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Anthropic vient de révéler les secrets des compétences de Claude (à piquer)

IABrock Mesarich | AI for Non Techies5 juin 2026 à 17:0010:29
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INTRO

Anthropic a expliqué comment des « compétences » structurées et à périmètre restreint, ainsi que des pratiques d’itération, améliorent nettement les performances dans Claude Code, en mettant l’accent sur la flexibilité, la conception du contexte et les retours d’usage réels.

POINTS CLÉS

Compétences définies comme des systèmes fonctionnels

Anthropic décrit les compétences non comme de simples prompts textuels, mais comme des dossiers structurés contenant instructions, scripts, données et ressources. Ces éléments permettent à l’IA d’explorer et d’exécuter des tâches avec plus de précision. Cette définition élargie remet en cause l’idée qu’il s’agit de simples fichiers markdown, en soulignant leur rôle d’outils modulaires riches en contexte.

Des centaines de compétences utilisées activement

L’entreprise indique utiliser des centaines de compétences en interne, montrant leur rôle central dans l’accélération des workflows de développement. Elles sont continuellement affinées via l’usage réel, les améliorations étant guidées par les échecs et cas limites rencontrés en production.

Neuf catégories de compétences identifiées

Une analyse interne a regroupé les compétences en neuf catégories, dont références de bibliothèques/API, analyse de données, automatisation métier, scaffolding et runbooks d’incident. Un point clé: les compétences les plus efficaces appartiennent clairement à une seule catégorie, tandis que celles trop larges dégradent les performances.

Éviter de surcharger une seule compétence

Combiner plusieurs fonctions dans une même compétence nuit à la clarté et à l’efficacité. Anthropic insiste sur le fait que des compétences à périmètre restreint produisent des résultats plus fiables, suivant une logique modulaire proche des microservices.

Les “pièges” comme contenu à forte valeur

Recommandation majeure: inclure une section « gotchas », qui documente les erreurs fréquentes et schémas d’échec. Elle est décrite comme la composante au signal le plus fort, aidant le modèle à éviter les écueils connus. Cette section s’enrichit avec le temps et devient une base de connaissances vivante.

Utilisation d’exemples et de contre-exemples

Les compétences efficaces incluent des exemples positifs (bons résultats attendus) et négatifs (à éviter). Par exemple, une compétence de rédaction d’e-mails peut fournir de vrais messages réussis ainsi que des consignes pour éviter un ton générique, trop corporate ou artificiel.

Divulgation progressive via le système de fichiers

Les compétences sont organisées autour d’un fichier principal complété par des documents pour des cas spécifiques (debug, cas limites). Cette approche de « divulgation progressive » permet à Claude d’accéder à des couches de contexte plus profondes uniquement si nécessaire, améliorant l’efficacité et réduisant les tokens inutiles.

Éviter des instructions trop prescriptives

Contrairement aux pratiques courantes, Anthropic déconseille les instructions rigides étape par étape. Les compétences doivent fournir des objectifs clairs et des contraintes, tout en laissant une flexibilité d’exécution, afin de tirer parti des capacités de raisonnement du modèle.

Descriptions conçues pour l’IA, pas pour les humains

Les descriptions ne sont pas des résumés mais des signaux d’activation indiquant quand utiliser une compétence. Au début d’une session, le modèle parcourt les compétences disponibles et leurs descriptions, rendant le choix des mots crucial pour un déclenchement correct.

Collaboration et distribution

Les compétences peuvent être partagées entre équipes via des dépôts ou sous forme de plugins. Cela permet de standardiser les workflows et d’améliorer collectivement les outils, notamment dans les environnements collaboratifs.

Développement itératif à partir de bases minimales

Beaucoup des compétences les plus efficaces d’Anthropic ont commencé comme de simples instructions accompagnées de quelques “gotchas”. Elles ont évolué par itérations à mesure que de nouveaux cas limites apparaissaient, suivant une logique build-measure-learn plutôt que la recherche de perfection initiale.

CONCLUSION

L’approche d’Anthropic présente les compétences IA comme des systèmes modulaires évolutifs où clarté, contraintes et apprentissage itératif surpassent les conceptions rigides, offrant un cadre solide pour un développement assisté par IA plus efficace et scalable.

Transcription complète

Anthropic just dropped this article breaking down how they use Claude skills and all the key lessons they've learned while building Claude code. I went through the entire article and this is a bit more tailored towards developers. So what I did is I broke down key points here that you can use in order to implement into Claude to build better skills. So in this video I'm going to break down the key things that Enthropic has learned from building Claude code and how they're using skills. We're going to talk about the nine different skills that Claude uses and we're going to go over all the common misconceptions and mistakes people make when it comes to using Claude skills. So, by the end of this video, you'll walk away a better Claude user. I can guarantee you that. And there's a lot of things that I personally learned from this article that I'm going to be implementing myself. All right. So, first of all, if you want to read this yourself, there will be a link in the description to go through the exact same article I'm going to talk about in this video. So, without further ado, let's dive right into this. So, first things first, here are the key lessons that Enthropic has learned from building Claude code and how they're personally using skills across the entire Enthropic team. So, what better way to learn about how to craft skills than from Enthropic themselves. So, first of all, Enthropic says, "We've been building and using skills in Claude Code extensively at Enthropic with hundreds of them in active use. These are the lessons we've learned about using skills to accelerate our development." And from Enthropic themselves, here is how they classify what a skill is. Skills are folders of instructions, scripts, and resources that agents can discover and use to do things more accurately and efficiently. Now, if you've used Claude before, that is absolutely a no-brainer. You probably already know that, and you've probably begin building skills yourself. But what we want to focus on is the fact that they said a common misconception we hear about skills is that they are just markdown files. They're actually a folder that can include scripts, assets, data, etc. that the agent can discover, explore, and manipulate. So, these aren't just markdown files. Enthropic is basically stating that most people don't realize these are actual folders that include extra context in order to make these skills more useful. Later on in this video, I'm going to show you exactly what I mean. So, let's move on to the nine different types of skills Enthropic says that you can create. So, Enthropic says, after cataloging all of our internal skills at Enthropic, we noticed they cluster into nine different categories. And then this highlighted part is what we really want to focus on. So they said the best skills cleanly fit into one. The ones that try to do too much straddle several and confuse the agent. And then they go on to mention this isn't a definitive list, but it's a useful framework for us to begin identifying which of these different categories do each of our skills play into. So this part gets a little bit technical, but stay with me. So the first category is library and API references. So this is you know if you're using APIs, CLIs, SDKs, all these different things kind of more developer speak. Next up we have product verification. Then data and analysis. Next up we have business automation. You could think of these as you know our actual skills that are automating our workflows and specific tasks. We have scaffolding and templates. So framework correct boiler plates. Then we have code quality and review CD deployment incident runbooks infrastructure ops. So, if you're not a developer, a lot of these don't necessarily pertain to you. Most of your skills might fall under the business automation, data and analysis, and maybe your library and API references. I think the key thing to focus on here is not to fully understand each of these different types of skills. But I think what we need to take from this is that you want to make sure your skills aren't doing too many different things. You want to make sure it kind of falls under one of these categories and not multiple. Otherwise, it's not going to work as efficiently as if you have one separate for each different types of skills. All right, so now moving into the actual structure of our skills. Next up, they are saying that we should be building a gotchas section. They even go on to call this the highest signal content in any skill and that is the gotchas section. Says these sections should be built up from common failure points that Claude runs into when using your skills. Ideally, you will update your skill over time to capture these gotchas. So basically what Enthropic is saying is not only do we want to instruct Claude how to do something in a skill, but we really want to focus on what not to do and that's a really good starting point to kind of reverse engineer how to craft it perfectly. Now in order to show you exactly what I mean I'm inside of Claude Code here and what I'm going to focus on is I'm going to go over to one of the skills that I have. So I'm going to come down and click on email drafter skill. And what we're going to see is inside of this skill, we obviously have our demo skill, we have email drafter, we have examples, and then we have our actual skill MD file. What we want to focus on is the examples here. So it shows our good emails markdown. This right here shows references, which are actual good emails that we should be sending. Looks like we have a couple of different examples here that we could actually go ahead and look at. I personally had Claude go and read my email inbox to see exactly how I respond to my emails. And this is exactly, you know, the structure of that. Now, on top of the good emails example markdown file, we also now have this avoid markdown file, which if I open this up, this is what it looks like. It says what not to make an email sound like. Never sound like a corporate template, a LinkedIn thought leader, an AI assistant, all these things. So, it broke down basically the key things and the key wording it should never use. And this is something that we could add directly inside of the skill folder, but not inside of the skill markdown file. Now, this is a perfect segue into the next concept that they talked about in this article. And they suggested use the file system in progressive disclosure. And basically what that means is if we take a look at this graphic, we obviously have our main skill MD file, which is basically our hub for this workflow. This is kind of what that looks like. It's the markdown instructions on how this, you know, workflow should run. But underneath that, we have a couple of other different markdown files. We have a stuck jobs MD. We have a dead letters, retry storms, and consumer lag. Basically, what this means is if it ever gets stuck, it will read this markdown file and basically it will help debug that specific problem that the skill is running into. And if we come down to read the key point that Enthropic makes here, it says you should think of this entire file system as a form of context engineering and progressive disclosure. Tell Claude what files are in your skill and it will read them at appropriate times. So, this is a key thing that for me sometimes I forget or I just don't fully utilize. I really just think of the main skill MD file. I don't really think of these separate ones that we could create to troubleshoot things or, you know, to be really specific if it's a really niche problem that I'm trying to solve within that, you know, given workflow. Now, next up is something that's pretty contrary to what a lot of people say, including me, when it comes to creating our skills. So, Enthropic is suggesting to avoid railroading claude. And what they mean by this is giving it room to go and figure out how to do a task and not be too strict with how we get a workflow done. Right here it says Claude will generally try to stick to your instructions. And because skills are reusable, you'll want to be careful of being too specific in your instructions. Give Claude the information it needs, but give it the flexibility to adapt to the situation. Now, if I zoom in on this example that they give us right here, it shows that this is actually too prescriptive. It shows each of these different steps exactly what to do. Whereas it shows the better skill instruction. Says cherrypick the commit onto a clean branch. Resolve the conflicts preserving intent. If you can't land cleanly, explain why. So it's really kind of giving it the room it needs to go and run this task. Which I think for a lot of people is pretty contrary to kind of what they would believe. You'd really think that you want it to be very very structured so that way you get the same output every time. But apparently Claude is saying that that's something that they try to do is they try to avoid railroading Claude and instead let the model just do its thing and figure it out. Next up, this one's pretty interesting and this makes a lot of sense. They're saying to write descriptions for the model, not for humans. So when Claude Code starts a session, it builds a listing of every available skill with its description. And that part is really important. And in order to show you exactly what I mean, if I pull up this skill here, you can see that I have, you know, description. And a lot of times somebody might think, hey, maybe I write this description for me so I could understand, you know, and remember what this skill is for. But really that's not the case. Anthropic is saying that we need to use this description box in order to give our AI model context on when they should be running that skill. A key thing here is this says this means the description field is not a summary. It's description of when to trigger this skill. So this is something that I think a lot of us could actually take and begin applying immediately. it could be worth it to go into Claude and say, "Hey, I want to actually update the description for all these different skills." And then we could go and begin editing those and hopefully it makes them run quicker as well as save you on some token costs. All right, so next up, they talk about distributing skills. One of the biggest benefits of skills is that you could share them with the rest of your team. This is something we know if you've used Claude or Claude Co-work before, you can use something called plugins. They say there are two main ways right now that you could share these with others. And I get so many questions inside of my school community about what's the best way to go about sharing the particular skills that we make ourselves with our team members. So there's a couple ways we could either check our skills into our repo under cla-kills. And in order to show you what I mean, if I come into claude code, you can now see in my files I have this skills section. So if I click on this, click on skills, we now have, you know, all these skills right here that we could always pull up at any given time, see our markdown files here, and begin making changes and edits there. So, we can take our skill file, share it with a team member, and all they have to do is dump it into this.kills folder. And the other way to do this is to just simply make a plugin that you could share with your team. This is going to be better if we have a culmination in a bunch of different skills that we want to share as opposed to just like, you know, one or two singular ones. And lastly, the main takeaway from this is they say most of enthropic skills that they've created began as a few lines in a single gotcha and then got better because people kept adding to them as Claude hit new edge cases. So really, sometimes it is best just to start from scratch. Really start from zero. Try to explain what it is you're trying to build. Make sure to add some of the gotchas that we talked about earlier on in this video, which is basically just explaining what not to do a lot of the time and then begin just making changes and iterating from there. Anyways guys, I hope you got some value from this. Subscribe to this channel for more Clawude and AI content for nontechnical people. If you want to dive deeper, make sure to join my school community. And if you want the 15 Claude skills that I can't live without, there's going to be a link in the description for free. Thanks for saying the end and I'll see you in the next

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