How Cats.txt Showed LLMs.txt Evidence Is GEO Astrology

I got tired of watching the industry treat "an AI bot fetched it" and "ChatGPT said it helps" as evidence that llms.txt does anything, so I invented a standard called cats.txt: a text file in which you formally declare your office cats, their jobs, their breeds, and how often they purr. I wrote a specification, published it on my blog, and did a LinkedIn post explaining why you should definitely adopt it, because as we all know, large language models love LinkedIn.

Then, I checked it against the exact four "proofs" people cite for llms.txt. It was crawled by the AI bots.

How A File About My Cats Came To Be A 'Web Standard'

It began, as these things tend to, with irritation. For months I had been watching perfectly sensible people point at four observations: the bots crawled it, Google indexed it, an LLM repeated it, ChatGPT endorsed it, and present them, in. The search implication is whether the section improves the evidence around the page, not simply whether it adds more wording. Clear entities, crawlable structure, internal links, and useful context are what make the topic easier to evaluate.

The risk is usually hidden in the execution layer. A page can look fine to a human and still fail for an automated visitor if the form, call to action, rendering path, or confirmation step is not accessible enough for the agent to complete the task.

A Word Of Genuine LLMs.txt Fairness First

I do not much care whether llms.txt works, will work, or how long it takes to get there. For the length of this argument, I am happy to park two inconvenient facts and grant the idea every benefit of the doubt. The first is that no large. The strategic issue is whether automated visitors can understand, trust, and complete the same journey a human visitor can. Agent readiness is partly technical, but it is also about clear tasks, accessible flows, and reliable evidence.

The useful check is whether this improves the system behind search performance, not only the words on the page. Internal links, crawlable content, clear entities, current evidence, and a sensible page structure all help the recommendation become easier to trust.

The Faulty Thinking

The trap is this: Getting baited into treating a set of observations as evidence, when the observations would occur whether or not the underlying thing were true. It is the intellectual equivalent of concluding your umbrella causes the. The practical question is what this changes in the system: the page structure, the evidence presented, the measurement habit, or the way the topic is connected to related work.

The practical value is in connecting the idea to an observable signal. That means deciding what should be checked, what would prove the issue is real, and where the team should make the smallest useful improvement first.

1. 'It's Definitely Used, The LLM Bots Crawl It!'

The first argument: You can see Anthropic crawling it, you can see OpenAI crawling it, the bots turn up in your logs, therefore the file is being used. A crawler fetching a file tells you nothing about whether the contents are read,. The strategic issue is whether automated visitors can understand, trust, and complete the same journey a human visitor can. Agent readiness is partly technical, but it is also about clear tasks, accessible flows, and reliable evidence.

2. 'It Was Indexed By Google, So It Must Matter!'

The second argument: The file was indexed by Google, which proves Google considers it important, because why would Google index something that didn't matter? Google indexes text files. It has done so, enthusiastically, since before most of. The search implication is whether the section improves the evidence around the page, not simply whether it adds more wording. Clear entities, crawlable structure, internal links, and useful context are what make the topic easier to evaluate.

3. 'ChatGPT Returned Information That Was Only In My LLMs.txt File'

The third argument is the strongest looking, and therefore deserves the most care. The claim is that a model produced a fact that existed only inside the llms.txt file, and therefore must have read the file as a special, trusted source. The search implication is whether the section improves the evidence around the page, not simply whether it adds more wording. Clear entities, crawlable structure, internal links, and useful context are what make the topic easier to evaluate.

4. 'ChatGPT Itself Says LLMs.txt Helps!'

The fourth, the cloudy summit of Mt. Stupid. You ask ChatGPT whether llms.txt works; it tells you yes, that it can probably help, you should do it, and you take that as confirmation from the horse's mouth. A language model telling you. The search implication is whether the section improves the evidence around the page, not simply whether it adds more wording. Clear entities, crawlable structure, internal links, and useful context are what make the topic easier to evaluate.

The Convergence Problem

This last one is not merely funny. It is the mechanism underneath all four, and it is worth naming: the convergence problem. When you ask a model whether llms.txt helps, it is not reasoning. It is not running an experiment, consulting a. The practical read is that brand signals need to be consistent enough for both people and AI systems to form a stable view of the company, its expertise, and its trust signals.

Why Any Of This Matters

I am not doing this purely for sport, though I will admit the sport is excellent. There is a real cost hiding under the comedy. Every hour, and every dollar spent implementing llms.txt, or the next GEO ritual, or the one after that, is an. The search implication is whether the section improves the evidence around the page, not simply whether it adds more wording. Clear entities, crawlable structure, internal links, and useful context are what make the topic easier to evaluate.

How A File About My Cats Came To Be A 'Web Standard' in practice

Introduction I got tired of watching the industry treat "an AI bot fetched it" and "ChatGPT said it helps" as evidence that llms.txt does anything, so I invented a standard called cats.txt: a text file in which you formally declare your. The practical read is that brand signals need to be consistent enough for both people and AI systems to form a stable view of the company, its expertise, and its trust signals.

What the visibility signal actually changes

What the visibility signal actually changes: how Cats.txt Showed LLMs.txt Evidence Is GEO Astrology: the Practical Angle should be treated as a visibility signal, not a standalone headline. Introduction I got tired of watching the industry treat "an AI bot fetched it" and "ChatGPT said it helps" as evidence that llms.txt does anything, so I invented a standard called cats.txt: a text file in which you formally declare your office cats, their. This connects with LLMs & the Low Bar when the same signal needs a clearer operating decision. A useful companion note is Google Answers Question About LLMs Author.txt, because it looks at a nearby part of the same system.

What the visibility signal actually changes: the practical question is whether the page, brand evidence, and surrounding content make the answer easier to trust. If that support is weak, search systems can still understand the topic but fail to connect it confidently to the brand.

What the visibility signal actually changes: that is why the response should begin with an audit of the evidence already on the site before creating a new asset. The fastest improvement is often a clearer page, a better internal link, or a stronger explanation of why the brand belongs in the answer.