Claude Skills vs. MCP: A Tale of Two AI Customization Philosophies

In the rapidly evolving landscape of artificial intelligence, the ability to customize and extend the capabilities of large language models (LLMs) has become a critical frontier. Anthropic, a leading AI research company, has introduced two powerful but distinct approaches to this challenge: Claude Skills and the Model Context Protocol (MCP). While both aim to make AI more useful and integrated into our workflows, they operate on fundamentally different principles. This post delves into a detailed comparison of Claude Skills and MCP, explores whether they can or should be merged, and discusses the exciting future of AI customization they represent.

What are Claude Skills? The Power of Procedural Knowledge

Claude Skills, also known as Agent Skills, are a revolutionary way to teach Claude how to perform specific tasks in a repeatable and customized manner. At its core, a Skill is a folder containing a SKILL.md file, which includes instructions, resources, and even executable code. Think of Skills as a set of standard operating procedures for the AI. For example, a Skill could instruct Claude on how to format a weekly report, adhere to a company’s brand guidelines, or analyze data using a specific methodology.

The genius of Claude Skills lies in their architecture, which is built on a principle called progressive disclosure. This three-tiered system ensures that Claude’s context window isn’t overwhelmed with information:

1. Level 1: Metadata: When a session starts, Claude loads only the name and description of each available Skill. This is a very lightweight process, consuming only a few tokens per Skill.

2. Level 2: The SKILL.md file: If Claude determines that a Skill is relevant to the user’s request, it then loads the full content of the SKILL.md file.

3. Level 3 and beyond: Additional resources: If the SKILL.md file references other documents or scripts within the Skill’s folder, Claude will load them only when needed.

This efficient, just-in-time loading mechanism allows for a vast library of Skills to be available without sacrificing performance. Skills are also portable, working across Claude.ai, Claude Code, and the API, and can even include executable code for deterministic and reliable operations.

What is the Model Context Protocol (MCP)? The Universal Connector

The Model Context Protocol (MCP) is an open-source standard designed to connect AI applications to external systems. If Claude Skills are about teaching the AI how to do something, MCP is about giving it access to what it needs to do it. MCP acts as a universal connector, similar to a USB-C port for AI, allowing models like Claude to interact with a wide range of data sources, tools, and workflows.

MCP operates on a client-server architecture:

• MCP Host: The AI application (e.g., Claude) that manages connections to various external systems.

• MCP Client: A component within the host that maintains a one-to-one connection with an MCP server.

• MCP Server: A program that exposes tools, resources, and prompts from an external system to the AI.

This architecture allows an AI to connect to multiple external systems simultaneously, from local files and databases to remote services like GitHub, Slack, or a company’s internal APIs. MCP is built on a two-layer architecture, with a data layer based on JSON-RPC 2.0 and a transport layer that supports both local and remote connections.

The Core Difference: Methodology vs. Connectivity

The fundamental distinction between Claude Skills and MCP can be summarized as methodology versus connectivity. MCP provides the AI with access to tools and data, while Skills provide the instructions on how to use them effectively. According to Anthropic’s own documentation:

“MCP connects Claude to external services and data sources. Skills provide procedural knowledge—instructions for how to complete specific tasks or workflows. You can use both together: MCP connections give Claude access to tools, while Skills teach Claude how to use those tools effectively.”

This highlights that Skills and MCP are not competing technologies but are, in fact, complementary. An apt analogy is that of a master chef. MCP provides the chef with a fully stocked pantry of ingredients and a set of high-end kitchen appliances (the what). Skills, on the other hand, are the chef’s personal recipe book and techniques, guiding them on how to combine the ingredients and use the appliances to create a culinary masterpiece.

Can a Claude Skill be an MCP? And Should They Be Merged?

Given that both are Anthropic’s creations, a natural question arises: could a Claude Skill be implemented as an MCP, or should the two be merged into a single, unified system? While technically possible to create an MCP server that exposes Skills, it would be architecturally inefficient and would defeat the purpose of both systems.

Exposing Skills through MCP would negate the benefits of progressive disclosure, as it would introduce the overhead of the MCP protocol for what should be a simple filesystem read. It would also create a redundant abstraction layer, as Skills already require a local code execution environment. The two systems are designed for different purposes and have different optimization goals: Skills for context efficiency within Claude, and MCP for standardized integration across different AI systems.

Therefore, Claude Skills and MCP should be treated as independent, complementary technologies. The most powerful workflows will come from using them in synergy.

The Power of Synergy: Using Skills and MCP Together

The true potential of these technologies is unlocked when they are used in concert. Here are a few integration patterns that showcase their combined power:

• Skills as MCP Orchestrators: A Skill can contain a complex workflow that orchestrates calls to multiple MCP servers. For example, a “Deploy and Notify” Skill could contain a deployment checklist, notification templates, and rollback procedures. It would then use MCP to access GitHub for code, a CI/CD server for deployment, and Slack for notifications.

• Skills for MCP Configuration: An organization can create Skills that teach Claude its specific standards for using MCP tools. For example, a “GitHub Workflow Standards” Skill could contain instructions on branch naming conventions, pull request review checklists, and commit message templates, ensuring that Claude uses the GitHub MCP server in a way that aligns with the company’s best practices.

• Hybrid Skills: A Skill can contain embedded code that makes calls to an MCP server. This is useful for self-contained workflows that need to fetch external data.

The Future: A Marketplace for Skills and an Ecosystem for MCP

The future of AI customization will likely see the development of a vibrant Skills Marketplace. Similar to the app stores for our smartphones or the extension marketplaces for our code editors, a Skills Marketplace would allow developers to publish, share, and even sell Skills. This could create a new economy around AI expertise, with a wide range of Skills available, from free, community-contributed Skills to premium, industry-specific Skill packages for domains like law, medicine, or finance.

Simultaneously, the MCP ecosystem will continue to grow, with more and more tools and services exposing their functionality through MCP servers. This will create a virtuous cycle: as more tools become available through MCP, the demand for Skills that can effectively use those tools will increase.

Conclusion

Claude Skills and the Model Context Protocol represent two distinct but complementary philosophies of AI customization. MCP is the universal connector, providing the what—the access to tools and data. Skills are the procedural knowledge, providing the how—the instructions and methodology. They are not competitors but partners in the quest to create more powerful, personalized, and integrated AI assistants. The future of AI workflows will not be about choosing between Skills or MCP, but about leveraging the power of Skills and MCP to create intelligent systems that are truly tailored to our needs.

References:

[1] Anthropic. (2025, October 16). Claude Skills: Customize AI for your workflows. Anthropic.

[2] Anthropic. (2025, October 16). Equipping agents for the real world with Agent Skills. Anthropic.

[3] Model Context Protocol. (n.d.). What is the Model Context Protocol (MCP)? Model Context Protocol.

[4] Model Context Protocol. (n.d.). Architecture overview. Model Context Protocol.

[5] Willison, S. (2025, October 16). Claude Skills are awesome, maybe a bigger deal than MCP. Simon Willison’s Weblog.

[6] Claude Help Center. (n.d.). What are Skills? Claude Help Center.

[7] IntuitionLabs. (2025, October 27). Claude Skills vs. MCP: A Technical Comparison for AI Workflows. IntuitionLabs.

@article{nagabhushanaradhya2025claudeskills,
  author  = {Nagabhushanaradhya, Subramanya},
  title   = {Claude Skills vs. MCP: A Tale of Two AI Customization Philosophies},
  journal = {subramanya.ai},
  year    = {2025},
  month   = {October},
  url     = {https://subramanya.ai/2025/10/30/claude-skills-vs-mcp-a-tale-of-two-ai-customization-philosophies/}
}