The Problem
Ask an LLM to "create an Excel report" and you'll get a beautifully formatted description of a spreadsheet that doesn't exist. LLMs handle language well. Deterministic, file-producing operations? Not their strength.
Agent Skills, announced by Anthropic on October 16, 2025, address this gap. They're modular capability packages—instructions, scripts, resources—that Claude loads on-demand for specific tasks.
How They Work
Skills use progressive disclosure across three phases:
- Startup: Load only skill names and descriptions (keeps context lean)
- Activation: Claude requests permission when a skill is relevant
- Execution: Full instructions and resources load in a sandboxed environment
No token tax for unused capabilities. No mega system prompts. Claude routes to the right tool without upfront bloat.
Skills cover common enterprise workflows—Excel manipulation, PDF extraction, PowerPoint generation—and support custom builds for team-specific standards (review checklists, compliance rubrics, documentation templates).
Skills vs. MCP
This isn't redundant with Model Context Protocol (MCP), Anthropic's November 2024 standard for external integrations.
MCP connects Claude to outside systems: databases, APIs, SaaS tools. It's infrastructure for reaching external data.
Skills package repeatable workflows inside Claude's ecosystem. They optimise doing the thing, not reaching the thing.
Mental model: Skills are built-in shortcuts. MCP is the app ecosystem.
Enterprise Considerations
Skills run in sandboxed environments, but executable code raises audit questions. Security teams will need to review custom skills before org-wide deployment.
Anthropically noted future plans for centralized management, analytics, and visual skill-building tools. Adoption path matters—OpenAI's GPT Store has ecosystem momentum. Skills need enterprise validation beyond early-stage enthusiasm.
The company's projected to overtake OpenAI in ARR by 2027-28, per industry analysts, driven by enterprise utility focus. Skills fit that trajectory: less about demos, more about standardizing operational work LLMs currently handle inconsistently.
The Real Test
Skills solve a genuine problem—LLMs producing representations instead of artifacts. The implementation question: how well do they scale when teams manage dozens of custom workflows? We'll see.
What this means in practice: CTOs evaluating agentic AI now have a path between "prompt engineering everything" and "building custom tooling." Worth watching how enterprise deployment patterns emerge over the next quarters.