AI agents are only as effective as the context they receive. Even with capable models and well-documented NVIDIA libraries, agents can spend extra steps finding the right tools, burn tokens on dead ends, or struggle with specialized tasks. Skills package the instructions, examples, and tool guidance for agents to move faster from intent to solution. To measure whether these skills improve agent trajectories and outputs, NVIDIA built an open evaluation layer for agent skills.
NVIDIA SkillEvaluator is an open source tool for measuring how skills affect agent performance through static checks and real-world task runs with and without each skill. NVIDIA verified Skills are packaged, signed capability descriptors that tell an agent exactly what an NVIDIA product does, when to invoke it, and how to call it. The verified part is the measurement that determines it is ready.
This post shares the first benchmark results for more than 300 verified skills across over 30 NVIDIA products. Each skill was evaluated on two independent harnesses. For each harness, Skill Lift was calculated by comparing scores from runs with and without the skill installed.
NVIDIA publishes plugins for Claude Code, Codex, and Cursor, and the same skills are available through Skills.sh, ClawHub, and Hermes Hub.
