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This paper presents a taxonomy and lifecycle survey of dynamic skill libraries for large language model agents, proposing an eight-stage lifecycle architecture and a six-sense taxonomy to organize evolving skill artifacts.
This paper identifies two coupled scaling laws for skill libraries in LLM agent systems: routing accuracy decays logarithmically with library size, and execution dynamics show a rescue effect. The laws are validated across 15 models and over a million decisions, and law-guided optimization significantly improves performance.