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 stephenlzc · Agent Tool · ★ 139
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is AI-Powered-Literature-Review-Skills safe to install? View the security audit →
Literature Reviewer Skill 中文（主文档） | English ⚠️ 安全提示: 本 Skill 依赖 browser skill 进行浏览器自动化，将访问浏览器 cookies 和登录状态。建议在隔离环境（如 Docker）中运行。详见 SECURITY.md 系统性学术文献回顾（Literature Survey）Skill for Kimi CLI 采用 8阶段工作流，浏览器自动化检索，支持 CNKI/WOS/ScienceDirect，零配置输出 GB/T 7714-2015 格式文献综述 📖 简介 Literature Survey Skill 是一个为 Kimi CLI 设计的 Skill 插件，用于帮助用户进行系统性的学术文献回顾。 核心优势 🚀 8阶段工作流：从查询分析到综述生成的完整流程 🌐 浏览器自动化：无需API配置，直接访问学术数据库 ✅ 多数据库支持：CNKI、Web of Science、ScienceDirect、PubMed 📚 结构化输出：GB/T 7714-2015引文 + 标题 + 摘要的Markdown文档 🔄 中断续传：支持会话保存和恢复 ✨ 功能特性 ✅
| Stars | 139 |
| Forks | 18 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 66.1798910234957/100 |
| Last Updated | 2026-09-07 |
| Created | 2026-03-05 |
| Platforms | python |
| Est. Tokens | ~17k |
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AI-Powered-Literature-Review-Skills is AI驱动的系统性学术文献回顾工具 | AI-Powered Literature Survey Tool with 8-Phase Workflow & Agent Swarm Architecture. It is categorized as a Agent Tool with 139 GitHub stars.
AI-Powered-Literature-Review-Skills is primarily written in Python. It covers topics such as academic-papers, academic-research, ai-agent.
You can find installation instructions and usage details in the AI-Powered-Literature-Review-Skills GitHub repository at github.com/stephenlzc/AI-Powered-Literature-Review-Skills. The project has 139 stars and 18 forks, indicating an active community.
AI-Powered-Literature-Review-Skills is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to AI-Powered-Literature-Review-Skills on Agent Skills Hub include De-Anthropocentric-Research-Engine, cli-anything-zotero, bibverify. 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: