Remember when teaching your computer new tricks meant writing thousands of lines of code, wrestling with API documentation, and probably crying into your coffee at 2 AM? Those days aren't completely gone, but they just got a whole lot easier.

Anthropic just dropped something that's changing how we think about AI capabilities: Claude Skills. And if you think that sounds simple, you're right. That's exactly the point.

At their core, Skills are ridiculously straightforward: they're just folders. Inside these folders, you've got a markdown file with some instructions, maybe a few scripts, and whatever resources Claude might need to tackle a specific task.

Think of Skills as those detailed instruction manuals your team creates for onboarding new employees, except instead of getting buried in some forgotten Notion page, Claude actually reads them and follows them to the letter. Every. Single. Time.

The beauty is in how Claude handles them. When you fire up a session, Claude doesn't load every single skill into memory like some overstuffed backpack. Instead, it scans the metadata—just a name and description—from each skill. This takes up maybe a few dozen tokens per skill. Then, when you ask Claude to do something that needs a particular skill, it loads just that one, pulling in only what's needed for the job at hand.

This approach, which Anthropic calls "progressive disclosure," means you can have dozens or even hundreds of skills available without drowning Claude's context window. It's like having an entire library at your fingertips, but only opening the books you actually need.

Here's where it gets interesting. Skills tap into something that coding agents already have: access to a filesystem and the ability to run commands. Claude Code, Cursor, and similar tools have been working this way for a while now, but Skills formalize the pattern and make it repeatable.

A basic skill looks something like this:

---
name: my-awesome-skill
description: Does something incredibly useful
---

# Instructions go here

Claude, when you need to do X, follow these steps:
1. First, do this thing
2. Then, do that thing
3. Finally, wrap it up like this

## Optional scripts or resources can live in the same folder

But Skills can get much more sophisticated. Anthropic's own document creation abilities—the ones that let Claude whip up Excel spreadsheets, Word docs, PowerPoint presentations, and PDFs—are entirely built using skills. Those skills include Python scripts for handling binary file formats, validation functions, and detailed instructions on formatting and structure.

The slack-gif-creator skill that Anthropic released includes validation to ensure GIFs meet Slack's 2MB size limit. If Claude creates a GIF that's too large, it knows to try again with different settings. It's this kind of procedural intelligence that makes Skills so powerful.

One of the most exciting things about Skills is how accessible they are. You don't need to be a developer to create simple ones—just write markdown. Have a specific way your team formats reports? That's a skill. Follow certain brand guidelines for presentations? Another skill. Need to structure meeting notes in a particular way? You guessed it.

For more complex needs, you can attach executable scripts. But even then, you're just writing normal Python, JavaScript, or bash scripts—nothing exotic or proprietary.

And if you're not sure where to start, Anthropic created a "skill-creator" skill that guides you through the whole process interactively. Claude asks about your workflow, builds out the folder structure, formats the files, and bundles everything together. It's like having a personal assistant who helps you create personal assistants.

You can also check out the repository where Anthropic and the community are sharing example skills covering everything from algorithmic art to web app testing. Want to learn how to build skills? Just look at how others have done it. The format is simple enough that reading a few examples will get you up to speed quickly.

If you've been following AI developments, you might be wondering how Skills compare to other approaches like the Model Context Protocol (MCP) or just using detailed prompts. Understanding these differences reveals a lot about why Skills matter and when to use each approach.

Prompting is what most of us start with—writing detailed instructions directly in your conversation with Claude. You might craft a mega-prompt that explains your company's style guide, outlines a specific workflow, or provides context about a project. This works, but it has limitations:

• You have to include the same instructions every time you start a new chat

• Long prompts consume significant context window space from the start

• Instructions can get lost or diluted in longer conversations

• There's no easy way to share prompts across your team in a standardized way

• You're trading valuable context space for instructions, leaving less room for actual work

MCP (Model Context Protocol) is a comprehensive protocol specification covering hosts, clients, servers, resources, prompts, tools, sampling, and multiple transport mechanisms. It's designed to standardize how AI models connect to external services and data sources. Think of MCP as plumbing—important infrastructure that lets Claude talk to your calendar, email, databases, and other external systems.

Skills, on the other hand, are about procedural knowledge. They teach Claude how to do specific tasks or workflows. While MCP gives Claude access to tools, Skills teach Claude how to use those tools effectively. And unlike prompts, Skills load dynamically only when needed, keeping your context window clean.

Here's a practical example showing how all three work together:

• A prompt might say: "Help me analyze this sales data"

• An MCP connection gives Claude access to your company's database

• A skill teaches Claude your company's specific data analysis methodology, how to structure the report, which visualizations to use, and how to format insights according to your team's standards

The token efficiency difference is striking too. A detailed prompt for complex tasks might consume 2,000-5,000 tokens every single time. MCP implementations, particularly complex ones like GitHub's, can consume tens of thousands of context tokens just from loading available tools. Skills use a fraction of that through progressive disclosure—only loading what's needed when it's needed.

This simplicity is by design. Skills are essentially markdown files with a bit of YAML metadata—lightweight, readable, and close to how we naturally explain tasks to each other. This makes them incredibly easy to create, share, and understand, which is why they're generating so much excitement in the developer community.

One of the most powerful aspects of Skills is their composability. Claude can automatically identify which skills are needed for a task and coordinate their use. Need to fetch data from an API, process it with custom business logic, generate visualizations, and format everything into a branded report? Claude can chain together multiple skills to make it happen.

This composability extends beyond just using multiple skills in sequence. Skills can reference other skills, creating layers of specialized knowledge. You might have a base skill for data analysis that other, more specific skills build upon. Or a brand guidelines skill that document creation skills automatically incorporate.

The possibilities for Skills are genuinely mind-bending. Here are some directions that are already emerging or seem ripe for exploration:

Domain-Specific Agents: Imagine a data journalism skill set that teaches Claude where to find census data, how to load it into databases, how to identify interesting stories, and how to create clean visualizations. Or a legal research agent with skills for citation formats, case law analysis, and document summarization following specific jurisdictional requirements.

Company Knowledge Bases: Skills that encode your organization's specific processes—how you structure sprint retrospectives, format technical specifications, or handle customer communications. This transforms Claude from a generic assistant into one that thinks like your team.

Development Workflows: Skills for building specific types of applications, following architectural patterns, implementing security best practices, or adhering to code style guides. Some developers are already creating skills for building Datasette plugins or MCP servers.

Creative Toolkits: Skills for generative art with specific algorithms, music composition following certain rules, or design systems that maintain consistent aesthetics across outputs.

Educational Modules: Skills that teach Claude domain-specific concepts so it can help learners more effectively. A chemistry skill might include molecular visualization tools and problem-solving frameworks. A language learning skill could incorporate spaced repetition algorithms and grammatical correction patterns.

Quality Assurance Frameworks: Skills for testing web applications, validating data pipelines, or checking code against security standards. These could include not just instructions but also testing scripts and validation tools.

The community is already building and sharing skills at a rapid pace. The anthropics/skills repository on GitHub is growing daily, and we're likely to see a Cambrian explosion of specialized skills as more people realize how easy they are to create and share.

What makes Skills particularly exciting is their portability. Because they're just files and folders with a simple format, they work across different contexts—Claude.ai, Claude Code, the API. Create a skill once, use it everywhere.

And here's something even wilder: Skills aren't tied to Claude. The format is so straightforward that other AI models can use them too. Point a different AI tool at a skills folder and tell it to read the instructions, and it'll work. No proprietary lock-in, no complex dependencies.

Anthropic has hinted at even more ambitious plans. They're exploring how Skills could help agents create their own skills, letting AI codify its learned behaviors into reusable capabilities. Imagine Claude learning from how you work and automatically generating skills that capture those patterns.

There's also the question of how Skills will integrate with enterprise deployments. Anthropic is working on simplified creation workflows and organization-wide deployment capabilities, making it easier for companies to distribute skills across teams while maintaining security and governance.

If you're on Claude's Pro, Max, Team, or Enterprise plan, you can start using and creating Skills right now. Head to your settings, enable the feature (it requires the code execution tool to be enabled), and you're off to the races.

For the technically inclined, check out Anthropic's documentation or dive into the Claude Skills Cookbook on GitHub. If you're less technical, start simple: create a markdown file with instructions for a repetitive task you do often, add the required YAML frontmatter, and see what Claude does with it.

The barrier to entry is refreshingly low. You don't need to learn a new programming language, set up servers, or wrestle with API credentials. Just write down how you want something done, and Claude will follow your instructions.

Skills represent a fundamental shift in how we think about AI capabilities. Instead of waiting for model providers to bake every possible feature into their systems, users can extend capabilities themselves. Instead of prompt engineering on the fly for every task, you can codify best practices once and reuse them reliably.

We're moving from AI as a tool you have to constantly guide to AI as a platform you can teach. From one-off interactions to repeatable workflows. From generalist responses to specialist precision.

And the simplicity of the approach means this isn't just for developers. Domain experts—marketers, analysts, designers, researchers—can create skills that capture their specialized knowledge without writing a single line of code in many cases.

The AI agent revolution everyone's been talking about might not come from increasingly complex systems and protocols. It might come from folders full of markdown files that anyone can create, share, and remix. Sometimes the best solutions really are the simplest ones.