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 MRT-8/paper-for-beginners --skill paper-for-beginnersgit clone --depth 1 https://github.com/MRT-8/paper-for-beginnersWrote 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/mrt-8/paper-for-beginners/paper-for-beginners)<a href="https://agentmods.dev/skills/mrt-8/paper-for-beginners/paper-for-beginners"><img src="https://agentmods.dev/badge/skills/mrt-8/paper-for-beginners/paper-for-beginners/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/mrt-8/paper-for-beginners/paper-for-beginners"><img src="https://agentmods.dev/badge/skills/mrt-8/paper-for-beginners/paper-for-beginners.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 | $0.00144 | $0.01831 |
| Opus 5 | $0.00072 | $0.00915 |
| Sonnet 5 | $0.00029 | $0.00366 |
| Haiku 4.5 | $0.00014 | $0.00183 |
Grade A, and why
paper-for-beginners 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 10d 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.
用 `curl` 直接调用 Notion API 写入（MCP 工具排版能力不足）。写入前先阅读 `references/notion-formatting.md` 了解完整的 block 结构、JSON 模板和排版规范。 How it starts
The opening of the file, as written. The whole thing — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
小白读论文技能
将学术论文转化为零基础读者也能看懂的通俗解读，保留核心信息的同时去除专业门槛。
写作风格
- 大白话优先：能用日常用语就不用专业术语
- 比喻丰富：每个抽象概念都配一个生活化比喻
- 故事感强：让读者感觉在听故事，而不是在读教科书
- 准确不失真：通俗化不等于不准确，核心信息必须保留
- 有温度：用"你可以这样理解""简单来说""打个比方"等口语化引导
- 中文为主：专业术语首次出现时给出英文原文，后续使用中文
工作流程
第一步：查找论文
- 使用
WebSearch搜索论文，优先查找正式发表版本（会议/期刊 > arXiv）- 搜索策略：先搜论文标题 + "paper"
- 如果用户给的是 arXiv 链接，也搜索是否有正式发表版本
- 使用
WebFetch获取论文内容（优先 HTML 版本） - 搜索开源代码仓库（搜索"论文标题 github"或查看论文中的代码链接）
第二步：系统性阅读（内部分析，不直接输出）
按 Abstract → Introduction → Method → Experiments 顺序阅读，内部思考以下问题：
- 作者要做什么（1 句话概括）
- 难点在哪里
- 现有方法的痛点/不足是什么
- 作者的研究假设或核心直觉是什么
- 宏观上怎么解决的
- 技术细节（关键组件、步骤、每步的输入输出）
- 方法为什么有效
- 和主流方法的本质区别、适用场景
- 做了哪些关键实验，局限性在哪里
- 主要贡献是什么
第三步：小白化改写（10 个 Section）
| Section | 标题 | 内容要求 |
|---|---|---|
| 1 | 论文基本信息 | 标题（中英文）、作者/机构、发表venue、论文链接、代码链接 + 摘要中文翻译（忠实原文，通顺可读，单独用引用块展示） |
| 2 | 一句话看懂 + 速记版流程 | 一句大白话概括这篇论文在做什么（侧重问题和方法，零术语）+ 3-5 步速记 pipeline（每步用自明性大白话，不用论文术语，读者只看这几步就能大体理解论文在做什么） |
| 3 | 专业术语词典 | 8-15 个关键术语，格式：术语名 — 大白话解释 + 生活比喻 |
| 4 | 这个研究为什么要做 | 三段式动机分析：①旧方法哪里不好（痛点）→ ②新方法的灵感从哪来 → ③核心假设是什么。用大白话讲清"为什么需要这篇论文" |
| 5 | 方法流程详解 | 每步严格按"吃进什么→做了什么→吐出什么"三段式描述，确保读者跟着走完就理解整个方法。如有关键公式/算法，用人话翻译其含义和在方法中的角色（"这个公式其实就是在算…"） |
| 6 | 和别的方法比有什么不同 | 核心区别是什么、创新点在哪里、用简单对比表（旧方法 vs 新方法，列出优点/缺点/改进点）呈现、说明适合解决什么类型的问题 |
| 7 | 实验怎么做的、效果如何 | ①实验设计思路（怎么验证的）→ ②关键结果（"比XX好了多少"）→ ③哪些场景下效果最好 → ④实验局限（没测哪些数据集、计算开销大不大、泛化验证是否充分） |
| 8 | 故事版本 | 用完整比喻故事串联核心思想，3-5 段，有趣易记 |
| 9 | 论文亮点与不足 | 亮点 3-5 条 + 方法层面不足 2-3 条（设计缺陷、理论局限、适用范围窄等，和 Section 7 的实验局限不重复） |
| 10 | 读完应该记住的 | 3-5 条 takeaway + 一句话读后收获（"这篇论文最值得记住的是什么"，侧重价值和启发，和 Section 2 的读前导读不重复）+ "想动手试试？"（开源情况、复现关键步骤、需要注意的超参数/数据预处理/训练细节、能否迁移到其他任务及如何迁移） |
示例（Section 3 术语格式）：
Attention 机制 — 让模型学会"重点看哪里"。就像你读长文章时眼睛会自动跳到加粗关键词上，attention 就是教 AI 做同样的事。
示例（Section 2 速记 pipeline 格式）：
- 把图片切成小方块 → 2. 让每个方块互相"看看"对方 → 3. 综合所有方块的信息做判断
示例（Section 5 公式翻译格式）：
论文里有个公式 L = -log P(y|x)，翻译成人话：就是在算"模型猜对答案的概率有多高"，概率越高这个值越小，所以训练目标就是让这个值尽量小。
What ships with it
1 file 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.
- 10d ago First seen · 98 lines · 144 tokens per session scan A c7e713aa122b
paper-for-beginners is a skill published in the GitHub repository MRT-8/paper-for-beginners (3 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 144 tokens to every session and 1,831 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.
Other skills, from other repositories
paper-deep-reader
A guided method for reading one research paper and writing a detailed teaching report about it. It explains the subject from the basics through the paper's methods, formulas, evidence, and limits.
interlinear
Read and annotate English technical papers in a terminal or chat with concise Chinese translations and explanations while preserving the source text and argument. Use when a user asks to read, explain, translate, annotate, or study an academic paper, arXiv preprint, PDF, DOI, technical passage, figure caption…
keshav-three-pass
Tactic: Read one paper by Keshav's three-pass method — a shallow skim, a contribution-grasping full read, then a deep virtual re-implementation. Use when the goal is understanding a paper rather than extracting a fixed schema.
first-pass-skim
Keshav's first pass over one paper — a 5-10 minute skim of title, abstract, headings, figures, and conclusion only, producing skim notes and a read-deeper judgment. Use this as the first step whenever a paper is being read via the Keshav three-pass method; always precedes second-pass-grasp and never reads section…
qalmri-worksheet
Tactic: Fill a six-slot QALMRI worksheet for one paper: Question, Alternatives, Logic, Method, Results, and Inference. Use for a structured reading worksheet rather than a graded evaluation.
paper-reading-zh
A Chinese-language workflow for closely reading research papers, especially in computer science and artificial intelligence, from sources such as PDFs, arXiv, or conference pages.