ai-engineering-from-scratch-zh is a Chinese-language adaptation of a course that teaches AI engineering by implementing algorithms, tools, and agents from the ground up. It is for learners following a 20-stage path across mathematics, machine learning, agent systems, and production topics, with lessons using Python, TypeScript, Rust, and Julia. The catalogue entries provide agent skills and instructions associated with the course.
Borrowing it
Nothing to install: this file belongs to fancyboi999/ai-engineering-from-scratch-zh. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/fancyboi999/ai-engineering-from-scratch-zh/main/.claude/skills/learn-mcp/SKILL.mdgit clone --depth 1 https://github.com/fancyboi999/ai-engineering-from-scratch-zhWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/fancyboi999/ai-engineering-from-scratch-zh/learn-mcp)<a href="https://agentmods.dev/skills/fancyboi999/ai-engineering-from-scratch-zh/learn-mcp"><img src="https://agentmods.dev/badge/skills/fancyboi999/ai-engineering-from-scratch-zh/learn-mcp/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/fancyboi999/ai-engineering-from-scratch-zh/learn-mcp"><img src="https://agentmods.dev/badge/skills/fancyboi999/ai-engineering-from-scratch-zh/learn-mcp.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00123 | $0.02361 |
| Opus 5 | $0.00062 | $0.01180 |
| Sonnet 5 | $0.00025 | $0.00472 |
| Haiku 4.5 | $0.00012 | $0.00236 |
Grade A, and why
learn-mcp scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
学习 Model Context Protocol (MCP)
教授专注的 Model Context Protocol (MCP) 路线。一次调用覆盖一节课。学习者应检查请求与响应、预测边界结果、运行或手工跟踪 lab，并在推进前记录课程 checkpoint。
使用宿主的调用语法
可移植 skill 名称是 learn-mcp。不要将某个宿主的语法说成协议规则。
| 宿主 | 开始或继续 |
|---|---|
| Codex | learn-mcp，或从 /skills 选择它 |
| Claude Code | /learn-mcp |
| 其他兼容宿主 | Use learn-mcp to start or resume the Model Context Protocol (MCP) path. |
选择课程前读取路线
唯一事实来源是 learning-paths/model-context-protocol.json。仓库可用时优先本地文件；否则从以下地址获取所需文件：
https://raw.githubusercontent.com/fancyboi999/ai-engineering-from-scratch-zh/main/<path>
按 manifest 的 lessons 数组及其 order 进行。必修顺序为 06、07、08、09、10、11、12、13、14、15、16、18、17、28、29、30、31。第 16 课后，数字上的下一课不再是此路线的下一课。
对选中课程，完整读取 docs/zh.md 和 quiz.json。只在当前教学步骤需要时读取或运行 code/ 与 outputs/。采用课程声明的 protocol era。绝不把 legacy handshake 规则混进现代无状态 trace。
第 23 课是唯一可选 capstone。只能在所有必修行完成且 manifest prerequisitePaths 中第 19、20 课均完成后提供。绝不悄悄向此路径添加其他课程。
确立证据模式
第一次可执行 checkpoint 前，确定：
- 课程文件是否在本地可用。
python3 --version是否成功。- 学习者能否在当前工作目录写入
MCP-LEARNING.md。 - 学习者选择第 07 课的可选第二实现时，是否有 TypeScript runner。
本地文件和 Python 3 都可用时，采用 executable mode。记录绝对工作目录、精确命令、exit code、request id 与 method、选择的 protocol era，以及观察到的结果或错误。隐去 tokens、secrets、cookies、authorization headers 和敏感参数值。
仓库或 runtime 不可用时，采用 conceptual mode。阅读课程，手工跟踪一个小型 request 与 response，并将证据标为 Conceptual。将 runtime、transport、authorization 和 deployment 检查保留为 Pending。绝不把手工跟踪说成已执行通过。
可执行文件需要但缺失时，提供将仓库克隆到学习者选择目录的选项。克隆前等待确认。没有克隆时概念课仍必须可用。
查找或创建进度
在当前工作目录使用 MCP-LEARNING.md。不要将此路线写入 LEARNING.md，也不要修改 Agent Skills 进度。
决定不存在状态前，安全处理旧文件名：
- 若存在
MCP-LEARNING.md，使用它。若也有MCP-ENGINEERING-LEARNING.md，两个文件都不要覆盖；报告冲突并询问下一次更新由哪个文件拥有。 - 若
MCP-LEARNING.md不存在而MCP-ENGINEERING-LEARNING.md存在，在教学前将 legacy 文件在同目录改名为MCP-LEARNING.md（rename the legacy file toMCP-LEARNING.md）。字节级保留所有学习者笔记和证据行（Preserve every learner note and evidence row byte for byte）。不能原子 rename 时，先复制，验证新文件匹配，再删除 legacy 文件。 - 只有两个文件名均不存在时才创建新状态文件。绝不以空白模板替换 legacy 进度。
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 155 lines · 123 tokens per session scan A e103b52cf5e0
learn-mcp is a skill published in the GitHub repository fancyboi999/ai-engineering-from-scratch-zh (1,055 stars, last pushed 2d ago), licensed MIT. It adds 123 tokens to every session and 2,361 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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