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 JuneYaooo · MCP Server · ★ 72
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is nihaixia safe to install? View the security audit →
nihaisha 把倪海厦中医课程整理成可检索、可追溯、有安全边界的 Agent Skill。 Claude Code / Codex / OpenClaw Skill。装进 agent 后，可以用自然语言按症状、方剂、穴位、课程模块、课次或板书截图检索倪海厦课程资料，输出学习型辨证梳理、方证/穴位/药性对比、逐课复习计划和截图证据索引。 English → docs/README.en.md 课程蒸馏方法 本项目的课程蒸馏方法来自作者维护的 lineage-skill：把高密度课程材料整理成可溯源、可迁移、可产出的 Agent Skill。 能做什么 白话问题入口：把“感冒怕冷”“手脚冷”“拉肚子”“睡不着”等普通表达转成课程里的分水岭问题。 多课程检索：覆盖伤寒论、金匮要略、仲景心法、临床案例、八纲辨证、扶阳论坛、易筋经、梁冬对话、斯坦福演讲、天纪、黄帝内经、神农本草、针灸课程，以及讲稿笔记、汉唐中医、诊疗日志、电子书和音频合集索引。 六经与方证导航：按六经、症状、方剂和传变逻辑整理《伤寒论》核心内容。 穴位与药性学习：可检索针灸经络穴位、配穴思路，以及神农本草课程里的药性、剂型、配伍与单味药线索。 逐课复习：按课程模块和课次整理主题地图、关键词和适合复习的问题。 截图证据：已接入 2986 条截图证据索引，对应图片均已压缩为仓库内 WebP，可按方名、穴位、课次、病机、术数关键词或时间点检索。 安全边界：默认作为课程学习与中医理论整理，不做个人诊断、处方或剂量指导。 适合哪些场景 |
| Stars | 72 |
| Forks | 15 |
| Language | Python |
| Category | MCP Server |
| Quality Score | 56.9615938447619/100 |
| Last Updated | 2026-05-31 |
| Created | 2026-05-27 |
| Platforms | claude-code, codex, mcp, python |
| Est. Tokens | ~4831k |
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李可老中医SKILL（1930-2013）思维操作系统。基于13个来源（11本著作+2篇访谈）蒸馏， 含423个医案、120+首方剂、92种症状路由、24种假证识别。 核心能力：急危重症救治（心衰/中风/休克）、
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nihaixia is 倪海厦中医课程资料的 Agent Skill：支持课程检索、方证穴位辨析、学习笔记整理与板书截图证据索引。 | An Agent Skill for Ni Haixia TCM course study, formula-pattern lookup, acupoint reference, and screenshot evidence indexing.. It is categorized as a MCP Server with 72 GitHub stars.
nihaixia is primarily written in Python. It covers topics such as acupuncture, agent-skills, claude-code-skills.
You can find installation instructions and usage details in the nihaixia GitHub repository at github.com/JuneYaooo/nihaixia. The project has 72 stars and 15 forks, indicating an active community.
The top alternatives to nihaixia on Agent Skills Hub include awesome-ai-agent-skills, RivalSearchMCP, likeskill. 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: