nihaisha-nishi-tcm: 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

by JuneYaooo · MCP Server · ★ 2.2k

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 60/100

None of the 43 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 43 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

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Quick Facts

Stars2,232
Forks489
LanguagePython
CategoryMCP Server
Quality Score60/100
Open Issues2
Last Updated2026-11-07
Created2026-05-27
Platformsclaude-code, codex, mcp, python
Est. Tokens~15k

About nihaisha-nishi-tcm

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。...

Compatible Skills

These tools work well together with nihaisha-nishi-tcm for enhanced workflows:

  • claude-prime — semantic(0.19)+complementary+same_lang+similar_pop+shared_platform (56%)
  • book-to-skill — semantic(0.18)+complementary+same_lang+similar_pop+shared_platform (56%)
  • turbo — semantic(0.17)+complementary+same_lang+similar_pop+shared_platform (56%)
  • ros2-engineering-skills — semantic(0.17)+complementary+same_lang+similar_pop+shared_platform (56%)
  • verilog-generator — semantic(0.16)+complementary+same_lang+similar_pop+shared_platform (56%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 43 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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:

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