xhs-content
XiaoHongShu (小红书) content creation tool. Collects trending XHS notes by keyword, generates a topic research report, then produces XHS-style note drafts. Use when the user wants to: (1) research what's trending on XiaoHongShu/小红书 for a topic, (2) generate XHS note drafts, (3) find XHS content inspiration, or mentions "小红书", "XHS", "xiaohongshu", "xhs content", "xhs note".
Source facts
- Repository
- mindfold-ai/xhs-content
- Last source activity
- March 11, 2026 at 06:49
- Detected SKILL.md language
- Mixed languages
- Stars
- 15
- Forks
- 1
Install options
The review-first prompt is selected by default. You can switch to a direct command or download a local copy.
Review the source files
Read SKILL.md and any companion files shown by SkillsMP before deciding whether to install.
Showing SKILL.md
SKILL.md
Source instructions · Read-only preview- name
- xhs-content
- description
- XiaoHongShu (小红书) content creation tool. Collects trending XHS notes by keyword, generates a topic research report, then produces XHS-style note drafts. Use when the user wants to: (1) research what's trending on XiaoHongShu/小红书 for a topic, (2) generate XHS note drafts, (3) find XHS content inspiration, or mentions "小红书", "XHS", "xiaohongshu", "xhs content", "xhs note".
# XHS Content — 小红书选题采集 + 内容生成
从小红书采集热门笔记 → 汇总选题报告 → 生成笔记草稿。
## Prerequisites
- **Python 3** — 运行采集脚本
- **xhs-cli** — 小红书 CLI 工具 (`pipx install xhs-cli` 或 `uv tool install xhs-cli`)
- 已登录 (`xhs login` — 支持 QR 码 / cookie / Chrome 自动提取)
## Usage
给定一个关键词，执行三步流程：
### Step 1: 采集
```bash
python3 scripts/xhs-search.py --keyword "<关键词>" --limit 30
```
采集结果自动保存到 `data/<date>-<keyword>/sources.jsonl`。
### Step 2: 汇总报告
读取 `sources.jsonl`，生成选题报告 `data/<date>-<keyword>/report.md`，包含：
- 话题热度概览（总笔记数、平均互动）
- 热门角度分类（按标题/内容聚类）
- Top 笔记摘要（标题、互动数据、核心观点）
- 选题建议（基于数据的差异化切入点）
- 全部采集笔记表格（标题、作者、赞/藏/评、链接，按互动量降序）
### Step 3: 生成笔记
基于报告 + 固定 prompt，生成 1-3 篇小红书笔记草稿，保存到 `data/<date>-<keyword>/drafts.md`。
## Workflow (for Claude)
When this skill is triggered:
1. Ask user for keyword (if not provided)
2. Run `xhs-search.py` to collect posts → save JSONL
3. Read JSONL, generate report markdown
4. Read report + `references/prompt.md`, generate note drafts
5. Present drafts to user for review
## Data Directory
All outputs go to `.claude/skills/xhs-content/data/<date>-<keyword>/`:
- `sources.jsonl` — raw collected posts
- `report.md` — topic research report
- `drafts.md` — generated note drafts
## References
- `references/prompt.md` — Fixed generation prompt with XHS style rules
View on GitHub