{"slug":"mrgediao-paper-reading-zh","name":"Paper Reading Zh","description":"Evidence-aware Chinese paper reading rules packaged as reusable agent skills for Codex, Claude Code, and ChatGPT.","long_description":"# paper-reading-zh\n\n给 AI 加一套论文阅读的证据规则：未核验的不补，读不到的不编，比较前先对口径。\n\n[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](./LICENSE)\n[![Version](https://img.shields.io/badge/version-v0.2.1-green.svg)](./CHANGELOG.md)\n[![PRs welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](./CONTRIBUTING.md)\n[![LINUX DO](https://img.shields.io/badge/LINUX%20DO-Community-blue.svg)](https://linux.do)\n\nAn evidence-rule pack for AI-assisted paper reading. Docs and outputs are in Chinese by design.\n\n`paper-reading-zh` 是一个中文论文精读规则包，面向 Codex、Claude Code、Claude Project 和 ChatGPT Project。它不是论文翻译器，也不是文献管理器；它的目标是让 AI 先判断材料和论文类型，再基于可定位证据解释结论，少一点顺滑猜测。\n\n- **证据边界**：venue、年份、CCF、代码链接未核验就写“未核验”；实验数字必须锚定原文 Table / Figure 或具体段落。\n- **防顺滑编造**：公式抽取乱码不硬补，图表读不到不描述，只能读到摘要时明确写“仅基于摘要”。\n- **跨论文口径审计**：比较多篇论文前先检查数据集、指标定义、模型规模、训练预算和测试 setting。\n- **论文类型自适应**：算法、系统/测量、数据集/benchmark、理论/证明、综述/立场、观点/路线图使用不同主体骨架。\n- **证据审计**：可把核心主张逐项对到原文锚点、证据类型、支持强度和未覆盖问题。\n- **双入口复用**：同一套规则同时提供 Agent Skill（CLI / Agent 环境）和 Web Prompt Kit（网页端项目）。\n\n## 和直接把论文丢给 AI 的区别\n\n经常用 AI 读论文的人大多见过这些行为：查不到 venue 就补一个像样的，只读到摘要却写出全文精读，两篇论文口径不同也直接判胜负。这套规则把它们逐条挡住：\n\n| 场景 | 无规则的典型行为 | 本规则下的输出 |\n|---|---|---|\n| venue / 代码链接查不到 | 凭印象补一个 | 写“未核验”；找过没找到写“未找到” |\n| 只能读到摘要 | 当作读完整篇开始精读 | 写“仅基于摘要”，先问是否继续轻量解读 |\n| 引用实验数字 | 复述时丢失出处 | 锚定到原文 Table / Figure 或段落，定位不到就不写 |\n| 公式抽取乱码 | 凭记忆重构公式 | 说明 PDF 抽取异常，只解释上下文可确认的含义 |\n| 多篇论文比较 | 按分数直接排名 | 先对数据集 / 指标 / 规模口径，不可比就标注“口径不完全可比” |\n| 理论、benchmark 或路线图论文 | 一律硬套“方法 / 实验 / 结果” | 按主要贡献与证据结构选择论文类型；无法判断时不强套模板 |\n| 用户要求证据审计 | 只给笼统的“有实验支持” | 列出主张、锚点、证据类型、支持强度依据和未覆盖问题 |\n\n左列是常见失败模式的示意，不是对某个具体产品的实测记录；右列是规则的硬性要求。\n\n## 输出长什么样\n\n默认输出是约 2000 到 3500 中文字的中等深读。关键词（不超过 5 个）、一段话总结（不超过 150 字）和论文基本信息（标题 / venue/年份 / 链接 / 任务领域 4 项）保持稳定；主体会按论文类型调整，不再把系统、理论、benchmark 或路线图论文硬塞进同一骨架。\n\n证据标注落在输出里是这样的（依据一篇真实验证过的 16 页路线图论文改写的示意节选，完整记录见 [docs/validation-2026-05-27.md](./docs/validation-2026-05-27.md)）：\n\n```text\n论文基本信息：\n- 标题：A Time Scaling Theory for Multi-Layer Electronic Systems\n- venue/年份：未核验\n- 链接：未找到\n- 任务领域：多层电子系统的时间缩放理论（产业路线图）\n\n……\n- ","tagline":"Evidence-aware Chinese paper reading rules for Codex, Claude Code, Claude Project, and ChatGPT Project","category":"research","tags":["paper-reading","evidence-rules","chinese","agent-skill","research"],"author":"MrGeDiao","verified":false,"attribution":{"status":"community_indexed","statusLabel":"Community indexed","shortLabel":"COMMUNITY INDEXED","sourceLabel":"OpenAgentSkill auto-indexer","sourceDetail":"MrGeDiao/paper-reading-zh","creatorName":"MrGeDiao","creatorUrl":"https://github.com/MrGeDiao","sourceUrl":"https://github.com/MrGeDiao/paper-reading-zh","indexedBy":"OpenAgentSkill community index","claimUrl":"https://www.openagentskill.com/skills/mrgediao-paper-reading-zh#claim-this-skill","claimCta":"Claim this skill","trustNote":"This listing was indexed from public sources and is not marked official until a maintainer claim is approved.","publicNote":"Attribution links to the public repository or creator profile. 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