analyze-xiaohongshu

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原始内容

---
name: analyze-xiaohongshu
description: Analyze a Xiaohongshu (小红书/RED) account by scraping its notes and covers, then generating a comprehensive methodology report across 7 dimensions. Use when the user provides a Xiaohongshu account URL or user ID and asks to analyze the account, reverse-engineer its strategy, or generate a methodology report. Triggers on phrases like "分析小红书账号", "爬取小红书", "analyze xiaohongshu account", "生成账号方法论报告".
---

# analyze-xiaohongshu

Scrape a Xiaohongshu account's notes and cover images, then produce a structured methodology report.

## Workflow

### Step 1: Parse input

Accept either format:
- Full URL: `https://www.xiaohongshu.com/user/profile/<ID>`
- Raw user ID string

Extract the account ID using the pattern `/user/profile/([a-zA-Z0-9]+)`.

### Step 2: Environment check

```bash
python3 --version          # must be 3.8+
pip3 show playwright       # check if installed
```

If playwright is missing:
```bash
pip3 install playwright && playwright install chromium
```

### Step 3: Run the scraper

The scraper is at `scripts/scraper.py` (relative to this skill directory). Find its absolute path first.

```bash
python3 <skill_dir>/scripts/scraper.py <account_url_or_id>
```

**Behavior:**
- Opens a visible Chromium browser
- If no saved cookie exists or cookie is expired → waits for user to manually scan QR code and log in (up to 3 minutes)
- Saves cookie to `data/cookies.json` for reuse
- Scrolls the profile page to collect up to 100 note cards — **no detail page visits**
- For each card, extracts: note ID, URL, title, cover image URL, like count, content type (video/image)
- Downloads cover images to `covers/`
- Supports resume: skips notes already in `data/<account_id>/notes.json`
- Saves data to `data/<account_id>/` inside the skill directory

**Output paths:**
```
<skill_dir>/data/
├── cookies.json
└── <account_id>/
    ├── profile.json
    ├── notes.json        # fields: note_id, url, title, cover_url, likes, type, cover_local_path
    └── covers/
        └── <note_id>.jpg
```

### Step 4: Load data

After the scraper finishes, read the JSON files:

```python
import json
profile = json.load(open("data/<account_id>/profile.json"))
notes   = json.load(open("data/<account_id>/notes.json"))
```

### Step 5: Analyze covers (batched visual analysis)

Read cover images in batches of 5–10 using the Read tool (images). Prioritize covers from the top 20% notes by likes first, then the rest for comparison.

Engagement score per note = `likes` (only metric available from profile cards)

### Step 6: Generate the report

Write a Markdown report to `data/<account_id>/report.md`. Include all 7 dimensions below.

### Step 6.5: Generate analysis.json

After the report, generate `data/<account_id>/analysis.json` with structured data extracted from your analysis. The dashboard (`scripts/dashboard.py`) **requires** this file.

```json
{
  "account_id": "<account_id>",
  "generated_at": "YYYY-MM-DD",
  "topic_categories": {
    "<category_name>": ["keyword1", "keyword2", ...]
  },
  "title_formulas": [
    {
      "name": "公式名称",
      "pattern": "regex_pattern",
      "template": "模板描述",
      "trigger": "触发机制",
      "color": "#hex",
      "examples": ["示例标题1", "示例标题2"]
    }
  ],
  "cloud_keywords": ["word1", "word2", ...],
  "radar_dimensions": ["收藏潜力", "分享潜力", "时效性", "专业门槛", "变现潜力"],
  "radar_scores": {
    "<category_name>": [0-100, 0-100, 0-100, 0-100, 0-100]
  },
  "strategies": [
    {
      "title": "策略标题",
      "body": "策略详细说明",
      "action": "具体行动建议"
    }
  ],
  "top10_note_ids": ["8-char-prefix", ...],
  "top10_analysis": [
    { "note_id_prefix": "8-char-prefix", "success_factors": "该笔记的成功因素拆解" }
  ],
  "hit_patterns": [
    "爆款共性总结（如：同一公式贡献X%总赞）"
  ],
  "title_hit_vs_miss": {
    "dimensions": ["对比维度1", "对比维度2", ...],
    "hit": ["爆款特征1", "爆款特征2", ...],
    "miss": ["普通特征1", "普通特征2", ...]
  },
  "cover_analysis": {
    "styles": [
      { "name": "风格名", "count": 4, "traits": "风格特征描述", "performance": "high|medium|mixed|low" }
    ],
    "hit_cover_patterns": ["爆款封面共性1", "爆款封面共性2"],
    "template_suggestion": "推荐的封面模板描述"
  },
  "category_insights": {
    "<category>": "该类别的定性分析（优势、弱点、建议）"
  },
  "format_comparison": {
    "video_avg_likes": 0,
    "image_avg_likes": 0,
    "video_total_likes_pct": 0,
    "insight": "视频vs图文总结",
    "per_category": [
      { "category": "类别名", "best_format": "视频|图文|均可", "reason": "原因" }
    ]
  },
  "content_formulas_recap": [
    { "label": "A 公式名", "template": "公式模板" }
  ],
  "action_plan": {
    "this_week": ["本周行动1", "本周行动2"],
    "this_month": ["本月行动1"],
    "long_term": ["长期方向1"]
  }
}
```

**Field guidelines:**
- `topic_categories`: 3-6 categories with 5-20 keywords each, derived from the account's actual content themes
- `title_formulas`: 4-8 formulas with valid regex patterns, derived from the account's title patterns
- `cloud_keywords`: 20-40 high-frequency keywords from the account's titles
- `radar_scores`: One entry per topic category, scores 0-100 for each of the 5 radar dimensions
- `strategies`: 3-5 actionable strategies from the report's dimension 7
- `top10_note_ids`: First 8 characters of the top 10 notes' IDs (by likes)
- `top10_analysis`: Per-note success factor breakdown for each top 10 note, matched by `note_id_prefix`
- `hit_patterns`: 1-3 bullet points summarizing the key patterns shared by hit content
- `title_hit_vs_miss`: Multi-dimension comparison table of hit vs miss title characteristics
- `cover_analysis`: Cover style distribution, hit cover patterns, and template suggestion
- `category_insights`: One qualitative paragraph per topic category (strengths, weaknesses, recommendations)
- `format_comparison`: Video vs image performance comparison with per-category recommendations
- `content_formulas_recap`: 3-5 quick-reference content formulas (label + template)
- `action_plan`: Time-bound action items split into `this_week`, `this_month`, `long_term`

### Step 7: Generate the dashboard

Run the dashboard generator to produce an interactive HTML dashboard:

```bash
python3 <skill_dir>/scripts/dashboard.py <account_id>
```

This reads `data/<account_id>/analysis.json` (required) along with `profile.json` and `notes.json`, and outputs `data/<account_id>/dashboard.html`.

Note on data availability: the scraper collects card-level data only (title, likes, cover, type). Hashtags and publish dates are not visible on profile cards and are excluded.

---

## Report Structure (7 Dimensions)

### 1. 账号概览
- 昵称、简介、粉丝量级、内容类型定位
- 核心统计：总笔记数、平均点赞、点赞中位数
- 视频 vs 图文比例

### 2. 爆款笔记分析（Top 10）
- 按点赞数降序排列
- 每篇列出：标题、点赞数、内容类型、成功因素拆解
- 爆款定义：点赞量排名前 20% 的笔记

### 3. 标题分析
- 标题长度分布与最佳长度区间
- 高频词和句式模式（如"xx个方法"、"千万别xx"、提问式等）
- 爆款 vs 普通标题的差异对比
- 可复用的标题公式总结（至少 3 个）

### 4. 封面图分析（视觉分析）
- 风格类型分布（实拍 / 设计图 / 截图 / 对比图等）
- 配色特征、文字使用规律、构图规律
- 爆款封面的共性特征
- 可复用的封面模板建议

### 5. 内容选题分析
- 主题分类及各类占比（基于标题文本聚类）
- 各类别的平均点赞表现
- 最受欢迎的选题方向

### 6. 内容形式分析
- 图文 vs 视频的比例与点赞表现对比
- 哪种形式更受该账号受众欢迎

### 7. 可复制的方法论总结
- 该账号的核心增长策略（3-5条）
- 可直接复用的内容公式
- 具体行动建议清单（可执行的 to-do list）

---

## Error Handling

- **Scraper gets no notes**: Check that `section.note-item` elements exist on the profile page. If not, the scraper falls back to `a[href*="/explore/"]` links.
- **Cookie expired**: Delete `data/cookies.json` and rerun the scraper to trigger a fresh login.
- **Partial data**: If the scraper was interrupted, rerun it — it will skip already-downloaded covers.
- **Missing cover images**: Proceed with analysis using available covers; note which notes lack covers.
- **Anti-bot detection**: The scraper stays on the profile page only. If detection still occurs, increase `DELAY_MIN`/`DELAY_MAX` in `scraper.py`.

name: analyze-xiaohongshu description: Analyze a Xiaohongshu (小红书/RED) account by scraping its notes and covers, then generating a comprehensive methodology report across 7 dimensions. Use when the user provides a Xiaohongshu account URL or user ID and asks to analyze the account, reverse-engineer its strategy, or generate a methodology report. Triggers on phrases like "分析小红书账号", "爬取小红书", "analyze xiaohongshu account", "生成账号方法论报告".

analyze-xiaohongshu

Scrape a Xiaohongshu account's notes and cover images, then produce a structured methodology report.

Workflow

Step 1: Parse input

Accept either format:

Full URL: https://www.xiaohongshu.com/user/profile/<ID>
Raw user ID string

Extract the account ID using the pattern /user/profile/([a-zA-Z0-9]+).

Step 2: Environment check

python3 --version          # must be 3.8+
pip3 show playwright       # check if installed

If playwright is missing:

pip3 install playwright && playwright install chromium

Step 3: Run the scraper

The scraper is at scripts/scraper.py (relative to this skill directory). Find its absolute path first.

python3 <skill_dir>/scripts/scraper.py <account_url_or_id>

Behavior:

Opens a visible Chromium browser
If no saved cookie exists or cookie is expired → waits for user to manually scan QR code and log in (up to 3 minutes)
Saves cookie to data/cookies.json for reuse
Scrolls the profile page to collect up to 100 note cards — no detail page visits
For each card, extracts: note ID, URL, title, cover image URL, like count, content type (video/image)
Downloads cover images to covers/
Supports resume: skips notes already in data/<account_id>/notes.json
Saves data to data/<account_id>/ inside the skill directory

Output paths:

<skill_dir>/data/
├── cookies.json
└── <account_id>/
    ├── profile.json
    ├── notes.json        # fields: note_id, url, title, cover_url, likes, type, cover_local_path
    └── covers/
        └── <note_id>.jpg

Step 4: Load data

After the scraper finishes, read the JSON files:

import json
profile = json.load(open("data/<account_id>/profile.json"))
notes   = json.load(open("data/<account_id>/notes.json"))

Step 5: Analyze covers (batched visual analysis)

Read cover images in batches of 5–10 using the Read tool (images). Prioritize covers from the top 20% notes by likes first, then the rest for comparison.

Engagement score per note = likes (only metric available from profile cards)

Step 6: Generate the report

Write a Markdown report to data/<account_id>/report.md. Include all 7 dimensions below.

Step 6.5: Generate analysis.json

After the report, generate data/<account_id>/analysis.json with structured data extracted from your analysis. The dashboard (scripts/dashboard.py) requires this file.

{
  "account_id": "<account_id>",
  "generated_at": "YYYY-MM-DD",
  "topic_categories": {
    "<category_name>": ["keyword1", "keyword2", ...]
  },
  "title_formulas": [
    {
      "name": "公式名称",
      "pattern": "regex_pattern",
      "template": "模板描述",
      "trigger": "触发机制",
      "color": "#hex",
      "examples": ["示例标题1", "示例标题2"]
    }
  ],
  "cloud_keywords": ["word1", "word2", ...],
  "radar_dimensions": ["收藏潜力", "分享潜力", "时效性", "专业门槛", "变现潜力"],
  "radar_scores": {
    "<category_name>": [0-100, 0-100, 0-100, 0-100, 0-100]
  },
  "strategies": [
    {
      "title": "策略标题",
      "body": "策略详细说明",
      "action": "具体行动建议"
    }
  ],
  "top10_note_ids": ["8-char-prefix", ...],
  "top10_analysis": [
    { "note_id_prefix": "8-char-prefix", "success_factors": "该笔记的成功因素拆解" }
  ],
  "hit_patterns": [
    "爆款共性总结（如：同一公式贡献X%总赞）"
  ],
  "title_hit_vs_miss": {
    "dimensions": ["对比维度1", "对比维度2", ...],
    "hit": ["爆款特征1", "爆款特征2", ...],
    "miss": ["普通特征1", "普通特征2", ...]
  },
  "cover_analysis": {
    "styles": [
      { "name": "风格名", "count": 4, "traits": "风格特征描述", "performance": "high|medium|mixed|low" }
    ],
    "hit_cover_patterns": ["爆款封面共性1", "爆款封面共性2"],
    "template_suggestion": "推荐的封面模板描述"
  },
  "category_insights": {
    "<category>": "该类别的定性分析（优势、弱点、建议）"
  },
  "format_comparison": {
    "video_avg_likes": 0,
    "image_avg_likes": 0,
    "video_total_likes_pct": 0,
    "insight": "视频vs图文总结",
    "per_category": [
      { "category": "类别名", "best_format": "视频|图文|均可", "reason": "原因" }
    ]
  },
  "content_formulas_recap": [
    { "label": "A 公式名", "template": "公式模板" }
  ],
  "action_plan": {
    "this_week": ["本周行动1", "本周行动2"],
    "this_month": ["本月行动1"],
    "long_term": ["长期方向1"]
  }
}

Field guidelines:

topic_categories: 3-6 categories with 5-20 keywords each, derived from the account's actual content themes
title_formulas: 4-8 formulas with valid regex patterns, derived from the account's title patterns
cloud_keywords: 20-40 high-frequency keywords from the account's titles
radar_scores: One entry per topic category, scores 0-100 for each of the 5 radar dimensions
strategies: 3-5 actionable strategies from the report's dimension 7
top10_note_ids: First 8 characters of the top 10 notes' IDs (by likes)
top10_analysis: Per-note success factor breakdown for each top 10 note, matched by note_id_prefix
hit_patterns: 1-3 bullet points summarizing the key patterns shared by hit content
title_hit_vs_miss: Multi-dimension comparison table of hit vs miss title characteristics
cover_analysis: Cover style distribution, hit cover patterns, and template suggestion
category_insights: One qualitative paragraph per topic category (strengths, weaknesses, recommendations)
format_comparison: Video vs image performance comparison with per-category recommendations
content_formulas_recap: 3-5 quick-reference content formulas (label + template)
action_plan: Time-bound action items split into this_week, this_month, long_term

Step 7: Generate the dashboard

Run the dashboard generator to produce an interactive HTML dashboard:

python3 <skill_dir>/scripts/dashboard.py <account_id>

This reads data/<account_id>/analysis.json (required) along with profile.json and notes.json, and outputs data/<account_id>/dashboard.html.

Note on data availability: the scraper collects card-level data only (title, likes, cover, type). Hashtags and publish dates are not visible on profile cards and are excluded.

Report Structure (7 Dimensions)

1. 账号概览

昵称、简介、粉丝量级、内容类型定位
核心统计：总笔记数、平均点赞、点赞中位数
视频 vs 图文比例

2. 爆款笔记分析（Top 10）

按点赞数降序排列
每篇列出：标题、点赞数、内容类型、成功因素拆解
爆款定义：点赞量排名前 20% 的笔记

3. 标题分析

标题长度分布与最佳长度区间
高频词和句式模式（如"xx个方法"、"千万别xx"、提问式等）
爆款 vs 普通标题的差异对比
可复用的标题公式总结（至少 3 个）

4. 封面图分析（视觉分析）

风格类型分布（实拍 / 设计图 / 截图 / 对比图等）
配色特征、文字使用规律、构图规律
爆款封面的共性特征
可复用的封面模板建议

5. 内容选题分析

主题分类及各类占比（基于标题文本聚类）
各类别的平均点赞表现
最受欢迎的选题方向

6. 内容形式分析

图文 vs 视频的比例与点赞表现对比
哪种形式更受该账号受众欢迎

7. 可复制的方法论总结

该账号的核心增长策略（3-5条）
可直接复用的内容公式
具体行动建议清单（可执行的 to-do list）

Error Handling

Scraper gets no notes: Check that section.note-item elements exist on the profile page. If not, the scraper falls back to a[href*="/explore/"] links.
Cookie expired: Delete data/cookies.json and rerun the scraper to trigger a fresh login.
Partial data: If the scraper was interrupted, rerun it — it will skip already-downloaded covers.
Missing cover images: Proceed with analysis using available covers; note which notes lack covers.
Anti-bot detection: The scraper stays on the profile page only. If detection still occurs, increase DELAY_MIN/DELAY_MAX in scraper.py.