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 magicyuan876 · MCP Server · ★ 825
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is mineru-tianshu safe to install? View the security audit →
Featured in: Document Parsing
Tianshu 天枢 企业级 AI 数据预处理平台 支持文档、图片、音频等多模态数据处理 MCP 协议 结合 Vue 3 前端 + FastAPI 后端 + LitServe GPU负载均衡 English | 简体中文 <img src="https:
| Stars | 825 |
| Forks | 126 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 66.617481224943/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-28 |
| Created | 2024-08-22 |
| Platforms | mcp, python |
| Est. Tokens | ~17k |
These tools work well together with mineru-tianshu for enhanced workflows:
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全流程 智能招投标 Agent:标书生成 · 招投标解读 · 标书检查 · 标书文档ai排版 · 商机发现 一键完成。 21 项合规检查 · 多模型切换 · RAG 知识库 · OCR 抽取。 从招标公告到可交付 doc
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mineru-tianshu is 天枢 - 企业级 AI 一站式数据预处理平台 | PDF/Office转Markdown | 支持MCP协议AI助手集成 | Vue3+FastAPI全栈方案 | 文档解析 | 多模态信息提取. It is categorized as a MCP Server with 825 GitHub stars.
mineru-tianshu is primarily written in Python. It covers topics such as deepseek-ocr, markitdown, mcp-server.
You can find installation instructions and usage details in the mineru-tianshu GitHub repository at github.com/magicyuan876/mineru-tianshu. The project has 825 stars and 126 forks, indicating an active community.
mineru-tianshu is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to mineru-tianshu on Agent Skills Hub include pullmd, PaperQuay, BidMaster-Pro. 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: