Agent Skills for Large Language Models: Architecture, Acquisition, Security, and the Path Forward
arXiv:2602.12430v4 Announce Type: replace-cross Abstract: The transition from monolithic language models to modular, skill-equipped agents marks a defining shift in how large language models (LLMs) are deployed in practice. Rather than encoding all procedural knowledge within model weights, agent skills -- composable packages of instructions, code, and resources that agents load on demand -- enable dynamic capability extension without retraining. It is formalized in a paradigm of progressive disclosure, portable skill definitions, and integration with the Model Context Protocol (MCP). This sur
The proliferation of monolithic LLMs is revealing their limitations, driving a need for more modular and adaptable architectures to unlock broader real-world applications.
This architectural shift from monolithic LLMs to agents with composable skills fundamentally alters how AI capabilities are developed, deployed, and secured, impacting efficiency and scalability.
AI development moves towards modular, dynamic skill integration rather than continuous retraining of large, static models, allowing for more agile and context-aware AI systems.
- · AI platform providers
- · Developers of specialized AI tools
- · Enterprises leveraging AI agents
- · Cybersecurity firms
- · Companies reliant on monolithic LLM development
- · Traditional software-as-a-service providers (long term)
- · Organizations slow to adopt modular AI
The adoption of agent skills accelerates the deployment of AI into complex, real-world workflows, increasing automation.
This modularity enables more sophisticated and autonomous AI systems, potentially leading to significant productivity gains across various industries and collapsing traditional software layers.
Enhanced AI capabilities and autonomy may introduce novel security vulnerabilities and ethical challenges requiring new regulatory frameworks and oversight mechanisms.
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Read at arXiv cs.AI