We all know the frustration of going back and forth with AI only to end up with a completely useless output or the feeling of cringe when we see someone else or even worse a team member put out a clearly AI generated LinkedIn post, email, comment, or anything else. And of course good prompts, skills, and context can improve AI outputs, but even with that AI drifts and it never actually guarantees high-quality outputs. And across an entire business most people won't actually apply many of these best practices resulting in lots of AI slop going out into the world. So in this video I want to show you my new favorite skill that deslop every AI output before it goes out the door, show you how it can prevent you and your business from putting out slop, and show you how to adapt it for yourself or your business easily. Now you'll be able to download the skill for free in the first link in the description below, but before showing you the skill with some examples and exactly how it works, let me quickly go over why I actually built this and why I think it's so powerful. Now I basically built this because I kept seeing a recurring pattern for myself across my own business and kept hearing it more and more from some of my AI agency clients and I've been reading it online more and more too, which is something along the lines of yes, me and my business are probably getting more done with AI, but I'm also producing and seeing more low-quality work across my business whether that's in marketing, internal presentations, customer support replies, or any really asset inside of a business. I noticed this for myself. Sometimes I look at past things I put out and I go like Ben this is embarrassing. Uh but I also notice it sometimes with things my team members put out. And of course if you look at LinkedIn these days you might wonder if there are any people actually writing stuff themselves anymore. And this is of course increasingly becoming a problem across businesses, which is something they call work slop. And there are even companies that go as far as banning employees from using AI for anything public-facing just to protect their brand. So I started to think about how I could potentially solve this by firstly thinking about what slop actually is. And what I realized is that the term slop is actually subjective. What I might consider slop, one of my team members or someone else might not consider slop. And it has a lot to do of course with your taste and your specific quality bar. If your quality bar lies here, you would consider this AI output slop. But if person B's quality bar lies here, he might say this is perfectly fine. And if person's A quality bar lies here, you might think this is a great output. And honestly, I think this quality bar with more and more people using AI for everything is going to really set you apart from other people. But what I've also noticed is that my own personal quality bar changes moment to moment. What I I don't actually enforce it consistently. Because when I get tired, for example, when I get frustrated with AI, when I have no time or I have to just get something out quickly, a lot of my sub principles go out of the door. So my quality bar drops and I might just put something out into the world that doesn't actually meet my standard. And this is the first reason I built this skill to enforce my own standard even when I can't be bothered to. Because you define what good looks like once and the skill holds you to that on every AI output that goes out into the world. Think of it like a a spell check, but for slop. And then the second reason I built this is of course one of the biggest problems for companies is that everyone, every employee in the business have a has a different quality bar in their head. And each individual AI just tends to agree with everything the user says without having any notion or idea of what the actual quality bar is for the company. And besides that, AI just reinforces the user. It doesn't necessarily reinforce the truth or the truth within a company. And with more and more employees using AI, it becomes impossible for a company to actually see all of it, let alone adapt it. There's an excellent article by A16Z on institutional AI versus individual AI that talks about exactly this problem. The article starts off with a question, AI just made every individual 10x more productive, but no company became 10x more valuable as a result. Where did the productivity go? And the main point he makes is that giving every individual a productivity boost with AI doesn't necessarily add up to a better company because speed of production without a shared direction or quality benchmark means you're not actually adding value to the company but creating more chaos. And in order to actually do that, the way a company works and the technology itself need to be redesigned to actually make it useful for an institution, not just individual. Now, I thought it's a great article but it goes a lot more in depth. Definitely recommend reading through it if you have time. I'll make sure to link in the description below, too. So, that's the second reason I built this, to run that same slop check across my entire business. So, every AI output, no matter who generated it in my team, actually is aligned with my company's truth and meets my company's quality bar. That's why I'm such a proponent of the AI OS or shared second brain across a business because of course then everyone's AI tools and AI agents can actually pull from the company's shared strategy and knowledge docs and of course align them a bit more. But just providing these models with um some context doesn't actually guarantee that the outputs they generate are actually well aligned and meet the quality bar of a company. So, that's the reason I built this. So, every AI output, no matter who generated it in my team, actually meets the company's bar before it goes out. Now, before showing you some examples, let me quickly go over how this actually works. Now, the idea again of this D Slop Scale is of course to use it on any AI output before you or any of your team members puts it out into the world uh whether that's an internal or external use case to make sure it passes that quality bar and if not, suggest changes. Now, the way it does that is by first going through a universal slop check which basically checks universally agreed upon slop patterns like m-dashes, um AI writing patterns, AI words. It fact checks the the AI output. It looks at contradictions and unreadable writing. And then it runs the the layer which is the company slop check, which is the subjective layer. Does this actually match my or my company's standards on the tone of voice? Does it match our brand identity? If it's a visual, is this aligned with our company strategy and our company facts? And does it actually do the job it's supposed to do? And this is of course the part that has to be personalized for you or your business. Now, the skill will basically do this through the following process. First, it checks what kind of output this actually is. Is it a marketing post? Is it customer support reply or an internal document? Based on that, it defines which checks it has to do. I have checks for universal slop detection and company-specific slop detection. And each of the checks basically has a reference file with instructions and guidelines that the output should follow. For example, the AI writing tells doc looks at common AI content patterns like language, grammar, vocabulary, and style patterns to detect AI writing. The factual accuracy does a systematic claim check by checking against authoritative sources on the internet. And for the company-specific ones, for example, my voice doc, it has guidelines on how my brand's voice should sound and not sound. For example, no guru energy or no bold claim or big statements with no backing or no nuance. Now, I can read all of them if you download the skill and you can adapt and personalize it easily, which I'll show you how to do in a second. Then it spins up three sub agents that grade the output against the universal and company-specific criteria. We do this through sub agents to make it more unbiased. And then lastly, it gives you a clear verdict if this is good enough or not, gives you suggestions on how you might want to change it, and mentions the sources it found it in. It can be one of three verdicts: good to go, good to go but might want to fix some things, or not ready. For example, here I ran one of my other skills to do a follow-up email after a sales call that basically goes through the last sales call transcript and write a a email. And then I asked it to run the D-SLOP scale on the email copy. So, in this case, the verdict was good to go with a few things you might want to fix, and it gives a table with an overview of what might be off and a suggestion on how to improve. So, it tells you for each of the checks what might be off in this one, for example, the source of the reference file that backs up that claim, and a suggestion for how to improve it. So, in this case, it detected a couple of signs of AI writing. The fact check actually detected that something in the email copy wasn't completely aligned with what actually was said call, and it found some small readability and voice issues. Now, the point is they're not always going to be perfectly relevant. That's why you can easily decide which of these suggestions you actually want to apply, and you can then tell Claude right away which fixes you want to apply, and it can rewrite the copy actually based on a good source of truth. Here, for example, I ran it after using my LinkedIn skill to write a LinkedIn post, and in this case, it came back with a verdict not ready because the post breaks some hard rules and has many issues. But, it can be run on anything. Here, I ran it on a newsletter. Here, I ran it on a sales proposal, where it actually also checks the visual guidelines, but I think you get the idea. And the point of this scale is not to expect it to fix everything perfectly instantly. That's not really the goal and the in the scope of this scale, because of course, there can be hundreds of different AI outputs or different assets that it can judge, and it doesn't have enough context around each of the specific use cases, which can literally be hundreds. But, that's not the point of this scale. It just detects things that that are potentially misaligned with the brand. And if they're completely misaligned, it flags it and tells you this is not ready. You can then decide based on what those suggestions are what changes you actually want to apply. And if you use this across a company or in a team, the nice thing is that it will be a very nice, easy helper for all of the team members to improve their outputs fast, but it also makes your team more aware of what's actually important in your business. Basically, it makes them more aware of the quality bar. So, I basically added this as a rule for myself and for all my team members to always run this before putting any AI output into the world. And it can also easily be added as a last step inside of skills, for example. So, for example, my LinkedIn post writer skill, it could be the last step inside of the skill, and then there's actually one more step to the skill, which is at the end it will add the run into the D Slop log. Basically, it logs every time it runs in order to start detecting common patterns across the business in terms of what's going wrong with these AI outputs and it logs how often people use it. In my case, it saves in in my second brain or my AI OS, but it can also save it inside of the skill. And if you already have a shared second brain set up for your company, I think this could be a great add-on to combine it with this skill to not only provide everyone's AI tools with better context, but to also make sure that AI outputs are actually checked against the company's benchmark because that's what, of course, the second brain set up can't do or can't guarantee. Now, you can download the skill for free in the first link in the description below. I'll add some blanks where you need to personalize it for yourself and then you can ask Claude to personalize and fill out some of these for yourself like voice, etc. Or, if you're interested, we've also built a D Slop Builder skill that basically walks you through step-by-step through the process of personalizing the skill for yourself, which is available in my AI Accelerator community in the second link in the description below together with all the other skills and plugins we're building out inside of my accelerator. We also have full courses on how to set up your own second brain, your OS, Claude courses, unlimited one-on-one live tech calls, and multiple weekly Q&A sessions. So, if you're interested in that, you can check it out in the second link in the description below. Now, that's it for this video. Thank you so much for watching. If you got any value out of it, I highly appreciate like and subscribe. It really does help me. And if you want to learn more about Claude and Claude's skills, you can check out the video here above.