Agent Skills: The Missing Piece of the Enterprise AI Puzzle

The enterprise AI landscape is at a critical juncture. We have powerful general-purpose models and a growing ecosystem of tools. But we are missing a crucial piece of the puzzle: a standardized, portable way to equip agents with the procedural knowledge and organizational context they need to perform real work. On December 18, 2025, Anthropic took a major step towards solving this problem by releasing Agent Skills as an open standard [1]. This move, following the same playbook that made the Model Context Protocol (MCP) an industry-wide success, is not just another feature release—it is a fundamental shift in how we will build and manage agentic workforces.

The Problem: General Intelligence Isn’t Enough

General-purpose agents like Claude are incredibly capable, but they lack the specialized expertise required for most enterprise tasks. As Anthropic puts it, “real work requires procedural knowledge and organizational context” [2]. An agent might know what a pull request is, but it doesn’t know your company’s specific code review process. It might understand financial concepts, but it doesn’t know your team’s quarterly reporting workflow. This gap between general intelligence and specialized execution is the primary barrier to scaling agentic AI in the enterprise.

Until now, the solution has been to build fragmented, custom-designed agents for each use case. This creates a landscape of “shadow AI”—siloed, unmanageable, and impossible to govern. What we need is a way to make expertise composable, portable, and discoverable. This is exactly what Agent Skills are designed to do.

The Solution: Codified Expertise as a Standard

At its core, an Agent Skill is a directory containing a SKILL.md file and optional subdirectories for scripts, references, and assets. It is, as Anthropic describes it, “an onboarding guide for a new hire” [2]. The SKILL.md file contains instructions, examples, and best practices that teach an agent how to perform a specific task. The key innovation is progressive disclosure, a three-level system for managing context efficiently:

1. Metadata: At startup, the agent loads only the name and description of each installed skill. This provides just enough information for the agent to know when a skill might be relevant, without flooding its context window.
2. Instructions: When a skill is triggered, the agent loads the full SKILL.md body. This gives the agent the core instructions it needs to perform the task.
3. Resources: If the task requires more detail, the agent can dynamically load additional files from the skill’s scripts/, references/, or assets/ directories. This allows skills to contain a virtually unbounded amount of context, loaded only as needed.

This architecture is both simple and profound. It allows us to package complex procedural knowledge into a standardized, shareable format. It solves the context window problem by making context dynamic and on-demand. And by making it an open standard, Anthropic is ensuring that this expertise is portable across any compliant agent platform.

Skills vs. MCP: The Brain and the Plumbing

It is crucial to understand how Agent Skills relate to the Model Context Protocol (MCP). They are not competing standards; they are complementary layers of the agentic stack. As Simon Willison aptly puts it, “MCP provides the ‘plumbing’ for tool access, while agent skills provide the ‘brain’ or procedural memory for how to use those tools effectively” [3].

• MCP tells an agent what tools are available. It is the API that connects agents to databases, APIs, and other external systems.
• Agent Skills teach an agent how to use those tools. They provide the procedural knowledge, best practices, and organizational context required to perform complex, multi-step tasks.

For example, MCP might give an agent access to a git tool. An Agent Skill would teach that agent your team’s specific git branching strategy, pull request template, and code review checklist. One provides the capability; the other provides the expertise. You need both to build a truly effective agentic workforce.

Why an Open Standard Matters for the Enterprise

By releasing Agent Skills as an open standard, Anthropic is making a strategic bet on interoperability and ecosystem growth. This move has several critical implications for the enterprise:

1. It Prevents Vendor Lock-In: An open standard for skills means that the expertise you codify is not tied to a single agent platform. You can build a library of skills for your organization and deploy them across any compliant agent, whether it’s from Anthropic, OpenAI, or an open-source provider.
2. It Creates a Marketplace for Expertise: We will see the emergence of a marketplace for pre-built skills, both open-source and commercial. This will allow organizations to acquire specialized capabilities without having to build them from scratch.
3. It Accelerates Adoption: A standardized format for skills makes it easier for developers to get started and for organizations to share best practices. This will accelerate the adoption of agentic AI and drive the development of more sophisticated, multi-agent workflows.

The Road Ahead: Governance and the Ecosystem

The Agent Skills specification is, as Simon Willison notes, “deliciously tiny” and “quite heavily under-specified” [3]. This is a feature, not a bug. It provides a flexible foundation that the community can build upon. We can expect to see the specification evolve as it is adopted by more platforms and as best practices emerge.

However, the power of skills—especially their ability to execute code—also introduces new governance challenges. Organizations will need to establish clear processes for auditing, testing, and deploying skills from trusted sources. We will need skill registries to manage the discovery and distribution of skills, and policy engines to control which agents can use which skills in which contexts. These are the next frontiers in agentic infrastructure.

Agent Skills are not just a new feature; they are a new architectural primitive for the agentic era. They provide the missing link between general intelligence and specialized execution. By making expertise composable, portable, and standardized, Agent Skills will unlock the next wave of innovation in enterprise AI. The race is no longer just about building the most powerful models; it is about building the most capable and knowledgeable agentic workforce.

References:

[1] Anthropic. (2025, December 18). Agent Skills. Agent Skills.

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

[3] Willison, S. (2025, December 19). Agent Skills. Simon Willison’s Weblog.