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 WenyuChiou · MCP Server · ★ 6.8k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is awesome-agentic-ai-zh safe to install? View the security audit →
繁體中文 English awesome-agentic-ai-zh 🤖 AI Agent 學習地圖 — 從基本 LLM 概念到自己打造多 agent 系統 結構化 7 階段學習路徑，從「LLM 是什麼、token 怎麼算」一路到 multi-agent 編排、本地部署，每階段都有必做的 動手練習、必修閱讀、精選 project 🎯 專案介紹 這個專案是為想學習 AI 或 AI agent 的人設計的。 本 repo 把網路上散落各處的高品質專案、教材、動手練習、必修閱讀蒐集起來，按從零開始、循序漸進的順序整理成 7 個階段——每階段都會清楚指出該學什麼、必做哪些 動手練習、推薦哪幾個 project、進入下一階段前該檢查什麼。 走完整條路線，你會從「LLM 使用者」進階到「agent 系統建構者」——能看懂 framework 在做什麼、能設計多 agent 協作、能寫自己的 MCP server。 📋 目錄 🎯 專案介紹 📚 快速開始 線上閱讀 本地下載 ✨ 你會收穫什麼？ 🗺️ 學習地圖（兩條學習路徑） 💡 如何學習 📚 相關資源 🤝 如何貢獻 🙏 致謝 🎓 引用 License 📚 快速開始 線上閱讀
| Stars | 6,760 |
| Forks | 923 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 67.6598401214736/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-10 |
| Created | 2026-05-04 |
| Platforms | claude-code, cli, mcp, python |
| Est. Tokens | ~15k |
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awesome-agentic-ai-zh is A trilingual (繁中 / English / 简中) learning roadmap for agentic AI: from LLM basics to multi-agent systems, with 240+ curated resources and hands-on examples. 中文 AI agent 學習地圖。. It is categorized as a MCP Server with 6.8k GitHub stars.
awesome-agentic-ai-zh is primarily written in Python. It covers topics such as agentic-ai, agentic-workflows, ai-agent.
You can find installation instructions and usage details in the awesome-agentic-ai-zh GitHub repository at github.com/WenyuChiou/awesome-agentic-ai-zh. The project has 6.8k stars and 923 forks, indicating an active community.
awesome-agentic-ai-zh is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to awesome-agentic-ai-zh on Agent Skills Hub include cursor-talk-to-figma-mcp, ai-agents-from-zero, Claude-Code-Everything-You-Need-to-Know. 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: