Imagine you are trying to teach a new intern how to handle your company's specific procurement process. You don't just give them a general handbook on business ethics and say, go for it. You give them a very specific checklist: check the vendor ID, verify the tax exempt status, ping the manager on Slack if the total is over five hundred dollars, and then log it in the specific SQL database we use. Those are the repeatable, precise instructions that keep a business from imploding. Well, we are finally seeing that same level of modularity hit the AI world. Today's prompt from Daniel is about agent skills, a concept popularized by Claude Code that is now exploding across every major AI toolkit. We're moving away from the giant, messy system prompt and toward a digital Swiss Army knife approach where you can just snap a new capability onto an agent like a Lego block.
It is a massive shift in how we actually build these things, Corn. My name is Herman Poppleberry, and I have been obsessed with this specific transition because it represents the professionalization of AI development. For the last couple of years, we’ve been stuck in this world of prompt engineering where you’re basically whispering sweet nothings into a black box, hoping it remembers to format the date correctly. But with agent skills, especially following what Anthropic did with Claude Code in mid twenty-twenty-five, we are seeing a move toward what I call procedural knowledge packages. By the way, fun fact for everyone listening, today’s episode is actually being powered by Google Gemini three Flash. It’s the model behind the curtain today, and it’s actually quite fitting given how much Gemini's long context window benefits from these modular skill structures.
It’s funny you mention the professionalization aspect, because to me, it feels like AI is finally getting its own version of a standard library. You know, like how Python has its built-in functions so you don't have to reinvent the wheel every time you want to sort a list. But before we get too deep into the weeds, let's actually define what an agent skill is for someone who hasn't lived inside a terminal for the last six months. How does a skill differ from just a really good prompt or a standard API function call? Because on the surface, it sounds like we're just rebranding things we already do.
That is the big misconception, right? People hear "skill" and they think, oh, it's just a fancy system prompt. But technically, it’s much more structured. In the Claude Code implementation, for example, a skill is a modular unit. It’s usually a Markdown file that lives in a specific directory. It contains frontmatter, which is basically metadata that tells the agent when to use it, what the triggers are, and what the constraints are. Then you have the actual instructions, which are the step-by-step logic. But here is the kicker: it often includes supporting files or scripts. So, a skill isn't just a block of text saying "be a good coder." A skill might be "Validate React Component Accessibility." It includes the specific axe-core rules to check, the exact CLI commands to run the linter, and a template for how the report should look. It’s a package of behavior, not just a personality.
So it's essentially the difference between telling someone "be a chef" and giving them a very specific recipe for Beef Wellington that includes where the pans are kept and what temperature the oven needs to be at every stage. One is a vibe; the other is a procedure. But what happens if the "chef" realizes they’re out of puff pastry? In the old system prompt world, the AI might just hallucinate that it found some in the back of the freezer. Does the skill structure actually prevent that kind of creative lying?
It does, because of the "Error Handling" block that is now standard in these skill definitions. In a well-defined skill, you have a section called "Failure Modes." If the agent is running a "Database Migration Skill" and the connection times out, the skill doesn't just say "try again." It provides a specific fallback loop: "If connection fails, ping the #ops-alerts channel, log the error code to the local diagnostics file, and wait for human intervention." It’s basically hard-coding the guardrails so the model doesn't have to guess what "safe" looks like in a crisis.
And I think that's why Daniel wanted us to look at this, because since late twenty-twenty-five, this isn't just an Anthropic thing anymore. We're seeing LangChain, AutoGen, and CrewAI all move toward these skill registries. But I'm curious about the mechanism here, Herman. How does the agent actually "know" it has a skill? Is it just stuffing the whole Markdown file into the context window at the start of every chat, or is there something smarter happening under the hood?
It’s definitely getting smarter. If you just stuffed every skill into the context window, you’d run out of tokens or, more likely, the model would get confused by the "lost in the middle" phenomenon where it ignores the middle of a long prompt. Most of these frameworks, like the AutoGen skill registry that launched in March of twenty-twenty-six, use a dynamic loading system. The agent has a high-level manager that looks at your request. If you say, "Hey, can you audit this smart contract for reentrancy bugs?" the manager looks at the skill library, sees a skill tagged with "blockchain-security," and then—and only then—injects that specific skill into the active context. This is what we call "just-in-time prompting." It keeps the agent focused and reduces the noise.
Which makes total sense from a performance standpoint. I mean, even as a sloth, I appreciate not having to carry around a thousand-page manual if I’m only trying to figure out how to peel a banana. But help me visualize the "Manager" model here. Is the Manager itself another LLM, or is this a classic search algorithm like RAG?
It's usually a hybrid. You have a small, fast model—like a Gemini Flash or a Llama-3-Small—acting as a router. It takes the user intent, embeds it into a vector space, and compares it against the "Skill Manifest." The manifest is basically a table of contents for all your skills. Once the router finds a high-confidence match, it pulls the full Markdown file from your local disk or a cloud bucket and appends it to the system message for the "Worker" model. This keeps the worker model from getting distracted by instructions for skills it doesn't need to use right now.
There's a distinction here that I think we need to clear up, because I've heard people mixing up agent skills with the Model Context Protocol, or MCP. You’ve been reading the specs on this—how do they play together? Is one a subset of the other, or are they totally different beasts?
They are complementary but distinct. Think of MCP as the plumbing. MCP is the standard that allows an agent to securely talk to your local files, your Slack, or your database. It provides the connection. But MCP doesn't tell the agent how to behave once it's inside your Slack. That’s the skill. To use another analogy—and I'll stick to just one—MCP is the phone line that connects you to the office, and the skill is the standard operating procedure manual sitting on the desk. You need the phone line to get there, but you need the manual to know what to do once you've arrived. For example, an MCP server might give Claude access to your Jira tickets. But a "Skill" tells Claude: "When you look at Jira, follow these five steps to identify high-priority blockers and format them into a table for the Tuesday morning standup."
Got it. So the skill is the "playbook" and MCP is the "access." That's a huge deal for reliability, right? Because one of the biggest complaints about agents in early twenty-twenty-five was that they were too "vibes-based." You'd ask them to do something, and they'd do it differently every single time. If you’re a fintech startup, you can’t have your fraud detection agent hallucinating its own criteria for what constitutes a suspicious transaction. You need it to follow the exact same check every time. I actually saw a case study recently about a startup that implemented a "Fraud Detection Skill." Instead of having ten different agents with ten different prompts, they created one master skill file. Every agent, whether it was a customer support bot or a backend auditor, called that same skill. It standardized the logic across the entire company.
That’s a perfect example. And think about the technical debt that saves. If the government changes a regulation on what constitutes a "suspicious transaction," you don't have to hunt through twenty different Python scripts or system prompts. You go to the one "Fraud Detection Skill" file, update the logic, and it propagates everywhere. It’s the "Single Source of Truth" principle applied to AI behavior. I was actually talking to a developer at a large logistics firm who said they have a skill specifically for "Warehouse Route Optimization." It’s not just code; it’s a mix of heuristic rules, specific safety constraints for the robots, and the prompt logic for the AI to handle edge cases like a spilled pallet. If they change a safety rule, they change the skill.
But how does that work in practice if you have conflicting skills? Say I have a "Concise Writing Skill" and a "Detailed Legal Compliance Skill" loaded at the same time. If I ask the agent to write a contract summary, which skill takes the wheel? Or do they fight it out in the context window?
That is the "Skill Priority" problem. Most advanced frameworks are now implementing a weight system in the frontmatter. You can assign a priority score to a skill. So, if "Legal Compliance" has a priority of ten and "Concise Writing" has a priority of five, the agent knows that if there’s a conflict, it must prioritize the legal accuracy over the brevity. It’s basically a hierarchy of constraints. Without that, you just get a very confused AI that tries to do both and ends up doing neither well.
And that's where the scaling factor comes in. When you decouple the logic from the agent, you solve the problem of "prompt drift." In the old way, if you wanted to change how your company summarizes research papers, you had to go find every single agent you'd ever built and manually update their system prompts. It was a nightmare. Now, you just update the "Research Summarization Skill" file in your central repository, and every agent that uses it is instantly updated. It's basically microservices for AI behavior. You're building a library of enterprise-grade "how-tos" that are version-controlled and auditable.
And let's not overlook the "auditable" part. If you’re in a regulated industry, you can’t just say "the AI decided to do it that way." You need to show the auditor the exact logic the AI was following. With a skill-based architecture, you can point to a specific version of a Markdown file and say, "On June 12th, the agent was using Version 2.4 of the Compliance Skill, which included these specific rules." It turns the "black box" of AI into something that looks a lot more like a traditional, auditable software system.
Okay, but let's talk about the downsides, because nothing in tech is a free lunch. If we start abstracting everything into these modular skills, don't we run the risk of making debugging even harder? If an agent fails, now I have to figure out if it was a failure of the base model, a failure of the skill orchestration, or a bug inside the Markdown file of the skill itself. It feels like we're adding layers of complexity that might bite us in the tail.
You are hitting on a very real tension. The more layers of abstraction you add, the harder it is to trace the "chain of thought." If a skill has its own set of constraints and examples, and the model is trying to balance those against the user’s immediate request, you can get these weird logic collisions. For example, if a skill says "never use external libraries" but the user says "code this using Tailwind," which one wins? We're seeing a need for new governance tools—basically linters for skills—that can catch these contradictions before they go into production. And versioning is a huge headache. If you update a skill that three different teams are using, and you accidentally change an output format that a downstream automation depends on, you’ve just broken your entire pipeline. We’re going to need something like "Semantic Versioning for Skills" very soon.
I can see the GitHub issues now: "Breaking change in v3.1 of the Sarcastic Tone Skill—my agent is now being too mean to customers." It sounds funny, but if you’re relying on these for customer-facing roles, a minor tweak in the "Skill" could have massive brand implications. Do we have "Unit Testing" for skills yet? Or are we just yolo-ing these into production?
We are actually seeing the rise of "Eval-Driven Skill Development." Before you merge a change to a skill, you run it against a suite of "Evals"—a set of fixed inputs and expected outputs. If the new version of the "Sarcastic Tone Skill" causes the agent to fail a "Politeness Check" eval, the build fails. It’s exactly like CI/CD for software, but instead of testing code, you’re testing the behavioral boundaries of a model. This is where the industry is moving—away from "it feels right" and toward "it passes the test suite."
It’s inevitable. We’re moving from "AI as a toy" to "AI as infrastructure." And when you treat something as infrastructure, you have to bring in all the boring stuff like versioning, unit testing, and documentation. But what really fascinates me is the second-order effect here: the rise of skill marketplaces. If these skill files are just Markdown, and they're becoming standardized across frameworks, what's stopping me from going to a "Skill Store" and buying a set of high-end, battle-tested skills for, say, legal discovery or medical coding?
Nothing is stopping you. In fact, it's already starting. Hugging Face has been teasing a new skill hub, and the AutoGen registry I mentioned earlier already has over twelve hundred community-contributed skills. It’s a complete shift in the economy of AI development. In twenty-twenty-four, everyone was trying to build "the best LLM." Now, everyone is realizing that the model is just the commodity engine. The real value is in the "procedural data"—the specific, hard-won knowledge of how to perform a complex task. If I spend three months perfecting a skill that can migrate a legacy COBOL database to a modern cloud architecture without losing data integrity, that skill file is worth a fortune. It doesn't matter if you use Claude, Gemini, or an open-source Llama model to run it; the "intelligence" is in the skill file itself.
That is a wild thought. The "intelligence" moving from the weights of the model into the text of the skill file. It almost makes the LLM feel like a universal interpreter. You just feed it the "Skill" and it becomes an expert in that specific domain for the duration of the task. It reminds me of that scene in The Matrix where they just download the "How to fly a helicopter" program directly into Trinity's brain. We are literally building the "I know Kung Fu" button for AI agents.
It really is that dramatic. Think about the implications for specialized knowledge. If you’re a doctor, you could create a "Medical Chart Summarization Skill" that perfectly mimics your specific style and adheres to your hospital’s exact privacy protocols. You could then "license" that skill to other doctors in your network. You aren't selling them an AI; you’re selling them your expertise, packaged in a way that an AI can execute. It’s a new way of "digitizing" human talent.
But does this mean we're moving away from "chatting" entirely? Because the whole appeal of AI was that I could just talk to it. Now it feels like I'm back to being a configuration manager, tweaking YAML files and Markdown frontmatter. Is the "magic" of AI getting lost in all this structure? I know you love your papers and your specs, Herman, but for the average person, this sounds a lot like... well, like programming again.
I think it’s a "best of both worlds" situation. The end user still gets to "chat" with the agent. The complexity is hidden under the hood. It’s like using an app on your phone—you don't see the millions of lines of code, you just see the button. Skills allow developers to build "apps" for the agentic era. You’re giving the agent a set of pre-defined boundaries so it can be more helpful and less erratic. It’s not taking away the conversation; it’s making the conversation actually productive. Instead of spending twenty prompts trying to get the AI to understand your company's specific formatting style, the developer has already "installed" that style as a skill. You just say "write the report," and it already knows the "how."
I suppose that’s the dream—removing the friction. And it’s not just limited to coding, right? I can see this being huge in creative fields or even administrative work. Imagine an "Executive Assistant Skill" that isn't just a general instructions list, but a modular set of behaviors: "Manage Calendar Conflicts," "Draft Travel Itinerary," "Summarize Board Meeting Minutes." Each one is its own little package of excellence.
And they’re self-adapting. That’s the big difference between a skill and a traditional API call. If an API call fails, the program just crashes. But if a skill is "AI-native," and the agent hits a snag—like a website being down or a file being corrupted—the agent can use its underlying reasoning to find a workaround within the constraints of the skill. It’s context-aware. If the "Travel Itinerary Skill" says "always prioritize direct flights," and there are no direct flights, the agent doesn't just give up. it explains the situation and offers the next best thing that fits the "spirit" of the skill. That’s the beauty of combining structured logic with large language models.
It’s like the difference between a train and a self-driving car. A train can only go where the tracks are—that’s your traditional software. If a tree falls on the tracks, the train stops. But a self-driving car has a destination and a set of rules, and if it hits a detour, it can navigate around it while still following the rules of the road. The "Skill" is the destination and the rules, and the LLM is the driver.
That is an excellent analogy. And it leads us to the concept of "Skill Composition." This is where it gets really exciting. You can have an agent that combines three different skills at once. It could use a "Data Analysis Skill" to crunch some numbers, a "Visual Design Skill" to create a chart, and a "Persuasive Writing Skill" to draft the executive summary. Because these are modular, the agent can switch between these "mindsets" seamlessly. It’s like having a whole team of experts in one chat box.
So, looking ahead, where does this go? If we have five thousand community-shared skills by the end of twenty-twenty-five—which is what the Claude Code stats are already suggesting—do we eventually reach a point where agents are just "skill aggregators"? Like, you don't even choose a model anymore, you just choose a "Skill Bundle" and the framework handles the rest?
I think we'll see the rise of "skill orchestration engines." Right now, the agent has to manually decide which skill to load. Soon, we'll have models that are specifically trained to be "Skill Orchestrators." Their entire job will be to understand a complex human goal, break it down into a sequence of skills, and then execute those skills with surgical precision. It’s moving from being a "chatbot" to being an "operating system." In fact, you could argue that Claude Code is the first draft of an AI-native operating system where "Skills" are the executable programs.
It’s a big shift from the "one model to rule them all" philosophy. It’s much more decentralized. And it feels very pro-developer. It gives us a way to contribute to the AI ecosystem without needing a hundred million dollars to train a foundation model. I can write a killer "Documentation Auditor Skill" tonight and have it running on thousands of agents tomorrow. That’s an exciting level of accessibility.
It really is. And for our listeners who are building with these tools, the practical takeaway here is to stop thinking about your system prompt as one big monolith. If you have an agent that's doing three or four different things, break those things out. Look at your agent’s workflow and identify the repetitive, high-stakes tasks. Package those as skills. Give them clear input and output specs, add a few "golden examples" of what a perfect execution looks like, and put them in a version-controlled folder. Even if you aren't using Claude Code specifically, you can adopt the "Skills Pattern" in LangChain or just by using a modular prompting strategy. It will save you so much pain when it comes time to iterate or scale.
And treat those skill files like code. I know they’re just text, but they are the "logic" of your system. Use Git, use pull requests, and for the love of all that is holy, don't change a production skill on a Friday afternoon. Sloth-approved advice right there. Slow and steady wins the race, especially when you're dealing with modular AI logic.
I love that. "Treat your prompts like code" has been a mantra for a while, but with skills, it finally becomes a reality. You actually have a file format and a directory structure to point to. It’s not just an abstract idea anymore. It’s an engineering discipline.
I’m curious, though—does this mean we’ll see "Skill Debt" in the same way we see "Technical Debt"? Like, a company has five hundred different skills, half of them are redundant, and no one knows which one is the current version of the "Email Signature Skill"?
Oh, absolutely. We are already seeing "Skill Rot." A skill that worked perfectly on GPT-4 might behave slightly differently on GPT-5 or Claude 3.5. If the skill relies on a specific model’s quirks to work, it’s fragile. That’s why the best skills are model-agnostic. They focus on clear logic and structured data rather than "prompt magic." But yes, Skill Management is going to be a massive new job category. We’ll have "AI Skill Architects" whose entire job is to prune and optimize the corporate skill library.
Well, this has been a fascinating deep dive. I came into this thinking "agent skills" was just another buzzword, but I'm leaving convinced it's the actual architectural future of how we interact with these models. It’s the bridge between the "magic" of LLMs and the "reliability" of traditional software.
It’s the maturity phase of the AI revolution. We’re moving past the "wow, look what it can say" phase and into the "look what it can consistently do" phase. And that’s where the real transformation happens.
Before we wrap up, I think we should leave the listeners with an open question. As these skills proliferate and we get these massive marketplaces, will we see a fragmentation of the ecosystem? Will "Anthropic Skills" only work on Claude, or will the community force a standard that makes a skill truly portably across any model? If I write a skill for Gemini three Flash, can I run it on a small, local Llama model in twenty-twenty-seven? That’s where the real power move is—true portability of intelligence.
That is the multi-billion dollar question. If we can get a "Universal Skill Standard," the pace of innovation will go vertical. Right now, it's a bit like the early days of the web where you had to code differently for Netscape and Internet Explorer. We need a "W3C for AI Skills" to make sure that a "Research Skill" written in Berlin works just as well on a server in Tokyo, regardless of which model is powering it.
Well, I’ll be over here slowly pondering that. Thanks as always to our producer Hilbert Flumingtop for keeping the gears turning behind the scenes. And a big thanks to Modal for providing the GPU credits that power the generation of this show—they make the heavy lifting look easy. This has been My Weird Prompts. If you found this useful, do us a solid and leave a review on your podcast app. It genuinely helps us get these deep dives in front of more people who are trying to make sense of this wild AI landscape.
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