Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add SuperJJ007/eatbook --skill universitygit clone --depth 1 https://github.com/SuperJJ007/eatbookWrote 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/superjj007/eatbook/university)<a href="https://agentmods.dev/skills/superjj007/eatbook/university"><img src="https://agentmods.dev/badge/skills/superjj007/eatbook/university/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/superjj007/eatbook/university"><img src="https://agentmods.dev/badge/skills/superjj007/eatbook/university.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00137 | $0.03955 |
| Opus 5 | $0.00068 | $0.01978 |
| Sonnet 5 | $0.00027 | $0.00791 |
| Haiku 4.5 | $0.00014 | $0.00396 |
Grade A, and why
university scanned grade A with 1 finding 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 12d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
**取封面**：每本书 `curl "https://openlibrary.org/search.json?q=<书名+作者>&fields=cover_i&limit=5"`，取第一个有 `cover_i` 的，封面 URL = `https://covers.openlibrary.org/b/id/<cover_i>-L.jpg`，放进 `.cover>img`（带 `onerror="this.remove()"`，取不到时露 How it starts
The opening of the file, as written. The whole thing — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/eatbook:university — 领域学习路线构建
目标 / 边界 / 产出
目标：把一个领域变成可执行的分阶段学习路线。检索的目的不是挑几本书， 而是建立一张可溯源的「该学什么 → 该读什么 → 按什么顺序 → 现在去找什么」映射。
边界（明确不做）：
- 不下钻到书内章节/部分——书↔课映射到整本书为止；章节级精读规划是
/eatbook:read的事 - 不复刻培养方案——实验课/统计/科研训练等不可读化或读者已具备的单元不设课 （在"读者背景"节用白话解释一句）
- 不做前沿文献深研——研究生级课程与文献追踪归
/eatbook:branch - 方法论不端上桌——检索的严谨性留在过程里（本文件 + references 协议）， 产出只给结论和来源链接，不解释分级体系和检索方法
产出：两个文件，domains/<slug>/ 下：
00-domain.html（读者向 HTML 档案）：内联 HTML 概览流程图 + CSS 树状路线图 + 当前阶段找书 + 选书权衡system.md（Markdown，体系详情）：每门课完整书单、来源证据、公开课推荐；低频查阅
硬验收标准：路线图 5 秒看清每个阶段读哪些书。
步骤 0：确认领域
$ARGUMENTS有领域名则直接用；否则问用户："要系统学习哪个领域？"- 定 slug（小写英文，连字符），如 神经科学 →
neuroscience - 若
domains/<slug>/00-domain.html已存在：读取并播报进度，问用户是继续、补书单还是重做体系
步骤 1：解析激活指令（提示词优先，模糊才补问）
读者激活 skill 时给出的提示词是第一信息源，从中提取三个字段：
| 字段 | 例 |
|---|---|
| 领域口径 | "神经科学，打底后往认知走" → 口径已明 |
| 学习目标 | "想彻底搞懂脑科学" → 通识理解 |
| 精读规模 | "先精读一两本" → 已明 |
- 三个字段都提取到 → 零提问，直接进入检索
- 提取不到或有歧义的字段 → 用 AskUserQuestion 做一次简要访谈只补缺口 （口径歧义最常见也最致命——"神经科学/认知科学/脑科学"选书差异巨大， 歧义时每个选项注明代表教材和路线含义）
- 背景水平、硬核功底这里不问——那要等检索揭示该领域需要什么之后（步骤 3）
步骤 2：检索建课程骨架（内部工作，先定"该学什么"，不出书名）
检索方法与来源等级见 references/retrieval-protocol.md 和 references/source-registry.md。要点：
- 学会课程体系（T1）定主题覆盖：查该领域专业学会的本科课程指南/核心能力框架 （CS→ACM CS2023；数学→MAA CUPM；统计→ASA GAISE；心理→APA Guidelines； 生物→AAAS Vision and Change；神经→SfN/FUN core competencies……）
- 2-3 所 top 校本科培养方案（T2）定课程单元：fetch degree requirements，提取核心必修课； 仅查英语授课院校（US/UK/AU），中文培养方案不作 T2（中英教材体系独立，混用污染双信号）
- 两者交叉 → 8-12 门核心课，每门记录：课名、主题一句话、证据（文件名+链接，供织进叙述）
- 找不到学会课程体系的领域（哲学、政治学等）：退而用 3-5 所 top 校培养方案交叉
- 研究生级课程不进骨架（留给
/eatbook:branch） - 课程分型：顺序课（进路线图，按阅读顺序连续编号）/ 工具课（参考型，不占节点， 书进阶段书表标"全程伴随查阅"）/ 垫层课（降为一行补底说明，不设课）
- 提取硬核前置清单：检索过程中记录该领域绕不开的硬核知识 （数学/编程/实验/相邻学科——神经科学→生物化学基础、微积分线代； CS→离散数学；经济→微积分统计），供步骤 3 访谈用
步骤 3：硬核前置访谈（检索后才问得出来的问题）
拿着步骤 2 的硬核前置清单问读者（AskUserQuestion，multiSelect）： "这个领域的体系里有这些硬核前置，你具备哪些？"——每项注明它影响哪些课。
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 184 lines · 137 tokens per session scan A 0fa8d032bfce
university is a skill published in the GitHub repository SuperJJ007/eatbook (5 stars, last pushed 3mo ago), licensed MIT. It adds 137 tokens to every session and 3,955 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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