get-review-theme
从任意输入源提取结构化综述主题：支持文件（PDF/Word/Markdown/Tex）、文件夹、图片、自然语言描述、网页 URL 等；自动识别输入类型并提取内容；生成"主题+关键词+核心问题"结构化输出，可直接用于 systematic-literature-review 及其他文献综述技能。
Install
mkdir -p .claude/skills/get-review-theme && curl -L -o skill.zip "https://mcp.directory/api/skills/download/8451" && unzip -o skill.zip -d .claude/skills/get-review-theme && rm skill.zipInstalls to .claude/skills/get-review-theme
About this skill
Get Review Theme
与 bensz-collect-bugs 的协作约定
- 当用户环境中出现因本 skill 设计缺陷导致的 bug 时，优先使用
bensz-collect-bugs按规范记录到~/.bensz-skills/bugs/，严禁直接修改用户本地 Claude Code / Codex 中已安装的 skill 源码。 - 若 AI 仍可通过 workaround 继续完成用户任务，应先记录 bug，再继续完成当前任务。
- 当用户明确要求“report bensz skills bugs”等公开上报动作时，调用本地
gh与bensz-collect-bugs，仅上传新增 bug 到huangwb8/bensz-bugs；不要 pull / clone 整个 bug 仓库。
定位
- 从文件、图片、网页、文件夹或自然语言描述中提取结构化综述主题。
- 输出直接服务
systematic-literature-review或其他文献综述工作流。 - 最高原则：主题要可操作、关键词要能检索、核心问题要具体。
输入
必需：
{输入源}：文件路径、URL、文件夹路径、图片路径，或直接文本描述
可选：
{输出格式}：text/yaml/json，默认text
输出
始终包含三项：
主题关键词核心问题
格式由用户选择：
textyamljson
工作流
1. 识别输入类型
- 自然语言描述
- 图片
- URL
- 文本文件
- Word
- 文件夹
2. 提取内容
- 自然语言：直接使用
- 图片：依赖 LLM 原生视觉能力
- URL：优先网页读取工具，失败则请用户提供正文
- 文本 / PDF / Word：直接读取
- 文件夹：递归扫描并合并
.md/.txt/.pdf等核心材料
原则：
- 优先用宿主原生能力和现有标准工具
- 工具不可用时优雅降级，不额外引入脚本依赖
3. 语义提取
围绕以下任务输出：
- 用一句话概括主题
- 提取 5-10 个英文标准术语
- 提取 2-5 个具体研究问题或挑战
4. 格式化
text：适合直接复制给下游 skillyaml/json：适合结构化衔接
质量要求
- 主题要包含研究对象与核心问题或方法
- 关键词优先用标准检索术语
- 核心问题必须具体，避免“意义重大/挑战很多”这种空话
错误处理
- 文件不存在：提示用户改路径或直接粘贴内容
- 格式不支持：提示转换
- 内容提取失败：让用户手动提供文本
- URL 解析失败：让用户复制网页正文或提供 PDF
- 图片语义不清：请用户补一句描述
与下游技能的关系
topic可直接喂给systematic-literature-reviewkeywords可补充检索策略core_questions可作为综述边界和纳排参考
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