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- [00:01]
A
Today on the AI Daily Brief, an agent skills masterclass. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. Alright friends, quick announcements before we dive in. First of all, thank you to today's sponsors. Recall AI robots and pencils. Blitzy and super intelligent. To get an ad free version of the show, go to patreon.com aidaily Brief and if you are interested in sponsoring the show, send us a Note SponsorsIDailyBrief AI now, one other note. Today's episode, of course, features the one and only New Far Gaspar walking us through a masterclass in agent skills. For anyone who listened to my agent skills primer, it's a really good part two for that that gets much more practical with a whole framework for how to use skills and how to use them well. Now you can get all of this companion data, including things like the anatomy of an effective skill over on Play aidailybrief AI. That's where we keep the companion experiences for this show. And if after that you want even more. Nuphar, we have just opened up the second cohort for Enterprise Claw, which is an agent and agent team building program. I'll have links to all of that in the show notes. For now though, let's dive in and up your skills with skills. All right, Nuphar, welcome back to the show. We're talking skills. How you doing?
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B
I'm good. Happy to be here.
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A
Yeah, we are. Man. It is. Since the last time you were on the things that matter in terms of teaching, being up to speed, getting up to speed with AI are. Some of them are. Obviously there's fundamentals of teams and expectations and things like that that remain. But God, the last time you were here, that stuff we were talking about feels like ancient compared to where we are now.
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B
The human element is the, the, the technology is completely different.
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A
So what we're talking about today is I did a couple of weeks ago on the show, kind of an introduction and a primer to agent skills. It's a standard, a sort of primitive for the agent era that helps agents figure out how to do things that you need them to do in very simple terms. But obviously there's a lot more complexity in how you use them and how you use them well. And that's what we're going to be talking about today. So tell us a little bit about. About what, what we're going to go through and then let's.
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B
Okay. So obviously you did a very good job in your skill episode. You talked about what they are, the entropy categories and the various things that are currently like a landscape overview. But today I want to go much deeper and make it more of an operator cut, because I want to give people the actual playbook on how to build skills that work, what mistakes kill them, and what organizational opportunity really looks like. So we made it fun like we always try to do, and we structured it in a five level journey. So by the end, hopefully you will go from understanding skills are to knowing how to build an organizational skill library. And everything is accessible to you guys on the Play Daily brief, which we will demo in a minute. Okay, so we have five levels from apprentice to architect. So to make sure that we are all on the same page and give a reminder of what skills are at the core. Skills are just folders, not just markdown files. Folders that contain instructions, scripts and resources that give AI tools and agents the actionable playbooks to execute various tasks. But here there's something that many people are kind of missing, and that is that skills are not just for agents to read. They work in two modes. An agent can discover the skills that you enabled in the environment and it can do so automatically and invoke them on its own. Or us humans can trigger them manually, either by using the slash commands in most tools, or we can just provide verbal cues and the tools will know to pick up the skills that we intend them to use. So for example, you may say research this topic and it fires a very specific research skill that you built that is very specific to what you like in terms of doing the research. So that's something that we'll also show in a minute. And the very good thing about skills is that they are highly portable. You know, most of us have built many custom GPTs or gems over the last few years. The problem with them is that they were locked inside the ChatGPT or the Gemini Enterprise. Forever Skilled basically solved it. They are folders that you can just take with you between tools. They are human readables. So there is no proprietary format. And anyone in your team can open a skill file, read it, understand it, edit it, and you don't need any engineering degree and you can just take it between tools. Why are we saying that skills is not only the present, but also the future of AI and agent tools? Because we're already seeing that all major companies are supporting skills. Currently we counted about 44 tools and counting every day yet another tool introduces that they support tools. Recently Notion said so and many other tools already announced that we of course include in the tools that support skills, the Open Claw, the Claw, the cursor, windsurf, GitHub, and many, many other tools, they're all supporting that, but not just coding tools. And then we have people that have been basically using and building skills effectively for a while. And they will tell you that this is probably the complete game changer to how AI and agents work for you. And also it's quite addictive. Like once you start realizing the power of skills, odds are that you will create more and more and more. And I do want to flag out one thing very explicitly here, is that third party skills, one that you acquired from somewhere in the Internet, whether it's in Open Claw, Marketplace or other places, they are code and as such they can run with a lot of your agent permissions. And if you download that, it can execute scripts and sometimes it can be a malicious script. So be very, very careful. Whenever you're getting a skill that you have not verified the source, read it very carefully and treat it like installing any software package on your machine. And especially if it's a work machine, be very careful and pay attention. So you will not bring any malicious software back into your organization. All right, so this is the basic. Let's talk about when to build skills. So the question is, when should you build skills? And I wanted to start with three obvious signals. Either when you do something more than three times, that to me is a good indication that now is a good time to build a skill, or you keep basically pasting the same instructions and getting very frustrated with your tool. And that's another good one. And also when you need a consistent output. But the two additional things that you want to consider, first of all, this is a great opportunity to standardize things across either the way you do the work or others do the work with you. It's a great opportunity for you to think of all the things that you ever wanted to be more consistent of or get more consistent behavior by others and just build a skill to get others to behave the same way. And lastly, and that's something that often also NLW talks about, skills are not just a way for you to be more productive. It's also a way for you to unlock opportunities of things that you always wanted to do and just didn't have the bandwidth or the ability to do so. So think outside the box of what are some of the research tasks that you never had the opportunity to do? Or what are some of the work and business challenges that you could never ever solve because you didn't have the Know how and the bandwidth. Okay, I want to talk about two other things. One is that skills cannot be 10 different things. So one skill per task, if you find yourself getting to a point where it's completely separate jobs, separate them to different skills. And lastly, when it comes to the question of reuse versus creating a skill, I know that there are many marketplaces out there, and especially in the OpenClo ecosystem, we know that there are an abundance of them. Similarly, with the anthropic skills repo, however, it's very hard to navigate some of these skill marketplaces and find the exact fit. And often you will find yourself wasting so much more time just trying to read what others created versus creating your own. So, especially if you want to hone your ability to create skills, I will actually recommend that you sit down, build a skill for yourself, leverage some of the best practices that we're showing here, and at least you will learn how to do it well later on. You can of course go and search what others are building, because some of the skills that people are building are amazing. But I would advise to lean on more heavily towards building at this day and age.
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One note on that front. I agree entirely. I also think that by virtue of them being sort of marked, you know, just markdown files, you can also treat even skills that you download as templates, not things that you have to copy wholesale. So in the next show that I do this week, it's going to be a personal context portfolio and I'm sharing a GitHub repo that has basically templates for 10 files about yourself. And it's sort of. It's not meant to, it's not something you would copy, it's about yourself. So you have to use it like a template. But I think there's a lot of resources out like that. And so I think it sort of puts a fine point on the idea of wanting to have the skills to build, because it actually unlocks using all of these things that are out there in different ways, that aren't just sort of blindly copying it into your projects and hoping it works.
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Yeah, and by the way, versus custom GPTs that were black boxes, if you were to use others now, you get the full visibility into how this skill is instructed. So if you don't like some of it, just change it. I just wanted to note that Claude created an amazing entropic, rather created an amazing skill creator that they recently released. And I definitely encourage you to go and use it because it's genuinely impressive. It interviews you to extract your expertise it runs evals, it does a B testing and benchmarking. So if you are a cloud user, you can definitely leverage their skill creator tool to do it even better. But in case you're doing it on your own or you just want to understand what is the anatomy of a very effective skill, we created a list for you. So every skill should have some of these elements and I want to emphasize a few of them. The most important part is the beginning and that is the trigger. The trigger is how you instruct the tool on when to discover and when to basically fire this skill. And it's probably the most important line because if your trigger is not very precise or very meek, then your skill will just not be used and selected by the agent. So I would advise actually that you make it louder rather than quieter because the models will sometimes skip past more subdued descriptions. So trigger words, exact descriptions about when do you expect to be used? And be more explicit than implicit here that will go a long way. And then we have the body and what most people go wrong with the body that they write. Prose and skills are like playbooks, so favored and numbered steps or bulleted lists. Claude and all of the AI tools they really like structured instructions dramatically because that will also turn to be their action plan if it's very, very concrete. So try to make it as literal as possible. That's how the tools like to follow the instructions. However, I want you to also set the right level of freedom. So if a task is very fragile, like a database, migration, coding, querying, something that has to be very precise, be very prescriptive with the step by step. But if it's more of a creative task, like writing a strategy doc or something that is more open to interpretation, give the guidance. But do leave some room for the tool to be creative because if you're over railroading the model, you will not get as good results. We also encourage you to make sure to include an output format. And here it's even better if you just include an output example. So show the model Dante just described. If you want a template, include it. If the output is a table, show a table and headers. If it's a document, show the section structure. So that's very useful for you to get exactly what you want out of it. And another section that Anthropic recommended very strongly is the gotcha section. This is probably the highest signal content in any skill because it's the area where gets the model to go out of its own patterns. Because you're looking to put here things that where the model will typically go wrong or what assumption it might make that it shouldn't. And you need to say something like I know you want to do X, but don't, here's why. And every failure that I've seen is probably something that you should document here after you stress test your skill, a few things not to include are some of the classical prompting skills like don't include the Persona and stuff like that. That's not useful. The tools are looking to get playbooks a few skill killers that you should avoid. First of all, it's the trigger. If the trigger is not well set, the skill will never be picked for usage. Second, over defining the process, like we said, don't railroad the model. Also, don't state the obvious. Don't waste tokens on things that the model already knows. And we strongly recommend that you don't skip the gotcha section because this is often when your skill will go off or will create suboptimal results. And lastly, don't do like a monolithic blob everything crammed into one file instead of using more of a folder structure. So speaking of folder structure, the recommendation is to keep skill under 500 lines because it's a playbook, not the encyclopedia of everything that you do for work. If you have reference materials or context that are very important for the skill, move them outside of the Skill file into a separate set of files within the Skill folder when it's relevant. If you also have very long input and output examples and you should include input and output examples, you can also put them in a separate examples MD files inside the Skill repo or the Skill folder. That will help you a lot. And that's probably the most effective way to communicate the desired format. In terms of the discussion of whether or not I should append a bunch of files into my actual Skill folder that will be bundled with the Skill wherever the Skill goes versus perhaps just pointing the skill to my other files and other systems. The deciding factor should be if this is something that is context specifically for this skill that should always come with the skill. Whenever I'm offloading the skill to someone else, then put it within the Skill folder. Otherwise, when it's stuff that are more general about you or about your company that can be pointed to an external source,
- [15:10]
A
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B
Let's show you a concrete skill that is slightly more advanced and that is a meeting prep skill. Obviously all of us, no matter what we do, we have to get ready for meetings. So I wanted to showcase an interesting skill here. First of all, description when to use when users say prep for the meeting, meeting prep and so on. So it's offering quite a few options. So the skill will be picked up in almost all scenarios in terms of context required. So as part of the skill folder, I bundle stakeholder context. So either it's going to be transient or if you have regular stakeholders that you work with, that can be fixed context file that comes with the skill, it should get some email history for recency. So that can of course be pulled directly from the user systems calendar and other open action items involving the attendees. In terms of the steps, identify all the attendees from the calendar or the other inputs, collect the context, analyze the agenda, run scenario analysis. So part of what this skill does is kind of preparing you for what can go wrong with your meeting and generate a brief. And the output will be structure that is defined in an attached file because it's a very long and specific output. And in broad strokes the structure will be executive summary and so on. A few got you. That can happen when you're getting ready for a meeting with such a skill. Sometimes AI assumes attendee seniority just from title and if someone is a vp, they assume that they are the most important person in the room and attributes unnecessary. Wait for them, don't fabricate company details, don't prepare generic talking points, don't skip the what could go wrong analysis and so on. These are things that happened when triggering the skills without it. So that's why they're here. And the way this overall folder is structured, we have this file, we also have, like I said, the stakeholder context is an external pointer to be shared across relevant skills. There are brief format scenarios, examples and also there is a nested skill. And in this nested skill there is a sub skill of how to simulate the actual happening of the meeting. And that's also a very cool skill that will basically come with six to seven different scenarios of what could go wrong. We'll see like if someone is joining your meeting and has a hidden agenda, how will you address them? Someone is asking you difficult questions. So it will literally help you get ready for difficult questions. If you use it for a sales call, it can come up with difficult questions around the sales and so on. So that's an example of how you would build a skill that also has context and perhaps refers to another skill. Moving on. So obviously there are many, many skills that people have, but I wanted to include here 4 ideas of skills that might be useful for anyone who is a knowledge worker, which is most of the audience here. So first of all, as part of the materials that we provide you, we included an example of research with confidence. That's skill that not only does research that is very precise to what you care about, time horizon, specific sources, but also it has a built in fact checking methodology where it will compare sources and do a deeper dive into specific things that seems off as well as giving you confidence scoring about how securities with the findings. So, so you can decide how deep to go in. So that's one skill that I think every person who does any type of research, which is all of us, should build or reuse. Another one that I really like is Devil's advocate. This is basically a skill where we say take any proposal and systematically stress test it. What makes the version that we included a little bit more special is that it explicitly looks for blind spots and biases, both on your side and on the AI side because we know that the models have many of their biases. So it explicitly tries to avoid those and it always ends up with something that is more constructive. So it's not just kind of finding holes with anything that you want to do, but also helps you to be back to something that is actually actionable. And another skill that we created is a morning briefing. That's another classical one. It pulls together your priorities, calendar, pending item, relevant news. And the thing that makes it more powerful is that it binds your personal context files with the skills including the goals, the current project and stakeholders. And as part of the materials that we provide with this episode, we also included a prompt that lets you create one for yourself that will interview you and make sure that you will be able to create your own morning briefing. And another one that I strongly recommend that you will build is a board of advisor skills. So either one or several of those basically will simulate perspectives coming from multiple expert archetypes. So it won't be just like think like a cfo, but rather you can think of all the different perspectives that will help you make decisions. So if you are a startup founder, then perhaps your board of advisors will have someone coming more from VC background, someone from more of a entrepreneurial Background your imaginary advisor and various other perspectives. And you basically create a skill that gets them all to advise and assist you by providing various perspectives on any decision that you would like to make. A few more advanced patterns that for people who are already building skills for a while and want to take it one step further. First of all, having a dispatcher skill, which is a meta skill that reads all of your requests and routes them to the relevant skills. It's like a traffic controller basically, and it's very important where in your library of skills go past 10 or 15 active skills that you regularly use. Often I would advise that you will create this dispatcher instead of hoping that the agent will read through all the available skills and pick up the right one. And this is especially important when you have nuanced similar skills that you want to be picked up in completely different scenarios. Another thing that you can do is you can chain skills one after the other, either automatically by having a skill that basically calls one skill after the other, or manually, you will take the output of one skill and it becomes the input to another skill. So in the examples that we've shown before, maybe you start with research with confidence, and then the output you take it to the devil's advocate to poke holes in the research, and then you take it to another skill that does an executive summary and deck preparation for you. The only thing here is that skills need a clean input and output, so that's an important thing in order to change them. Well, obviously recently we've been seeing more and more the emergence of loops, agentic loops and other loop patterns. So you can also create skills that create stuff like that, that they will iterate, check, act, check again, and then iterate. And it's becoming very interesting also for non technical stuff, because you can think, for example, on marketing campaign optimization, for example, you will monitor your ad performance, adjust the bids, recheck, flag when the specific metrics that you're following, like rose or others, drop, do competitive analysis and vice versa. So you have like an endless loop of someone that optimizes your campaigns. And you can also create skills that basically orchestrate multiple agents or multiple sub agents execution. You can just explicitly prompt them to spin up multiple agents. Our research skill also does that, so you can take a look at that. And of course the sky's the limit and we'll be curious to see if you have other advanced patterns that you've discovered that are working well with you. I want to make sure that you don't just create skills, but that you test them. And make sure that they're working well for you over time. I think the easiest test for you is if you find yourself having to iterate after you get the output of the tool that used your skill. That means that your skill is not good enough. Because ideally a skill should create a ready to use output. And if this is not the case, you have to go back and fix the skill. And this becomes even more important when you are about to share it with 50 different people. That's the case for you to treat it like any other AI product and basically run a proper evaluation. And the rigor, of course, should match the stakes. If it's something that also updates your CRM, then make sure that the skill is well tested. If it's customer facing, make sure that it's well tested. And there are some ideas here on how to test it. But in general, every time that you have a new model or that you have a different tool that will be using the skills, you have to go back and reevaluate. Okay, let's talk about the organizational perspective. So up until now, skills were at least it could be inferred that we're primarily talking about skills as a personal asset. However, organizations that are very AI forward already realized that skills are the future of how to streamline work and how to get everybody to get more value from AI. And as you can hear, this is where I get genuinely excited because it's basically the pipe dream of every knowledge manager that finally can become real. And you can think about it the following. You can standardize the way work gets done, you can get a lot of the work done autonomously or to some extent, and you can bundle that also with organizational knowledge. So everything can be bundled into a single portable artifact that both the humans can read and new employees can onboard using that as well as the agentic tools that will be using them and doing the work for you. So what I've seen happening in some organizations, they do skill hackathon, where they create skills for their relevant teams. They are maintaining skills in shared libraries like they would maintain code. They make sure the skills are having clear ownership and use across various people. And those organizations are seeing massive uplift with the quality and results that they're getting and the ones that are still not there, their people are kind of reinventing the wheel every time that they have a conversation with AI or even if they create skills just for themselves eventually. And we're already seeing that with the Claude cowork, most organizations will have the set of plugins. So in Claude Cowork. We're seeing plugins for specific professions, but you can create a plugin which is comprised of typically skills and connections and perhaps some context for each and every department or each and every group in your organization. And all of a sudden everybody enjoys the same worldview and the same goodness. So to be a little bit more prescriptive here, what I would recommend that you do at the org level, you will start with discovery, running work audits, or understanding where people do repeating work, or where people are not getting optimal value from AI and where there are wish lists that are not being covered. So you'll have a list of opportunities to create skill and then you will curate them and build skills using the best possible methods, maybe with Claude's skill creator or just some of the best practices that we discussed here. I then want to encourage you to do a lot of validation, especially if those are going to be shared across many people. Then perhaps the person who created this skill will replace with another person who created another skill and they will stress test each other and of course people who should use to poke holes in the skill itself. Then you should package them into plugins and reusable elements. And lastly, skills have to have clear ownership, whether they are AI champions or subject matter experts be reviewed every time that they are being updated. And when they're no longer relevant or no longer serving us, they should be deprecated because otherwise we will very quickly have a stale system that was amazing at the beginning, but is no longer the case here. So that's the five levels hopefully got you from skill apprentice to architect. Everything we talked about, plus full skill template and a lot of bonus content and some advanced patterns are all packaged very nicely in the AIDB play. Everything is there. Feel free to go and take a much deeper journey using this artifact.
- [30:39]
A
Awesome. Thank you so much. So one sort of just mental framework that I wanted to maybe close on is the last thing you said about deprecating skills when they no longer serve. I feel like even more we're used to infrastructure, which is what skills are as being sort of semi permanent or long duration. And I think skills feel like one of the first infrastructure primitives of the AI era that exemplify one how iterative things are going to be, two, the sort of shorter half lives that we have to assume for things that are valuable. And I think that what that means is you're not going to have an initiative to design a bunch of skills for yourself and for your organization where you do a sprint and then it's done it feels like it's going to be something that is just now a new recurring, ongoing part of working with these systems and basically requires constant upkeep. I mean, is that, is that what you found so far in your experience with them?
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Yes. And even in the materials. One thing that I included was saying that like reevaluate skills in the following scenarios and when one month has passed because that's about the time horizon where things might become a little bit stale nowadays. So at least until, and I don't know if it's going to happen in the foreseeable future, but until things will be more stable and you will have a more self healing system for skills management, you have to proactively go and revisit everything that is created, including by the way, the context as part of the skill. So often the skill itself will remain relevant, but maybe the examples or the context that the skill refer to. That's the problem of why we're not getting good results anymore.
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A
Interesting. We need a little agent that sits there giving a rating to how stale skills are based on when they were last updated.
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I'm sure people, the advanced organizations are already building these systems of automations of skill reviews and suggestions for improvement.
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A
Awesome. All right, well nufar, thank you so much for this shirt. Tons of useful stuff for everyone to dig in. Can't wait to have you back.