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SkillEvaluator
by NVIDIA
Multi-tier agent skill evaluation with live and synthetic tests
Python
Updated Sep 3, 2026
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Summary
Evaluates agent skills through multi-tier tests, synthetic dataset generation, semantic overlap detection, and live agent runs. Layers quality gates to catch regressions and measures how individual skills change agent behavior in realistic interactions. Provides tooling for batch benchmarking and on-the-fly evaluation so you can compare skill variants and spot failure modes.
The Value Proposition
As agents become more autonomous and delegate to specialists, understanding which skills actually improve behavior is critical for trust. SkillEvaluator makes skill-level evaluation repeatable, letting teams separate benchmark scores from real-world impact and track agent track record over time. This matters for multi-agent trust and A2A evaluation because it surfaces failure modes and provides objective gates before deployment. See A2A evaluation.
Ideal For
Teams building or validating agent skills who need repeatable benchmarks, semantic overlap checks, and live behavior measurement before deployment.
Use Cases
- Validate whether a new skill improves real agent behavior before merging
- Generate synthetic evaluation datasets and detect semantic overlap with existing skills
- Run live evaluations to measure how skill changes affect downstream agent decisions
Works With
openaianthropichuggingface
Topics
agent-evaluationagent-securityagent-skillsagentic-aibenchmarkclaude-codecodexevaluateevaluationsecurity-scanner+1 more
Similar Tools
openai-evalsagent-playground
Keywords
multi-agent trusta2a evaluationagent skillsagent-evaluation