xiaohongshu — for Claude Code xiaohongshu, oasis7, community, for Claude Code, ide skills, 未登录时需扫码:, 获取二维码,用小红书 App 扫码。 服务端口可通过, 环境变量覆盖(默认, )。 ## 核心数据流 **重要:** 大多数操作需要, search.sh <关键词>

v1.0.0

Sobre este Skill

Cenario recomendado: Ideal for AI agents that need 基于 xiaohongshu-mcp 封装的 shell 脚本工具集。. Resumo localizado: xiaohongshu helps AI agents handle repository-specific developer workflows with documented implementation details.

Recursos

基于 xiaohongshu-mcp 封装的 shell 脚本工具集。
./install-check.sh # 检查依赖(xiaohongshu-mcp、jq、python3)
./start-mcp.sh # 启动 MCP 服务(默认端口 18060)
./status.sh # 确认已登录
未登录时需扫码:mcp-call.sh get login qrcode 获取二维码,用小红书 App 扫码。

# Tópicos principais

eng-cc eng-cc
[2]
[0]
Atualizado: 4/23/2026

Skill Overview

Start with fit, limitations, and setup before diving into the repository.

Cenario recomendado: Ideal for AI agents that need 基于 xiaohongshu-mcp 封装的 shell 脚本工具集。. Resumo localizado: xiaohongshu helps AI agents handle repository-specific developer workflows with documented implementation details.

Por que usar essa habilidade

Recomendacao: xiaohongshu helps agents 基于 xiaohongshu-mcp 封装的 shell 脚本工具集。. xiaohongshu helps AI agents handle repository-specific developer workflows with documented implementation details.

Melhor para

Cenario recomendado: Ideal for AI agents that need 基于 xiaohongshu-mcp 封装的 shell 脚本工具集。.

Casos de Uso Práticos for xiaohongshu

Caso de uso: Applying 基于 xiaohongshu-mcp 封装的 shell 脚本工具集。
Caso de uso: Applying ./install-check.sh # 检查依赖(xiaohongshu-mcp、jq、python3)
Caso de uso: Applying ./start-mcp.sh # 启动 MCP 服务(默认端口 18060)

! Segurança e Limitações

  • Limitacao: Requires repository-specific context from the skill documentation
  • Limitacao: Works best when the underlying tools and dependencies are already configured

About The Source

The section below comes from the upstream repository. Use it as supporting material alongside the fit, use-case, and installation summary on this page.

Demo Labs

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

FAQ e etapas de instalação

These questions and steps mirror the structured data on this page for better search understanding.

? Perguntas frequentes

O que é xiaohongshu?

Cenario recomendado: Ideal for AI agents that need 基于 xiaohongshu-mcp 封装的 shell 脚本工具集。. Resumo localizado: xiaohongshu helps AI agents handle repository-specific developer workflows with documented implementation details.

Como instalar xiaohongshu?

Execute o comando: npx killer-skills add eng-cc/oasis7/xiaohongshu. Ele funciona com Cursor, Windsurf, VS Code, Claude Code e mais de 19 outros IDEs.

Quais são os casos de uso de xiaohongshu?

Os principais casos de uso incluem: Caso de uso: Applying 基于 xiaohongshu-mcp 封装的 shell 脚本工具集。, Caso de uso: Applying ./install-check.sh # 检查依赖(xiaohongshu-mcp、jq、python3), Caso de uso: Applying ./start-mcp.sh # 启动 MCP 服务(默认端口 18060).

Quais IDEs são compatíveis com xiaohongshu?

Esta skill é compatível com Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use a CLI do Killer-Skills para uma instalação unificada.

xiaohongshu tem limitações?

Limitacao: Requires repository-specific context from the skill documentation. Limitacao: Works best when the underlying tools and dependencies are already configured.

Como instalar este skill

  1. 1. Abra o terminal

    Abra o terminal ou linha de comando no diretório do projeto.

  2. 2. Execute o comando de instalação

    Execute: npx killer-skills add eng-cc/oasis7/xiaohongshu. A CLI detectará sua IDE ou agente automaticamente e configurará a skill.

  3. 3. Comece a usar o skill

    O skill já está ativo. Seu agente de IA pode usar xiaohongshu imediatamente no projeto atual.

! Source Notes

This page is still useful for installation and source reference. Before using it, compare the fit, limitations, and upstream repository notes above.

Upstream Repository Material

The section below comes from the upstream repository. Use it as supporting material alongside the fit, use-case, and installation summary on this page.

Upstream Source

xiaohongshu

Install xiaohongshu, an AI agent skill for AI agent workflows and automation. Explore features, use cases, limitations, and setup guidance.

SKILL.md
Readonly
Upstream Repository Material
The section below comes from the upstream repository. Use it as supporting material alongside the fit, use-case, and installation summary on this page.
Upstream Source

小红书 MCP Skill

基于 xiaohongshu-mcp 封装的 shell 脚本工具集。

前置条件

bash
1cd scripts/ 2./install-check.sh # 检查依赖(xiaohongshu-mcp、jq、python3) 3./start-mcp.sh # 启动 MCP 服务(默认端口 18060) 4./status.sh # 确认已登录

未登录时需扫码:mcp-call.sh get_login_qrcode 获取二维码,用小红书 App 扫码。

服务端口可通过 MCP_URL 环境变量覆盖(默认 http://localhost:18060/mcp)。

核心数据流

重要: 大多数操作需要 feed_id + xsec_token 配对。这两个值从搜索/推荐/用户主页结果中获取,不可自行构造

search_feeds / list_feeds / user_profile
        │
        ▼
  返回 feeds 数组,每个 feed 包含:
  ├── id          → 用作 feed_id
  ├── xsecToken   → 用作 xsec_token
  └── noteCard    → 标题、作者、封面、互动数据
        │
        ▼
  get_feed_detail(feed_id, xsec_token)
        │
        ▼
  返回完整笔记: 正文、图片列表、评论列表
  评论中包含 comment_id、user_id(用于回复评论)

脚本参考

脚本用途参数
search.sh <关键词>搜索笔记关键词
recommend.sh首页推荐
post-detail.sh <feed_id> <xsec_token>帖子详情+评论从搜索结果获取
comment.sh <feed_id> <xsec_token> <内容>发表评论从搜索结果获取
user-profile.sh <user_id> <xsec_token>用户主页+笔记从搜索结果获取
track-topic.sh <话题> [选项]热点分析报告--limit N --output file --feishu
export-long-image.sh帖子导出长图--posts-file json -o output.jpg
mcp-call.sh <tool> [json_args]通用 MCP 调用见下方工具表
start-mcp.sh启动服务--headless=false --port=N
stop-mcp.sh停止服务
status.sh检查登录
install-check.sh检查依赖

MCP 工具详细参数

search_feeds — 搜索笔记

json
1{"keyword": "咖啡", "filters": {"sort_by": "最新", "note_type": "图文", "publish_time": "一周内"}}

filters 可选字段:

  • sort_by: 综合 | 最新 | 最多点赞 | 最多评论 | 最多收藏
  • note_type: 不限 | 视频 | 图文
  • publish_time: 不限 | 一天内 | 一周内 | 半年内
  • search_scope: 不限 | 已看过 | 未看过 | 已关注
  • location: 不限 | 同城 | 附近

get_feed_detail — 帖子详情

json
1{"feed_id": "...", "xsec_token": "...", "load_all_comments": true, "limit": 20}
  • load_all_comments: false(默认) 返回前10条,true 滚动加载更多
  • limit: 加载评论上限(仅 load_all_comments=true 时生效),默认 20
  • click_more_replies: 是否展开二级回复,默认 false
  • reply_limit: 跳过回复数超过此值的评论,默认 10
  • scroll_speed: slow | normal | fast

post_comment_to_feed — 发表评论

json
1{"feed_id": "...", "xsec_token": "...", "content": "写得真好!"}

reply_comment_in_feed — 回复评论

json
1{"feed_id": "...", "xsec_token": "...", "content": "谢谢!", "comment_id": "...", "user_id": "..."}

comment_iduser_id 从 get_feed_detail 返回的评论列表中获取。

user_profile — 用户主页

json
1{"user_id": "...", "xsec_token": "..."}

user_id 从 feed 的 noteCard.user.userId 获取,xsec_token 使用该 feed 的 xsecToken

like_feed — 点赞/取消

json
1{"feed_id": "...", "xsec_token": "..."} 2{"feed_id": "...", "xsec_token": "...", "unlike": true}

favorite_feed — 收藏/取消

json
1{"feed_id": "...", "xsec_token": "..."} 2{"feed_id": "...", "xsec_token": "...", "unfavorite": true}

publish_content — 发布图文

json
1{"title": "标题(≤20字)", "content": "正文(≤1000字)", "images": ["/path/to/img.jpg"], "tags": ["美食","旅行"]}
  • images: 至少1张,支持本地路径或 HTTP URL
  • tags: 可选,话题标签
  • schedule_at: 可选,定时发布(ISO8601,1小时~14天内)

publish_with_video — 发布视频

json
1{"title": "标题", "content": "正文", "video": "/path/to/video.mp4"}

其他工具

工具参数说明
check_login_status检查登录状态
list_feeds获取首页推荐
get_login_qrcode获取登录二维码(Base64 PNG)
delete_cookies删除 cookies,重置登录

热点跟踪

自动搜索 → 拉取详情 → 生成 Markdown 报告。

bash
1./track-topic.sh "DeepSeek" --limit 5 2./track-topic.sh "春节旅游" --limit 10 --output report.md 3./track-topic.sh "iPhone 16" --limit 5 --feishu # 导出飞书

报告包含:概览统计、热帖详情(正文+热评)、评论关键词、趋势分析。

长图导出

将帖子导出为白底黑字的 JPG 长图。

bash
1./export-long-image.sh --posts-file posts.json -o output.jpg

posts.json 格式:

json
1[{ 2 "title": "标题", "author": "作者", "stats": "1.3万赞", 3 "desc": "正文摘要", "images": ["https://..."], 4 "per_image_text": {"1": "第2张图的说明"} 5}]

依赖:Python 3.10+、Pillow。

注意事项

  • Cookies 有效期约 30 天,过期需重新扫码
  • 首次启动会下载 headless 浏览器(~150MB)
  • 同一账号避免多客户端同时操作
  • 发布限制:标题≤20字符,正文≤1000字符,日发布≤50条
  • Linux 服务器无桌面环境需安装 xvfb(apt-get install xvfb,脚本自动管理)

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