SkillRet benchmarks skill retrieval for LLM agents

SkillRet is a research benchmark for a practical agent problem: how an LLM agent finds the right reusable skill for a task. The benchmark contains 17,810 public agent skills, organized with semantic tags and a two-level taxonomy spanning 6 major categories and 18 sub-categories. This matters because agents will not scale only by making the base model smarter; they also need reliable retrieval over tools, skills, workflows, and prior procedures. Watch whether SkillRet becomes a standard evaluation for agent memory, tool routing, and skill libraries.

Key details: SkillRet, 17,810 skills, 6 major categories, 18 sub-categories, LLM agents.

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