MCRI: A Four-Dimensional Framework for Analyzing and Evaluating Agent Skills

Zongrui Yang; Li Xintong; Runchen Xu; Zhongsheng Wang; Zhedong Lin; Haoyuan Li; Jiamou Liu

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arXiv · 2610.01506

MCRI: A Four-Dimensional Framework for Analyzing and Evaluating Agent Skills

Zongrui Yang·Li Xintong·Runchen Xu·Zhongsheng Wang·Zhedong Lin·Haoyuan Li·Jiamou Liu

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Abstract

As agents evolve from single-tool systems into modular, composite architectures, skills are becoming an important mechanism for capability development and distribution. However, the academic community lacks a structured framework for systematically analyzing and evaluating skills. Drawing on information gain and behavioral constraint, we propose the four-dimensional MCRI Framework and operationalize it as MCRI-Eval, a large language model-based evaluation method. We evaluate MCRI-Eval using 63,812 public skills from the OpenClaw skill Hub, with 58,275 skill-conditioned model executions across BigCodeBench, BFCL-Fundamental, and Mind2Web. MCRI-Eval scores are positively associated with community popularity signals and achieve the highest downstream ranking agreement among the evaluated methods. MCRI-Eval also improves top-1 skill selection across all three benchmarks: compared with the strongest baseline on each benchmark, the skills selected by MCRI-Eval advance by 17.7, 22.8, and 19.6 percentile points in downstream performance rank on BigCodeBench, BFCL-Fundamental, and Mind2Web, respectively. These results indicate that MCRI-Eval provides a useful pre-execution signal for prioritizing promising skills before costly execution-based evaluation.

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Zongrui Yang, Li Xintong, Runchen Xu, Zhongsheng Wang, Zhedong Lin, Haoyuan Li, Jiamou Liu. 2026-10-01. MCRI: A Four-Dimensional Framework for Analyzing and Evaluating Agent Skills. https://arxiv.org/abs/2610.01506

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