The paper "Agent Skills for Large Language Models: Architecture, Acquisition, Security, and the Path Forward" provides a comprehensive survey on the transition from traditional, monolithic large language models (LLMs) to modular, skill-equipped agents.

Here is a short summary of its core themes:

• The Agent Skills Paradigm: Instead of relying solely on model weights or fine-tuning, agents can now use skills—self-contained, dynamically loaded packages of procedural instructions, code, and resources. Driven by a structured SKILL.md file, this allows agents to acquire domain-specific expertise on demand without the need for retraining.
• Architectural Foundations: Skills operate using a progressive disclosure architecture (loading metadata, then instructions, then resources) to conserve context window space while deeply modifying the agent's preparation for a task. The paper notes that skills provide the procedural "what to do," while the complementary Model Context Protocol (MCP) provides the "how to connect" to external tools.
• Skill Acquisition and Deployment: Agents can acquire skills through direct human authoring, reinforcement learning, autonomous exploration, and compositional synthesis. These skills are primarily being deployed within Computer-Use Agents (CUAs), allowing models to seamlessly operate software and graphical user interfaces (GUIs).
• Security Vulnerabilities and Governance: The rapid adoption of agent skills introduces significant security risks, with empirical analysis revealing that 26.1% of community-contributed skills contain vulnerabilities, such as prompt injection or data exfiltration. To mitigate this, the authors propose a Skill Trust and Lifecycle Governance Framework, which uses verification gates and trust tiers to grant graduated deployment capabilities based on a skill's proven safety.

The paper concludes by identifying seven open challenges for the field, including cross-platform portability, capability-based permission models, and the need for standardized skill verification, setting a research agenda for the future of self-improving, trustworthy agent ecosystems.