Agents4h ago
Agent Skills Fail In Real-World Tests
A study of 34,000 real-world skills reveals that modular instructions barely improve performance under realistic conditions. Weaker models actually perform worse when using these enhancements. This gap suggests that current benchmarks overstate the utility of skill-based architectures for AI agents. Practitioners should prioritize core model reasoning over modular skill libraries.