{"slug":"linwei-chen-paper-deep-reader-skill","name":"Paper Deep Reader Skill","description":"A source-grounded paper deep-reading agent skill with domain lenses, evidence audits, and adaptable delivery modes for cross-disciplinary research.","long_description":"# Paper Deep Reader\n\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)\n[![Version](https://img.shields.io/badge/version-3.0.0-blue.svg)](CHANGELOG.md)\n\n面向跨学科科研人员的、来源可追溯的单篇论文深读 Agent Skill。它从 PDF、URL、DOI、预印本标识、标题或全文出发，适配**论文领域、读者背景、研究目标、交付密度与模型视觉能力**。\n\n它不是摘要扩写器，也不把“写得很长”当成“读得很深”。它希望让读者在最短时间内回答：\n\n- 论文真正解决了什么具体问题？\n- 作者相对旧方法、旧理论或旧研究设计究竟改变了什么？\n- 核心机制能否用一个具体例子从头走通？\n- 关键公式、定义或分析框架怎样对应实际研究步骤？\n- 哪些图表与证据真正支撑核心结论，哪些只是作者的解释？\n- 论文值得精读、复现、引用、迁移，还是应当跳过？\n\n默认交付一份紧凑的“讲懂版”：\n\n```text\n为什么做 → 核心机制怎么工作 → 凭什么相信 → 在哪里会失败\n```\n\n核心原则是：**读深不等于写长；有助理解的图表必须加入，内部审计清单不必全部倾倒给读者。**\n\n## 核心理念\n\n### 深读与长写是两回事\n\n阅读范围由 `depth` 决定，写作密度由 `delivery` 决定。默认可以完整核对方法、证据和附录，同时只向读者呈现改变理解或判断的内容。压缩的是重复、背景和过程记录，不是核心机制、决定性证据或结论边界。\n\n### 先建立理解，再展开审计\n\n报告先用一个最小例子讲通机制，再回到术语、公式和精确条件。需要审稿、复现或逐图表核查时，先保留可独立阅读的理解层，再把详细账本放进附录，而不是让读者先穿过几十页检查表。\n\n### 完整性属于证据系统，不等于正文全量展示\n\n所有编号图表、关键形式化对象和核心主张仍进入 manifest 与 source map。主文只选择那些删除后会导致读者误解机制、误判证据或越过边界的内容。这样既保留可审计性，也避免把后台账本变成前台负担。\n\n### 图表是理解接口，不是装饰\n\n总体框架、机制流程、关键比较、决定性结果和失效边界图，只要能显著降低理解成本，就应进入正文。选择图表不采用僵硬数量上限，而要求每张图回答一个独立问题，并说明“先看哪里、真正说明什么、不能说明什么”。\n\n### 解释可以适配，事实不能随画像变化\n\n读者背景会改变术语密度、例子、推导步长和技术重点，但不会改变论文事实、证据等级或缺失信息。跨学科类比只能搭桥，不能替代精确定义。\n\n### 结论必须回到证据边界\n\n论文自称“首个”或“SOTA”不等于已经独立验证。报告区分作者主张、直接证据、本文推断和外部背景，并明确最可信结论、最薄弱主张、替代解释与最快补强方式。\n\n## 主要特性\n\n- **三遍阅读法**：全局地图 → 机制重构 → 证据审查。\n- **理解优先**：先用一个具体例子讲通机制，再回到术语、公式和精确条件。\n- **四种交付模式**：`brief`、`explain`、`audit`、`targeted`。\n- **深度与篇幅分离**：默认 `depth: deep + delivery: explain`，完整阅读但选择性表达。\n- **视觉教学**：加入能讲清框架、机制、比较、证据或边界的图表，不设僵硬数量上限。\n- **证据可追溯**：区分作者主张、直接证据、报告推断和外部背景。\n- **视觉能力双路由**：视觉模型直接核图；无视觉模型使用结构化来源、标题、正文引用、PDF 文字层或 OCR，并明确限制。\n- **结构化读者画像**：`domain × audience × goal × depth × delivery × language` 独立配置。\n- **多目标路由**：理解、审稿、复现、教学和跨领域迁移采用不同重点。\n- **全量视觉账本**：记录 Figure、Table、Algorithm、Scheme、Plate、Box 等编号对象及其核验状态。\n- **形式化零跳步**：解释承重公式、定义、定理、统计量或分析框架的目标、组成、研究位置和边界。\n- **主张—证据映射**：核心结论可回溯到实验、证明、观测、材料、案例或明确标注的推断。\n- **论文类型路由**：方法、理论、实证/观察、数据集/基准、系统、综述采用不同审查标准。\n- **跨学科 Lens**：覆盖计算机/AI、生物医学、物理/数学、化学/材料、工程、社会科学、地球环境与人文定性研究。\n- **CV 深度支持**：保留 backbon","tagline":"Cross-disciplinary, source-grounded paper deep-reading Agent Skill with vision/text-only routes, domain lenses, and evidence audits.","category":"research","commerce":{"type":"unknown","billing":"unknown","amount":null,"currency":null,"sourceUrl":null,"checkedAt":null,"runtime":"unknown","purchaseUrl":null,"checkout":"external","purchaseRequiresUserConsent":true},"tags":["paper-reading","agent-skill","deep-reading","evidence-audit","research"],"author":"Linwei-Chen","verified":false,"attribution":{"status":"community_indexed","statusLabel":"Community indexed","shortLabel":"COMMUNITY INDEXED","sourceLabel":"OpenAgentSkill auto-indexer","sourceDetail":"Linwei-Chen/paper-deep-reader-skill","creatorName":"Linwei-Chen","creatorUrl":"https://github.com/Linwei-Chen","sourceUrl":"https://github.com/Linwei-Chen/paper-deep-reader-skill","indexedBy":"OpenAgentSkill community index","claimUrl":"https://www.openagentskill.com/skills/linwei-chen-paper-deep-reader-skill#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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