No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by Golden2002 · Agent Tool · ★ 140
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is legal-research-skill safe to install? View the security audit →
Legal Research SKILL 中国法律全领域系统性检索专家 - AI Agent Skill for Legal Research 📖 项目简介 本项目提供一套专业的法律检索 Agent Skill，可用于 Cursor、Claude Code、OpenCode 等 AI 编程工具。无论是非法律专业人士需要了解法律知识，还是法律从业者进行法律检索，都能提供系统性的法律规范查找与整理服务。 P.S. : 直接复制中的内容，作为提示词输入给对话式ai，比如豆包、deepseek，也是好用的 🎯 核心功能 🔍 全领域法律检索：覆盖劳动争议、合同纠纷、侵权责任、婚姻家庭、行政法、刑事等领域 📚 法源全面覆盖：法律、行政法规、司法解释、部门规章、地方性法规、指导性案例 📋 结构化输出：提供标准化的法律检索报告模板 ⏰ 时效性处理：诉讼时效、除斥期间等期限梳理 📖 引用规范：标准法条引用格式 ⭐能够针对不同用户群体（遇到法律问题的普通人、法学生、专业律师、法官/检察官）提供差异化服务策略，识别用户身份并匹配合适的检索深度和输出内容。 📑 目录 快速开始 Skill 配置 使用场景 核心指令 工作流程 常见法律领域速查 法律文书引用格式 检索数据库推荐 贡献指南 许可证 快速开始 前置依赖 Node.js 20.6+（含 npm） Git 安装 OpenSkills 安装本 Skill Skill 配置 支持的工具 配置步骤 克隆本仓库到本地 将 文件夹复制到你的 AI 工具的 skills 目录 重启 AI 工具，Skill 将自动加载 使用场景 "查一
| Stars | 140 |
| Forks | 15 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 60.161119651232/100 |
| Open Issues | 1 |
| Last Updated | 2026-08-28 |
| Created | 2026-02-28 |
| Platforms | claude-code, python |
| Est. Tokens | ~14k |
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legal-research-skill is 本项目提供一套专业的法律检索 Agent Skill，可用于 Cursor、Claude Code、OpenCode 等 AI 编程工具。无论是非法律专业人士需要了解法律知识，还是法律从业者进行法律检索，都能提供系统性的法律规范查找与整理服务。. It is categorized as a Agent Tool with 140 GitHub stars.
legal-research-skill is primarily written in Python.
You can find installation instructions and usage details in the legal-research-skill GitHub repository at github.com/Golden2002/legal-research-skill. The project has 140 stars and 15 forks, indicating an active community.
legal-research-skill is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to legal-research-skill on Agent Skills Hub include claude-plugins, tutor-skills, repren. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: