LexSkill: A Systematic Chinese Legal Skill Framework with High-Quality Annotations and an Execution Harness

Abstract

Agents in knowledge-intensive domains require not only knowledge retrieval, but also the skills to effectively use retrieved knowledge. Since domain knowledge is often highly complex, agents must possess strong reasoning capabilities in order to perform tasks effectively. In the legal domain, different legal systems exhibit substantial differences, and the reasoning logic for applying legal knowledge varies significantly across systems. Meanwhile, existing skill collections lack a systematic skill framework grounded in domain knowledge, limiting their scalability and extensibility. To address these challenges, we present LexSkill, a reasoning-oriented legal skill library for the Chinese legal system. Within this framework, legal experts construct 36 atomic skills, further organized into 180 sub-atomic skills and 2 compound skills. Since legal problem solving also requires grounded access to legal resources and coordinated reasoning across multiple steps, we further introduce Chinese-Law-Harness, which augments the skill library with MCP modules built over a case repository and a statutory repository, and incorporates a multi-agent system for skill orchestration and execution. Evaluations on existing legal AI benchmarks show that leveraging the skill library through the proposed harness significantly outperforms direct problem solving without structured support, verifying the effectiveness of our skills and harness.