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While model and agent evaluation are inextricably linked, their technical benchmarks and metrics for success are fundamentally different.

The AI-Q skill enables Claude Code, Codex, or other general-purpose agents to submit a research task to a running AI-Q server and receive a well-formatted, detailed report with citations. The skill includes a SKILL.md file that tells the harness how to use AI-Q, plus a helper script that manages request routing, job submission, polling, and result retrieval. A skill can mean different things in agent workflows. Agent skills guide the harness, the NVIDIA NeMo Agent Toolkit helps define reusable tool functions, and the AI-Q Agent Skill exposes the full research pipeline—including intent classifi

NVIDIA agent skills are portable instruction sets that teach AI agents how to use NVIDIA CUDA-X libraries, AI Blueprints, and platform tools correctly. NVIDIA-verified skills published in the NVIDIA/skills GitHub repo are: Cataloged and synced daily from the NVIDIA product team that owns it Scanned for software and agent-native risks before publication Signed with a detached skill.oms.sig that can be verified post-download Documented with a skill card describing ownership, dependencies, limitations, and verification status Evaluation is the next layer. It will add standardized quality metri

An NVIDIA-verified skill starts in a source repository owned by a product team. From there, it moves through a publishing flow that can include both human review and automated policy checks, followed by scanning, evaluation, generation of the skill card, signing, cataloging, and synchronization into the public catalog.  Each verified skill is paired with a skill card, a machine-readable trust record that explains the following:  What the skill does Who built the skill  How is the skill licensed What are the skill dependencies   What are the known technical limitations, risks, and mitigatio