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[板块A] → [板块B]：[过渡策略描述]\n2. [板块B] → [板块C]：[过渡策略描述]\n...\n\n## 三、关键节点\n- **创新点落点**：X 板块 / 作者通过 X 方式建立创新性\n- **实验核心落点**：X 板块 / 核心发现是什么\n- **核心结论落点**：X 板块 / 最关键的一句话结论\n\n## 四、每章干货\n- **Title + Abstract**：研究问题 + 核心结论（1-2句）\n- **Introduction**：为什么要做这个研究（1-2句）\n- **[其他板块]**：......（简洁提炼，不复述原文）\n\n## 五、逻辑可视化（可选）\n[Introduction → Literature Review → Hypotheses → Method → Results → Discussion] 的递进箭头图，标注每段的核心功能\n```\n\n---\n\n## 逻辑梳理步骤\n\n1. **识别文献类型**，匹配对应逻辑模板\n2. **划分章节边界**，找出每个板块的核心功能\n3. **串联递进关系**，标注相邻章节间的过渡策略\n4. **定位三个落点**：创新点 / 实验核心 / 核心结论\n5. **提炼每章干货**，控制字数，不复述原文\n6. **输出逻辑链**，简洁、可存档\n\n---\n\n## 核心原则\n\n- **递进 > 并列**：不孤立描述每个板块，要说清楚\"为什么先写A再写B\"\n- **创新点定位**：心理学/HCI/社科类论文通常在 Introduction 末尾或 Discussion 前半部建立创新性\n- **实验核心聚焦**：Results 部分只提炼最核心的1-3个发现，不做完整统计报告\n- **干货控制**：每章不超过3句话，避免写成缩写版原文\n- **回看友好**：输出结果应能在3个月内快速回忆起论文全貌\n\n---\n\n## 文献类型适配\n\n| 类型 | 逻辑特征 |\n|------|----------|\n| 实证论文 | 问题→假设→方法→结果→讨论，线性递进 |\n| 综述 | 文献量→归类→分歧→研究空白→研究问题 |\n| 理论论文 | 现有理论批评→新理论构建→论证→边界讨论 |\n| 元分析 | 方法→结果汇总→异质性分析→理论更新 |\n\n若文献类型不符标准结构，在输出开头注明实际格式来源。\n\nFile v1.0.0:_meta.json\n\n{\n  \"ownerId\": \"kn730fawpv2k2cex1rqekcs2ed8716xf\",\n  \"slug\": \"paper-logic\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1779151462650\n}\n\nFile v1.0.0:skill-card.md\n\n## Description:\n\nHelps users analyze academic papers by extracting the logical argument path, chapter-to-chapter progression, innovation points, core findings, and concise section takeaways.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[11xinshou](https://clawhub.ai/user/11xinshou)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nStudents, researchers, and knowledge workers use this skill to turn pasted paper text or approved local paper files into a concise logic-focused reading note. It emphasizes argument flow, transitions between sections, innovation points, key findings, and review-friendly takeaways.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Broad paper-analysis phrases may trigger the skill in contexts where the user did not intend a full paper-logic breakdown.\n\nMitigation: Confirm the desired analysis scope before applying the structured output to ambiguous requests.\n\nRisk: Users may paste sensitive, unpublished, or restricted paper text, or point the agent at local files containing such content.\n\nMitigation: Analyze only paper text or files the user is comfortable processing in the current agent session.\n\n## Reference(s):\n\n- [ClawHub skill page](https://clawhub.ai/11xinshou/skills/paper-logic)\n\n## Skill Output:\n\n**Output Type(s):** [Text, Markdown, Guidance]\n\n**Output Format:** [Markdown analysis note with headings, ordered lists, bullets, and optional arrow-style logic visualization]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Concise Chinese-language paper-logic summary intended for later review.]\n\n## Skill Version(s):\n\n1.0.0 (source: release evidence)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.","readmeExcerpt":"Skill: 文献结构逻辑要点提炼 Owner: 11xinshou Summary: 学术文献逻辑梳理专家。专注挖掘论文内部章节间的递进关系与论证脉络，输出逻辑链而非结构清单。触发场景：(1) 输入\"梳理逻辑\" (2) 输入\"分析论证脉络\" (3) 输入\"找出创新点\" (4) 输入\"理清论文思路\" (5) 要求\"逻辑拆解\"。 Tags: latest:1.0.0 Version history: v1.0.0 | 2026-05-19T00:44:22.650Z | user - Initial release of paper-logic. - 专注于学术论文内部逻辑链梳理，输出章节递进关系、创新点、核心结论等关键信息。 - 提供标准输出格式，包括摘要逻辑主线、递进过渡、创新与实验落点、每章干货和可视化逻辑链。 - 支持多种文献类型（实证、综述、理论、元分析），按类型自动适配分析方式。 - 保证输出精炼、复习友好，强调逻辑而","codeSnippets":[],"executableExamples":[{"language":"text","snippet":"# [论文标题] — 逻辑拆解\n\n## 一、逻辑主线\n[一句话概括论文从开头到结论的核心论证路径]\n\n## 二、章节递进关系\n1. [板块A] → [板块B]：[过渡策略描述]\n2. [板块B] → [板块C]：[过渡策略描述]\n...\n\n## 三、关键节点\n- **创新点落点**：X 板块 / 作者通过 X 方式建立创新性\n- **实验核心落点**：X 板块 / 核心发现是什么\n- **核心结论落点**：X 板块 / 最关键的一句话结论\n\n## 四、每章干货\n- **Title + Abstract**：研究问题 + 核心结论（1-2句）\n- **Introduction**：为什么要做这个研究（1-2句）\n- **[其他板块]**：......（简洁提炼，不复述原文）\n\n## 五、逻辑可视化（可选）\n[Introduction → Literature Review → Hypotheses → Method → Results → Discussion] 的递进箭头图，标注每段的核心功能"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: paper-logic\ndescription: 学术文献逻辑梳理专家。专注挖掘论文内部章节间的递进关系与论证脉络，输出逻辑链而非结构清单。触发场景：(1) 输入\"梳理逻辑\" (2) 输入\"分析论证脉络\" (3) 输入\"找出创新点\" (4) 输入\"理清论文思路\" (5) 要求\"逻辑拆解\"。\n---\n\n# Paper Logic\n\n## 功能\n\n深挖论文章节之间的内在逻辑：递进关系、过渡策略、创新点定位、核心干货提炼。输出逻辑链而非结构清单，便于复习回看。\n\n## 输入\n\n用户粘贴文献文本（或指定已有文件路径）。\n\n## 输出格式\n\n```\n# [论文标题] — 逻辑拆解\n\n## 一、逻辑主线\n[一句话概括论文从开头到结论的核心论证路径]\n\n## 二、章节递进关系\n1. [板块A] → [板块B]：[过渡策略描述]\n2. [板块B] → [板块C]：[过渡策略描述]\n...\n\n## 三、关键节点\n- **创新点落点**：X 板块 / 作者通过 X 方式建立创新性\n- **实验核心落点**：X 板块 / 核心发现是什么\n- **核心结论落点**：X 板块 / 最关键的一句话结论\n\n## 四、每章干货\n- **Title + Abstract**：研究问题 + 核心结论（1-2句）\n- **Introduction**：为什么要做这个研究（1-2句）\n- **[其他板块]**：......（简洁提炼，不复述原文）\n\n## 五、逻辑可视化（可选）\n[Introduction → Literature Review → Hypotheses → Method → Results → Discussion] 的递进箭头图，标注每段的核心功能\n```\n\n---\n\n## 逻辑梳理步骤\n\n1. **识别文献类型**，匹配对应逻辑模板\n2. **划分章节边界**，找出每个板块的核心功能\n3. **串联递进关系**，标注相邻章节间的过渡策略\n4. **定位三个落点**：创新点 / 实验核心 / 核心结论\n5. **提炼每章干货**，控制字数，不复述原文\n6. **输出逻辑链**，简洁、可存档\n\n---\n\n## 核心原则\n\n- **递进 > 并列**：不孤立描述每个板块，要说清楚\"为什么先写A再写B\"\n- **创新点定位**：心理学/HCI/社科类论文通常在 Introduction 末尾或 Discussion 前半部建立创新性\n- **实验核心聚焦**：Results 部分只提炼最核心的1-3个发现，不做完整统计报告\n- **干货控制**：每章不超过3句话，避免写成缩写版原文\n- **回看友好**：输出结果应能在3个月内快速回忆起论文全貌\n\n---\n\n## 文献类型适配\n\n| 类型 | 逻辑特征 |\n|------|----------|\n| 实证论文 | 问题→假设→方法→结果→讨论，线性递进 |\n| 综述 | 文献量→归类→分歧→研究空白→研究问题 |\n| 理论论文 | 现有理论批评→新理论构建→论证→边界讨论 |\n| 元分析 | 方法→结果汇总→异质性分析→理论更新 |\n\n若文献类型不符标准结构，在输出开头注明实际格式来源。"},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn730fawpv2k2cex1rqekcs2ed8716xf\",\n  \"slug\": \"paper-logic\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1779151462650\n}"},{"path":"skill-card.md","content":"## Description:\n\nHelps users analyze academic papers by extracting the logical argument path, chapter-to-chapter progression, innovation points, core findings, and concise section takeaways.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[11xinshou](https://clawhub.ai/user/11xinshou)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nStudents, researchers, and knowledge workers use this skill to turn pasted paper text or approved local paper files into a concise logic-focused reading note. It emphasizes argument flow, transitions between sections, innovation points, key findings, and review-friendly takeaways.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Broad paper-analysis phrases may trigger the skill in contexts where the user did not intend a full paper-logic breakdown.\n\nMitigation: Confirm the desired analysis scope before applying the structured output to ambiguous requests.\n\nRisk: Users may paste sensitive, unpublished, or restricted paper text, or point the agent at local files containing such content.\n\nMitigation: Analyze only paper text or files the user is comfortable processing in the current agent session.\n\n## Reference(s):\n\n- [ClawHub skill page](https://clawhub.ai/11xinshou/skills/paper-logic)\n\n## Skill Output:\n\n**Output Type(s):** [Text, Markdown, Guidance]\n\n**Output Format:** [Markdown analysis note with headings, ordered lists, bullets, and optional arrow-style logic visualization]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Concise Chinese-language paper-logic summary intended for later review.]\n\n## Skill Version(s):\n\n1.0.0 (source: release evidence)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment."}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"学术文献逻辑梳理专家。专注挖掘论文内部章节间的递进关系与论证脉络，输出逻辑链而非结构清单。触发场景：(1) 输入\"梳理逻辑\" (2) 输入\"分析论证脉络\" (3) 输入\"找出创新点\" (4) 输入\"理清论文思路\" (5) 要求\"逻辑拆解\"。 Skill: 文献结构逻辑要点提炼 Owner: 11xinshou Summary: 学术文献逻辑梳理专家。专注挖掘论文内部章节间的递进关系与论证脉络，输出逻辑链而非结构清单。触发场景：(1) 输入\"梳理逻辑\" (2) 输入\"分析论证脉络\" (3) 输入\"找出创新点\" (4) 输入\"理清论文思路\" (5) 要求\"逻辑拆解\"。 Tags: latest:1.0.0 Version history: v1.0.0 | 2026-05-19T00:44:22.650Z | user - Initial release of paper-logic. - 专注于学术论文内部逻辑链梳理，输出章节递进关系、创新点、核心结论等关键信息。 - 提供标准输出格式，包括摘要逻辑主线、递进过渡、创新与实验落点、每章干货和可视化逻辑链。 - 支持多种文献类型（实证、综述、理论、元分析），按类型自动适配分析方式。 - 保证输出精炼、复习友好，强调逻辑而","editorialQuality":{"score":100,"threshold":65,"status":"ready","wordCount":572,"uniquenessScore":69,"reasons":[]}},"media":{"evidence":{"source":"no-media","verified":false,"confidence":"low","updatedAt":"2026-10-12T00:25:27.713Z","emptyReason":"No screenshots, media assets, or demo links are available."},"primaryImageUrl":null,"mediaAssetCount":0,"assets":[],"demoUrl":null},"ownerResources":{"evidence":{"source":"unclaimed","verified":false,"confidence":"low","updatedAt":"2026-10-12T00:25:27.713Z","emptyReason":"This page has not been claimed by the agent owner."},"hasCustomPage":false,"customPageUpdatedAt":null,"customLinks":[],"structuredLinks":{"docsUrl":null,"demoUrl":null,"supportUrl":null,"pricingUrl":null,"statusUrl":null},"customPage":null},"relatedAgents":{"evidence":{"source":"protocol-neighbors","verified":false,"confidence":"medium","updatedAt":"2026-10-12T15:41:24.129Z","emptyReason":null},"items":[{"id":"8ebccd8e-3863-4187-8355-c3f14e1f9edf","entityType":"agent","canonicalPath":"/agent/iofficeai-aionui","slug":"iofficeai-aionui","name":"AionUi","description":"Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | 🌟 Star if you like it!","url":"https://github.com/iOfficeAI/AionUi","homepage":"https://www.aionui.com","source":"GITHUB_REPOS","protocols":["MCP","OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-10-09T19:11:12.944Z","createdAt":"2026-02-25T03:38:16.584Z","downloads":null},{"id":"b917f68a-ebff-438e-84f8-3f4b2494c0bc","entityType":"agent","canonicalPath":"/agent/activepieces-activepieces","slug":"activepieces-activepieces","name":"activepieces","description":"AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents","url":"https://github.com/activepieces/activepieces","homepage":"https://www.activepieces.com","source":"GITHUB_REPOS","protocols":["OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-04-15T02:22:12.426Z","createdAt":"2026-02-25T03:38:12.412Z","downloads":null},{"id":"5cb26759-3a39-483f-94cf-276a98c13bb8","entityType":"agent","canonicalPath":"/agent/cherryhq-cherry-studio","slug":"cherryhq-cherry-studio","name":"cherry-studio","description":"AI productivity studio with smart chat, autonomous agents, and 300+ assistants. 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