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Agent Skill · 研究检索

xiaohongshu-search-summarizer(小红书搜索总结器)

xiaohongshu-search-summarizer 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

xiaohongshu-search-summarizer 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为操作浏览器;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 1,283。

总安装 102,829周安装 4,372GitHub Stars 7下载 36,025核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.3

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:xiaohongshu-search-summarizer(小红书搜索总结器)
来源仓库:https://github.com/piekill/xiaohongshu-search-summarizer
安装命令:
openclaw skills install xiaohongshu-search-summarizer
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install xiaohongshu-search-summarizer

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

小红书搜索总结器能快速检索关键词相关帖子并生成综合摘要。适合用于热点追踪、竞品分析及内容趋势调研等场景。xiaohongshu-search-summarizer 属于研究检索类 Skill,可作为该场景下的辅助能力补充。支持提取前 N 条结果的文字、图片与评论信息进行整合评分。

需设置搜索关键词与数量限制,注意结果排序可能受算法影响。输出内容仅供参考,实际决策应结合多源信息交叉验证。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install xiaohongshu-search-summarizer;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要操作浏览器,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

xiaohongshu-search-summarizer 怎么安装?

在终端执行 openclaw skills install xiaohongshu-search-summarizer。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

xiaohongshu-search-summarizer 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

xiaohongshu-search-summarizer 需要什么权限?

权限级别是操作浏览器。该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

xiaohongshu-search-summarizer 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

xiaohongshu-search-summarizer 用的人多吗?

公开数据是累计安装 102,829 次,近一周安装 4,372 次,下载 36,025 次,GitHub 7 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

xiaohongshu-search-summarizer 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
xiaohongshu-search-summarizer

小红书搜索总结器 · 本页

102,8297操作浏览器MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
xiaohongshu-search-summarizer
description
Searches Xiaohongshu(小红书) for a given keyword, extracts the top N posts (including texts, images, and user comments), and then synthesizes a comprehensive final analytical report. Use this skill whenever the user wants to search Xiaohongshu for a specific topic, compile research seamlessly combining text and images, or needs an aggregated thematic summary of social media posts, comments, and visual data on a given subject.
compatibility
Requires playwright-cli and python3, and the Python 'requests' package installed.

Xiaohongshu Search and Summarize

This skill automates the process of extracting high-quality multi-modal content (text + images) from Xiaohongshu (小红书) and actively assists you in generating a deeply integrated, analytical final report for the user. Due to Xiaohongshu's aggressive anti-scraping mechanisms, direct HTTP requests or naive scraping often result in 404s or blocks. This skill natively bypasses these by simulating a real user through the playwright-cli in a headed browser window.

It operates in two distinct phases:

Phase 1: Subagent Data Collection

  1. Simulate a search for the keyword on Xiaohongshu in a headed browser.
  2. Advance through image sliders to fully load all lazy pictures from the top N posts.
  3. Extract titles, descriptions, top comments, and all high-resolution images.
  4. Download those images to a local directory and generate a raw data document ([keyword]_raw_data.md).

Phase 2: AI Multi-Modal Synthesis (Your Job)

  1. You MUST use your file reading capabilities to read the [keyword]_raw_data.md file.
  2. Inside the raw data markdown, you will find paths to image files. You MUST use your file reading / vision capabilities on these image file paths to actually ingest and "see" their visual content. If you skip this step, you are only reading file names, not the images themselves!
  3. You analyze the texts, summarize the genuinely useful comments (discarding noise like "pm me"), and interpret the semantic content of the images you just viewed (e.g. diagrams, guidelines, step-by-step UI flows).
  4. You compile everything into a beautifully synthesized, single comprehensive report rather than just a linear list of posts.

Dependencies

  • playwright-cli (Must be available on the path)
  • python3 (Required to download images and stitch the raw data markdown)
  • requests Python package (pip install requests) — used by parse.py to download images

Usage Instructions

Step 1: Run the Extraction Script

Execute the wrapper script in scripts/run.sh. It accepts the following arguments:

/bin/bash <skill_dir>/scripts/run.sh "YOUR KEYWORD" <MAX_POSTS> <OUTPUT_DIRECTORY>
  • YOUR KEYWORD: The search term to look up on Xiaohongshu.
  • <MAX_POSTS>: (Optional, default = 10) The number of top posts to scan.
  • <OUTPUT_DIRECTORY>: (Optional, default = ./) Directory where the raw data and images will be saved.

Example execution:

/bin/bash ~/.claude/skills/xiaohongshu-search-summarizer/scripts/run.sh "openclaw使用场景" 10 "./xhs_report_openclaw_scenarios"

Step 2: Read Raw Data & Images

Once the bash script finishes successfully, navigate to the OUTPUT_DIRECTORY and use your file reading capabilities to ingest the generated [keyword]_raw_data.md file.

Inside this file, you will find descriptions, comments, and file paths pointing to post_X_img_Y.webp or post_X_img_Y.jpg.

Step 3: Synthesis & Summarization

This is the most critical step. Do not just return the raw markdown file to the user. Instead, write a polished comprehensive markdown report that reorganizes the information logically, while retaining a high level of detail.

Follow these strict compilation rules:

  • Do not list posts individually (e.g. avoid "Post 1: ... Post 2: ...").
  • Read the Images: You MUST use your file reading and vision capabilities on the .webp or .jpg image files found in the raw data directory to interpret their contents.
  • Detailed & Comprehensive Synthesis: Provide a highly detailed summary that includes diverse viewpoints, nuances, and specific examples found across different posts. Avoid over-summarizing or losing important context; preserve the richness and diversity of the information.
  • Extract and merge themes: Group ideas by concepts, steps, recurring themes, or pros/cons.
  • Evaluate comments: Merge insights from valuable comments directly into the core narrative. Skip useless or repetitive comments, but preserve diverse opinions or helpful counter-arguments from the comments section.
  • Integrate images contextually: Embed the most relevant and high-quality images directly into the flow of your final report to support the analytical points being made. Describe their visual meaning based on what you saw with your vision capabilities.
  • Save to OUTPUT_DIRECTORY: Save your beautifully compiled final Markdown report using your file writing capabilities directly into the same <OUTPUT_DIRECTORY> as the raw data (e.g., <OUTPUT_DIRECTORY>/[keyword]_synthesis.md), and give the user the path to it.

Error Handling

If you encounter 404 Not Found or "element not visible" errors during the browser invocation:

  • Keep in mind that Xiaohongshu may demand a login challenge. If the site pauses waiting for a login, instruct the user to verify the playwright-cli browser window and perform necessary authentication manually, then try the script again.

平台分布

OpenClaw

75.11%
按下载量换算27,058

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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