02:21So in this video, I'm going to show you two main things. First, a prompt that audits what the AI models currently find when they go searching for your brand. And second, a framework that I'm calling the 250 authority protocol that's designed to build the kind of content footprint that doesn't just influence what the AI finds.

02:41It gets your brand into the AI's memory permanently. But first, you need to understand exactly how small the gap is between being invisible to these models and being baked into them.

02:53Most people assume that because these models are basically trained on the entire Internet, no single business could ever meaningfully influence them, that you'd need millions of pages to even register. Anthropic, the team behind Claude, partnered with the UK AI Security Institute, the Alan Turing Institute to find out exactly how much data it actually takes to change a model's behavior.

03:16And the answer should make every single business owner pay attention. It's 0.00016 of the total training data.

03:25If you have a library with a million books, it's changing a single paragraph in two of them. In real numbers, it's 250 documents. The researchers inserted 250 specific files into the training data of models ranging from 600,000,000 parameters to 13,000,000,000.

03:43And here's what surprised them. They expected the bigger models to be harder to influence. They thought that 250 documents would get diluted in all that extra data.

03:52They didn't. 250 documents changed the behavior of the small model, and the exact same 250 documents changed the behavior of the massive model. The defenses don't scale.

04:03Now I wanna be honest with you. This study tested a specific trigger mechanism. It wasn't testing brand influence directly.

04:09And there are real barriers between publishing content online and having it actually make it into a model's next training run. Quality filters, deduplication, and all of that, but the principle is clear. These models learn from patterns and data, and the threshold for establishing a pattern is way lower than anyone assumed.

04:26If 250 documents can teach a model a behavior it was never supposed to learn, the question becomes, what could 250 pieces of content, high authority content about your brand teach the models?

04:39We're going to come back to that number. But right now, I wanna show you something more immediate because even before you start building toward the training data, you need to see what these AI models are currently finding when someone asks about your business. So right now, when someone asks an AI about your business, it goes searching the web, and it build this answer from whatever it finds.

05:00And I can tell you from running this for my own agency and my clients, what it finds is almost never what you want it to say. LLMs, these AI models, they pull heavily from Reddit, forums, Medium, and review sites because that content feels authentic to them, but they have no concept of time.

05:17A complaint from a disgruntled employee in 2019 carries the same weight as a genuine five star review from yesterday. A random forum post where someone misspelled your business name and said you were overpriced, that's shaping what the AI tells your future customers.

05:33Most business owners have no idea this is happening. They're checking their Google rankings. They're monitoring their reviews, but no one is checking what ChatGPT or Claude says when a potential customer asks about them.

05:46So I built a prompt for this. I call it the model training data risk auditor. I use this for my own agency and for my clients, and it does something simple but powerful.

05:56It forces the AI to go find the actual conversations happening about your brand across Reddit, forums, Medium, and review sites, then it surfaces the pattern. Let me show you that prompt.

06:08It's available in my school community. I'm gonna show it on screen. You can pause and grab it or join the community.

06:12It's in the link below. So I'm gonna come into the classroom and then the prompt catalog. That's where it lives, and it is right here under chat GPT LLM optimization, model training, data risk auto hunter.

06:24So we'll grab it. And, you can slow down. You can, uh, pause to grab the prompt yourself, and we'll come on over, and I'm gonna look for a business to run this for.

06:34So I love searching for Gary, Indiana. I don't live in Gary, Indiana, but it's a great example for just like a normal mid sized Midwestern city. It's an example I use all the time.

06:45Kinda hoping that the Chicago Bears get renamed the Gary Bears because that's fabulous. Anyway, plumber Gary Indiana. Let's see what we can find.

06:53Uh, let's use AJ's plumbing and sewer. Uh, not a great review profile. Uh, AJ has some work to do, but let's go ahead and use that.

07:02We'll come on over to Claude, and here's the prompt. I filled in I I left competitor blank.

07:09That was optional. I filled in AJ's plumbing and sewer and Gary Indiana, and here is what Claude generated.

07:17Uh, the raw sentiment snapshot, uh, moderately positive but thin, uh, invisibility, reputation patterns, it gives this, positive patterns, competitive differentiators, and the AI summary test.

07:29Here's the summary of what AI would generate. Limited online review volume, narrative vulnerabilities, vulnerability two, three, and then here are some action items, the top three priorities.

07:40And here's the platform by platform summary for all the different platforms that this AI prompt went out and checked. And we have the key competitor landscape, who is AJ's competing with, and what do theirs look like.

07:52Now I ran this for a client last month, and I found a four year old Reddit thread where someone complained about response time. Now since then, the business has completely overhauled their operations. Didn't matter.

08:03The AI didn't know. AI didn't care. The AI was still surfacing that thread as if the complaint happened yesterday.

08:09So grab the prompt, run it for your own business and for your clients. What it's going to give you is the raw picture of what AI models are currently working with and more importantly, specific action items so that you can fix what's broken.

08:23But finding the problems, that's just defense. Once you know the gaps, you need to fill them. And this is where the second prompt comes in, the 250 authority protocol.

08:31Because we're not just trying to fix what the AI finds. We're trying to build enough of a content footprint that your brand becomes a permanent part of the AI's memory. And here's what most people get wrong about AI and content.

08:46They think about it the same way that they think about Google. Write a blog post, optimize it, hope it ranks. But AI models don't work like Google.

08:53Google ranks individual pages. Google ranks your GBP. AI models learn patterns across the entire Internet.

09:00When an AI is deciding what to believe about a topic, it's not looking at one page. It's looking for consensus. Does the same information show up across multiple trusted sources in multiple formats from multiple perspectives?

09:14If it does, the AI treats it as a high confidence fact. If it only shows up in one place, it gets ignored or diluted by whatever else is out there. This is why one great blog post or 10 great blog posts on your website don't move the needle with AI, and it's also why 250 identical blog posts won't work either.

09:34AI systems are built to detect and discount echo chambers. So if all 250 documents say the same thing in the same way on the same domain, the AI sees that as one source. What you need is diversity.

09:48The same core expertise expressed across different formats, different platforms, different angles. That's what creates the pattern for AI to learn from.

09:57That's what builds consensus. So here's how the 250 authority protocol works in practice. And I wanna be clear, the number 250 comes directly from that anthropic study.

10:06It's the threshold that they showed was able to establish a pattern in models up to 13,000,000,000 parameters. We're going to use that as our benchmark.

10:16So your 250 documents need to be spread across at least four types of content in four different environments. The first bucket is your own content. These are case studies, service deep dives, data driven articles on your own website.

10:29This is your foundation. This is the content that establishes what you actually do with real numbers and real results. If you're a plumber in Gary, I don't want a generic page about water heater repair.

10:43I want the case study where you replace 14 water heaters in a specific neighborhood last summer and cut the average install time by two hours. Specifics are what separates content the AI trusts from content it ignores. The second bucket is professional platforms.

11:01This is LinkedIn articles, industry publications, guest posts on niche sites. This is your external authority. When AI sees your expertise in your own site and on LinkedIn and on an industry blog, it starts to triangulate.

11:15It sees the same person saying the same thing in different places, and that builds trust. The third bucket is community content. Reddit comments, Quora answers, forum posts, niche discussion boards.

11:27This is the content that AI models lean on the hardest for local and service based queries because it feels like real people talking. Sometimes I feel like ChatGPT spends all of its time on Reddit.

11:39And right now, this is the bucket where most businesses have zero presence. I just said ChatGPT spends all of its time on Reddit, but your competitors, they might have a strong website, but they're not showing up on Reddit.

11:51If you are showing up on Reddit, giving threads that are helpful, specific advice about plumbing in the Gary, Indiana area, that's the content that AI is going to wait when someone asks for a recommendation. Alright. In the fourth bucket, this is third party validation.

12:06Press mentions, chamber of commerce listings, local sponsorships, industry directories. This is the content that you don't write yourself, and that's exactly why the AI values it. When other sources confirm what your own content says, that closes the loop.

12:19The AI sees the pattern from every angle. Now I'm not going to sit here and tell you that if you publish 250 pieces of content, you're guaranteed to end up in the next version of ChatGPT's training data.

12:31There are, of course, quality filters, deduplication systems, and a billion other documents competing for space. But here's what I do know.

12:39That anthropic study showed the threshold is low. And whether the AI is searching the web in real time or pulling from its own trading data, the mechanism is the same. It's looking for pattern.

12:50It's looking for consensus. 250 pieces of diverse, high quality content creates exactly what the AI systems are looking for.

12:58So let me show you a second prompt. This prompt is going to help you plan this out. It takes your core expertise and generates specific angles, topics, and platforms for your 250 pieces of content.

13:09I'm gonna show it on the screen now. You can grab it from my school community. There's a link in the description.

13:13Here So it is, 250 authority content protocol. Uh, it's we're first starting by telling you how to actually use it. Uh, then the prompt itself, I'll go ahead and copy this.

13:28And as before, we'll run this for AJ's plumbing in in Gary.

13:35And here's the content matrix that it came up with. So you can see it's it's recommending, uh, the right angle, the platform, the headline, the sentence, the tone, the length. And it keeps going down recommending all of these different articles.

13:48The key here is not to write all 250 at once. Don't do all of this tomorrow.

13:54You're gonna run the prompt. You're gonna get your first batch of angles. You produce that content, uh, then shift your focus slightly.

14:00If you started with plumber Gary, then maybe you shift over to emergency pipe repair or water heater installation, main drain line replacement, and expand from there. Each batch builds on the last one and the content footprint grows. But here's the thing nobody's talking about.

14:14Producing 250 pieces of content sounds like a six month project. It doesn't have to be. Three years ago, producing 250 pieces of quality content would have cost you somewhere between 5 and $10,000 in writer fees, uh, or it would have cost you six months of your life if you did it all yourself.

14:30That, of course, was before AI assisted writing. And my agency would primarily use Claude for content generation. And in head to head tests against ChatGPT, Claude consistently produces content that scores higher on helpfulness, reads more naturally, and passes AI detection at a higher rate.

14:46But the AI isn't doing this alone. We run a multistep process to generate the highest content quality possible. So the first step, we're going to use the 250 authority protocol prompt to generate the topical map.

14:58That gives us every angle, every platform, every format that we need to cover. The planning that used to take a week, we can now do with a couple of prompt runs. The second step, we're going to use Claude to give us an outline for the content, and this is where most people mess up.

15:12They're going to go into ChatGPT or Claude and just say, hey. Write a blog post about water heater repair and get exactly what you would expect, a generic content that sounds exactly like every other AI generated page on the Internet. That's useless.

15:24The AI models are going to catch it. They're not going to trust it. And more importantly, readers won't believe it.

15:29They'll catch it. So instead, we start by generating the outline first, uh, based on information available online, what people are asking about from your area, real world people, what they're saying on Reddit.

15:41The Google people also ask questions and hyper local details about where your business is located. Then we actually use a long writing prompt to specify the the the exact structure, the audience, uh, how we want it formatted.

15:56This is the kind of specificity that both Google and the AI models reward. And the last step here, do not skip this, a human editor reviews every single piece before it goes live. AI makes errors.

16:08It hallucinates the details, especially for high ticket services like a water heater replacement, legal or medical, a wrong number or made up regulation can destroy your credibility. A few minutes of human review per piece is the difference between content that builds trust and content that undermines it.

16:25With this process, my agency can produce a full batch of 30 to 40 pieces in a few hours. That means the entire 250 piece protocol be can be executed in a month or two without burning out a single person on your team. Now I wanna zoom out for a second because this isn't just a content strategy.

16:42This is a positioning shift for your agency. Every local client you have right now is going to be represented in AI by whatever random content happens to exist about them online. Most of them have never even thought about what ChatGPT says when someone asks for a recommendation for the best plumber in Gary.

17:01That's a gap you can fill. You're not just going to be selling SEO anymore. You're going to be selling AI presence.

17:07This is the future. You're the person who makes sure that when the next version of these models gets trained or when the current version searches the web, your client is the answer.

17:18Google spent twenty years building systems to evaluate content quality. AI companies are three years into this process. Right now, there's a window where consistent high quality content can establish your brand in places that will be much harder to break into later.

17:36This window won't stay open forever. And if you want to see the exact Claude workflow my agency uses to produce this content at scale, including the specific prompts, the process, the quality checks, I break all of that down in this next video

The Hook

The bait, then the rug-pull.

Ask any AI tool to recommend a local business in your niche and your city, and odds are your name never comes up. Not because you lack reviews or a website, but because AI does not have a private database of your company. It searches the web in real time and builds an answer from whatever fragments of content happen to exist, with no idea how old any of it is.

Frameworks

Named ideas worth stealing.

08:28model

The 250 Authority Protocol

Own content: case studies, service deep dives, data-driven site pages
Professional platforms: LinkedIn, industry publications, guest posts
Community content: Reddit, Quora, forum threads
Third-party validation: press, directories, chamber listings, sponsorships

A four-bucket content strategy targeting the 250-document threshold from Anthropic research. Diverse platforms and formats create AI consensus.

Steal forAny local or service-based business trying to control what AI says about them

05:49concept

Model Training Data Risk Auditor

A single prompt that forces AI to search Reddit, forums, Medium, and review sites for a brand, surface reputation patterns, flag vulnerabilities, and generate action items.

Steal forAgency client audits and finding legacy negative content before it shapes future AI recommendations

13:50list

Multi-step AI content production process

Generate topical map with 250 protocol prompt
Generate outline using Reddit, PAA, and hyper-local context
Write full draft with structured prompt specifying audience and format
Human editor reviews every piece before publish

Structured production pipeline that avoids generic AI output by front-loading research and specificity then adding mandatory human review.

Steal forContent agencies producing high-volume batches without sacrificing trust or accuracy

CTA Breakdown

How they asked for the click.

VERBAL ASK

17:24next-video

“if you want to see the exact Claude workflow my agency uses to produce this content at scale I break all of that down in this next video”

Soft next-video CTA with no hard subscribe ask. Community via Skool is promoted mid-video through the prompt catalog, which is the real conversion vehicle.

MENTIONED ON CAMERA

05:49linkAI SEO Mastery Skool community prompt catalog ↗

FROM THE DESCRIPTION

PRIMARY CTAWhere the creator wants you to go next.

OTHER LINKSAlso linked in the description.