Install
$ agentstack add skill-chubbyguan-chubbyskills-xiaohongshu-ingest ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
小红书采集 + 爆款拆解 Skill
把小红书笔记抓成结构化 Markdown 入库,再用 DeepSeek 拆解爆款逻辑、衍生可直接写的选题。 自动区分图文与视频笔记:图文 → 下载图片本地嵌入;视频 → 像抖音那样转成文字稿。 补齐了仓库在「中文内容采集」上最大的平台缺口,也是「采集 → 选题」创作工作流的起点。
环境要求
# 图文采集零依赖(仅 Python 标准库)
# 视频笔记转录需要(与抖音/B站转录同一套依赖):
pip install funasr modelscope torch torchaudio
# macOS: brew install ffmpeg | Ubuntu: sudo apt install ffmpeg
# 强烈建议:配置 cookie 以规避风控(从浏览器登录后复制)
export XHS_COOKIE="你的小红书 cookie 字符串"
# 爆款拆解需要 DeepSeek API Key
export DEEPSEEK_API_KEY="your-api-key"
使用方法
# 1) 采集笔记 → 统一 frontmatter Markdown
python scripts/fetch_note.py "https://www.xiaohongshu.com/explore/xxxx" -o ./out
python scripts/fetch_note.py "http://xhslink.com/xxxx" -o ./out
python scripts/fetch_note.py "链接" -o ./out --no-images # 图文:只留图片链接不下载
python scripts/fetch_note.py "链接" -o ./out --no-video # 视频:不转录,只留视频链接
# 2) 爆款拆解 → 拆解报告 + 5 条衍生选题
python scripts/analyze_hook.py ./out/某笔记.md -o ./out
产出
fetch_note.py:标题、正文、标签、作者、互动数据(赞/藏/评)→ 统一 frontmatter Markdown(platform: xiaohongshu,含note_type: image|video)。按笔记类型分流:- 图文笔记:图片下载到本地
.assets/并以 `嵌入(正文常在图里,本地化后 Obsidian 直接可见)。--no-images` 只留链接,单张失败自动回退为链接 - 视频笔记:提取视频直链 → ffmpeg 抽音频 → SenseVoice 转录为文字稿写入
## 视频文字稿。--no-video只留视频链接;缺 funasr/ffmpeg 时自动降级为存链接 analyze_hook.py:目标人群 / 场景 / 痛点 / 情绪价值 / 标题钩子 / 正文结构 / 可复用模板 / 5 条衍生选题(带- [ ]勾选,可直接进选题库)
⚠️ 关于反爬(务必先读)
小红书风控严格,未登录的纯脚本访问经常被拦。本 skill 的策略:
- 优先解析页面内嵌的
__INITIAL_STATE__结构化数据 - 失败则回退到
og:元标签 - 仍失败时给出明确提示——此时可手动复制笔记正文存成
.txt,直接走analyze_hook.py拆解(拆解环节不依赖抓取)
提供 XHS_COOKIE 能显著提高采集成功率。页面结构若调整,需更新 fetch_note.py 里的选择器。
合规声明
仅供个人学习与研究使用。请遵守小红书用户协议与 robots 规则,控制请求频率,不要用于批量抓取、商用爬取或侵犯他人权益的场景。
衔接工作流
- 采集产物 →
knowledge-base-management入库(统一 frontmatter,按platform聚合) - 衍生选题 → 创作者选题流(采集 → 拆解 → 选题库 → 初稿)
- 多篇拆解 → 沉淀「爆款选题库」,对接
industry-intelligence-radar的趋势信号
参考
- 小红书爆款方法论:人群 × 场景 × 痛点 + 情绪价值
- DeepSeek — 拆解用 LLM
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: chubbyguan
- Source: chubbyguan/chubbyskills
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.