Claude Skills vs. MCP: Complementary Philosophies for AI Customization (subramanya.ai)

Anthropic has clarified two complementary approaches for customizing LLM behavior: Claude Skills and the open-source Model Context Protocol (MCP). Claude Skills are filesystem-based “agent skills” (a folder with a SKILL.md plus optional code/resources) that encode procedural knowledge and workflows. They use progressive disclosure—Level 1 loads lightweight metadata, Level 2 loads the SKILL.md when relevant, and Level 3 fetches referenced assets only as needed—giving high token efficiency and predictable, repeatable behavior across Claude.ai, Claude Code, and the API. Skills can include executable code for deterministic operations and enforce organization-specific methods (e.g., report formats, code review checklists). MCP is a client–server integration standard that exposes external tools and data to models: an MCP Host runs the AI, an MCP Client maintains connections, and MCP Servers expose APIs/tools (transport layer supports local/remote; data layer uses JSON-RPC 2.0). MCP provides the “what” (access to GitHub, Slack, databases, etc.), while Skills provide the “how” (procedures and best practices). Anthropic argues they shouldn’t be merged—wrapping Skills in MCP would break progressive disclosure and add unnecessary overhead—but they’re synergistic: Skills can orchestrate MCP servers, teach Claude how to use MCP tools, or embed hybrid code that calls MCP. The roadmap points to a Skills marketplace plus broader MCP adoption, creating a virtuous cycle of interoperable tools and domain-specific procedural expertise.