Claude Skills: Letting AI Autonomously 'Hire Experts' Based on Context

In October 2025, Anthropic dropped another bombshell: Claude Skills (Agent Skills) officially launched. You might think: “Just another UI tweak, right?” Not this time. Veteran developers believe Skills might be a bigger deal than MCP.

Why does this matter? Because Skills solves a core problem that’s long plagued AI applications:

As a data scientist who relies on Claude daily for work and life, when I first saw the Skills design, my immediate thought was: “This is brilliantly time-saving, effort-saving, and cost-saving!” In the past, I had to repeatedly explain in every new conversation: “Please use Python’s plotnine instead of R’s ggplot,” “Please follow our team’s code style guidelines.” Now all of this can be packaged into Skills and automatically invoked by Claude.

Skills are modular packages containing instructions, scripts, and resources that enable AI to dynamically load specialized knowledge. At its core is the Progressive Disclosure mechanism: Claude only invokes expert skills when needed, preventing context window overload. It’s like hiring consultants on demand rather than cramming every expert into the same conference room at astronomical cost. More importantly, it fixes MCP’s flaw of consuming tens of thousands of tokens at startup, making AI toolchains truly scalable and flexible.

This article explains why Claude Skills is groundbreaking and compares it with other popular Claude features so you won’t get confused!

What Are Claude Skills?

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Imagine you’re a CEO with financial experts, legal advisors, marketing gurus, and specialists across various domains. You wouldn’t demand that everyone report their entire expertise to you every morning. You only need to know: who’s good at what, then call the right person when problems arise.

Claude Skills works exactly this way. It’s a folder containing instructions, scripts, and resources that grants Claude AI domain-specific expertise. In reality, a standard Skill requires just:

• A SKILL.md file (see explanation below)
• Subfolders (optional): scripts/ (stores executable code like Python/JavaScript) and resources/ (stores templates or reference documents)
• (You can study Claude Skills architecture in the official open-source project)

This design is simple yet powerful. Anthropic compares Skills to onboarding materials or standard operating procedures (SOPs) for Claude as a “new employee.” With proper training, future task execution yields higher-quality results.

Why are Claude Skills important? They solve LLM’s fatal problem: context window overload. In other words, when there’s too much information, AI models can’t remember everything!

Previously, if you wanted Claude to master 20 specialized domains, you’d stuff all knowledge into system prompts, consuming tens of thousands of tokens. Worse, even if you only needed one domain, the other 19 would still occupy precious memory. Now, Skills let Claude “hire” corresponding experts only when needed (cost-effective and efficient).

From a developer’s perspective, this means we can finally modularize organizational procedural knowledge (knowledge about “how to do tasks well”) and workflows, reusing them across teams and projects. This is not only “giving AI more information” hoping it becomes an expert but also “teaching AI how to collaborate with experts.”

Three Major Strengths of Claude Skills

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Let’s examine what makes Claude Skills powerful. Quoting a Reddit discussion, its strengths can be summarized as 3A: “Automatically, Anytime, Anywhere.”

1. Automatically: Making AI an Expert in Task Automation

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Skills’ greatest value is enabling AI to complete tasks in repeatable, standardized ways, which is extremely helpful for both enterprises and individual workers.

Enterprise Use Cases:

• Brand Guidelines: Create a brand guideline Skill ensuring all external documents follow company visual design, instantly applying Anthropic’s color codes, font choices, and tone (official example Skill)
• Financial Report Formatting: Automatically apply fixed structures and chart styles when generating quarterly investor presentations, ensuring data presentation consistency
• Code Review Process: Have Claude check Pull Requests according to team standards, enforcing test coverage, naming conventions, etc.

Personal Work Applications:

• Data Analysis Framework: Package your habitual exploratory data analysis (EDA) workflow into a Skill, automatically applying the same checks to new datasets, including detecting outliers, imputing missing values, etc. (official example Skill)
• Content Creation Templates: If you run a blog, create a “technical article structure” Skill ensuring every article includes introduction, core arguments, examples, conclusion, etc.

Even better, Skills can contain executable code. This is a significant innovation! For example, Anthropic’s built-in PDF Skill includes pre-written Python scripts for extracting form fields or processing Excel formulas. These deterministic operations are more efficient and reliable than having LLMs generate prompts for such tasks.

Why is executable code in Skills important? Because many tasks require deterministic reliability. A practical example: sorting a table with 10,000 rows. Executing traditional code is far faster, more accurate, and more cost-effective than having an LLM reason through the process step-by-step. After all, using AI for such simple tasks introduces unnecessary risk of “AI choosing wrong execution methods.” It’s better to explicitly tell AI to use the sort() function. The key insight: When Claude executes scripts, the code itself doesn’t load into the context window. Only output results consume tokens. This means you can include complex utility scripts in Skills without worrying about memory explosion.

2. Anytime: Experts On-Demand, Without Occupying Resident Memory

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This is Skills’ smartest design: Progressive Disclosure.

Back to the CEO analogy: managing 50 experts doesn’t mean demanding detailed reports from everyone every morning. You only need a directory listing each person’s name and expertise summary. When handling a merger, you call the legal expert into the meeting room for detailed explanation. After the task ends, the legal expert leaves. Your desk isn’t permanently occupied by their documents.

Claude Skills operates exactly this way, with three-level progressive loading:

Level 1 (Metadata): Lightweight Index
At startup, Claude only loads a “directory” (names and brief descriptions) of all Skills, with each consuming approximately 100 tokens or less. Like browsing an employee roster seeing only “Mike: Financial Analysis Expert,” “Lynn: Brand Design Consultant.” Since it’s just flipping through a directory, you don’t immediately pay hefty salaries (tokens).

Level 2 (Instructions): Load Complete Instructions On-Demand
When Claude determines a Skill is relevant to the current task, it reads the Skill’s main definition (SKILL.md) from the file system into the context window. This is a model-invoked mechanism. Claude autonomously decides based on your request content and Skill descriptions through semantic matching whether to activate Skills, i.e., AI independently decides whether to “hire experts.”

Level 3+ (Resources): Deep Resources Accessed Only During Execution
Claude Skills folders can contain additional scripts, reference documents, and extensions to the Skill’s capabilities. These deep resources are only executed or accessed when needed (unused content doesn’t consume tokens). Therefore, Skills can include virtually unlimited reference materials. For instance, I could add 1,000 statistics textbooks to a “Data Science Best Practices” Skill. When Claude determines ARIMA analysis is needed, Claude Skills would only read 1 time-series textbook, not waste token costs reading the other 999 books.

I want to specifically clarify the difference between Skills and Claude Projects (equivalent to ChatGPT’s GPTs and Gemini’s Gems), because I use Claude Projects almost daily.

Projects’ context is “static”, meaning, opening a new conversation loads “all” related data and prompts. Those two quotes highlight key differences: Projects are like calling all employees into a meeting room even though you only need one expert’s proposal. And that room will definitely be overcrowded. Skills are dynamically activated. AI autonomously determines when to use which skill.

(Another important distinction: Projects are limited to Claude.ai web or desktop applications, detailed in the next Anywhere section)

Claude Skills’ “on-demand expert” characteristic led Django founder Simon to believe Skills fixes MCP’s token consumption problem. Some MCP services consume tens of thousands of tokens at startup by expanding all tool information at once (like cramming all employees into one conference room as mentioned above). Skills reduces this cost to dozens, only expanding complete information when needed.

Why is this the solution for AI ecosystems? Current computing power, though strong and continuously growing, isn’t infinite. LLM context windows have limits. We can’t stuff a billion books into AI expecting it to become a billion kinds of experts. Progressive Disclosure offers an alternative AI growth path: not “infinitely expanding memory,” but “intelligently using memory.” This is the more sustainable AI application architecture.

3. Anywhere: Create Once, Use Everywhere

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Claude Skills offers complete portability, letting you use Skills everywhere:

• Claude.ai web version / desktop application
• Claude Code (terminal tool)
• Claude API (developer platform)
• Claude Agent SDK (software development kit)

Create a Skill once and reuse it across all workflows. Use it for daily conversations on Claude.ai, for coding in Claude Code, integrate it into your automation systems via API. Completely seamless.

“Everything is a File”

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“Everything is a file” comes from UNIX’s design philosophy (see detailed interpretation). Claude Skills embraces this simple yet flexible approach: whatever “skill” you want to create, you don’t need APIs or worry about platform integration. You just prepare files, including prompts, reference materials, code scripts, etc.

Using Skills in Claude Code offers even greater workflow advantages. You can store Skills in the project directory’s .claude/skills/, sharing them with teams via Git, which is as simple as sharing code. This is fantastic for development teams: package “team code review standards,” “API design principles,” “unit testing strategies” into Skills, manage them through version control, ensuring all members’ (AI assistants) follow the same best practices.

I anticipate a future “Skills ecosystem,” similar to VSCode’s Extension Marketplace, where developers can share and download Skills across various domains.

How Claude Skills Differs from Other Features

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This section is precisely why I wrote this article: “How do Skills differ from Projects, custom slash commands, and other features I’m already using?”

Here’s a key comparison between Claude Skills and other popular Claude features:

The last row particularly deserves mention. Some view Skills as MCP’s natural evolution (“MCP 2.0”, if you will) because it provides the procedural knowledge MCP lacks: not just connecting tools, but teaching AI how to use tools correctly. For example, you could create a “Gmail Management” Skill that not only connects Gmail via MCP but includes procedural guidance like “how to write professional replies,” “how to prioritize emails.”

Understanding this comparison table reveals that Skills doesn’t conflict with other features. Skills can incorporate Sub-agents and MCP servers. They can work synergistically, building more complex AI workflows. This composability is one of Skills’ core design advantages.

How to Use Claude Skills

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This article aims to spark your interest in Claude Skills. For detailed operational instructions, I recommend reading the official Anthropic documentation. Below, I’ll introduce two methods to get you started quickly:

Built-in Claude Skills

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We’re lazy. Defaults are naturally the easiest to use (˚∀˚)

Anthropic officially provides pre-built skills for Excel, PowerPoint, PDF, and Word (detailed skill descriptions). After activating your Claude AI, they’re automatically available in conversations without any installation steps. In fact, Anthropic has entirely implemented Claude’s document and file creation capabilities (like generating Excel reports, editing PDFs) through Skills. This means Claude itself uses Skills to build Claude’s functionality! This proves Skills architecture’s power and universality.

Not enough pre-built skills? Anthropic has hidden more pre-built Skills in Claude AI’s settings page for you to try. Just go and enable them! Even better, the team open-sourced these Skills. You can see how the official team designed these useful Skills in this GitHub project, giving you more inspiration for writing your own Skills!

Scroll down a bit further in that settings page, and you’ll even see a skill-creator skill that lets you interactively build Skills through conversation without manually editing files. This is a blessing for non-technical users!

If you’re a technical person comfortable with Markdown and YAML formats, refer to the explanation below to start writing SKILL.md files for creating new skills.

SKILL.md Basic Structure

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To have custom Skills, simply create a new folder with at least one SKILL.md file. This markdown file’s content can be thought of as two sections:

• Header: YAML front matter metadata, including name and description
• Content: Any prompt content detailing this skill’s capabilities

Here’s a minimalist “output formatting” Skill example:

---
name: Minimalist Example Skill
description: This skill provides a standardized output format. Activate this skill when users request standardized output or fixed formats.
---

# Skill Operation Instructions

## Instructions
Please output final results in the following Markdown format:
*   **Title:** Use H1 tag
*   **Summary:** Use bullet points
*   **Body:** Keep concise and avoid redundancy.

## Examples
[Provide specific input and expected output examples here]

SKILL.md can be sophisticated and powerful, but getting started is super simple.

Conclusion

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My motivation for writing this article was simply wondering “How exactly do Skills differ from other Claude features?” After researching and combining my personal Claude usage experience, I believe Skills represents a major leap in AI tool design. It no longer forces us to choose between a “single-function lightweight assistant” (like Claude Code’s custom slash commands) and a “fully-featured but memory-exploding heavyweight monster” (like an excessively long CLAUDE.md). Instead, it provides a third path: hiring experts based on contextual needs.

This design is remarkably human-centered, mimicking how we manage knowledge and expertise in the real world. I don’t need to memorize everyone’s complete expertise. I just need to know who can help solve what problems. This Progressive Disclosure philosophy embodies the wisdom of professional division of labor in human society.

From a technical implementation perspective, Skills solves LLM applications’ core bottleneck: context management. Previously, we always struggled between “giving AI more knowledge” and “keeping context clean.” Now the Progressive Disclosure mechanism provides an elegant solution. It’s a fundamental breakthrough in LLM architecture design.

I anticipate other AI products will soon introduce mechanisms similar to Claude Skills. Because computing power is ultimately limited, “intelligently using memory” is more practical than “infinitely expanding memory.” The future AI application battlefield won’t necessarily be an endless race to see whose model has the largest context window, but who can most efficiently manage knowledge dynamically.

Next, I plan to create several Skills for my data science workflows, including “A/B Testing Analysis Framework,” “Causal Inference Hypothesis Checklist,” and more. If you’re also a Claude user, consider this: what repeatable professional procedures in your work deserve packaging into Skills?

Tools have evolved, and our work methods must evolve accordingly. Claude Skills makes this evolution remarkably smooth.

References:

• Simon Willison: Claude Skills are awesome, maybe a bigger deal than MCP
• Anthropic: Equipping agents for the real world with Agent Skills
• Anthropic: Claude Skills Documentation
• Anthropic: Introducing Agent Skills
• Reddit Discussions: Confused by Skills vs MCP vs Tools?; What are Claude Skills really?