xiaohongshu-note-analyzer
softbread/xiaohongshu-doctor
Comprehensive analysis of Xiaohongshu notes for content quality, keywords, title appeal, risk detection, and engagement potential.
What is xiaohongshu-note-analyzer?
Analyzes Xiaohongshu (Little Red Book) notes across six dimensions: keyword optimization, title/opening appeal, sensitive content risks, commercialization level, interaction potential, and content structure. Use this before publishing to identify optimization opportunities, detect compliance risks, and improve note visibility and engagement.
- Keyword analysis with search heat, layout optimization, and hashtag recommendations
- Title and opening paragraph appeal scoring using proven viral formulas
- Sensitive content and compliance risk detection with severity levels
- Commercialization assessment to identify and reduce advertising feel
- Interaction trigger analysis for discussion points and sharing motivation
- Content structure evaluation including formatting, emoji usage, and pacing
How to install xiaohongshu-note-analyzer
npx skills add https://github.com/softbread/xiaohongshu-doctor --skill xiaohongshu-note-analyzerHow to use xiaohongshu-note-analyzer
- 1.Paste or upload your Xiaohongshu note content (title, body text, hashtags, image descriptions)
- 2.Trigger analysis using keywords like '分析小红书笔记', '小红书内容审核', '笔记优化', or 'XHS分析'
- 3.Review the comprehensive analysis report covering all six dimensions
- 4.Prioritize recommended changes based on the modification priority list
- 5.Apply the optimized version suggestions to your note before publishing
Use cases
- Pre-publication review to catch compliance issues and optimize before posting
- Content optimization consultation to improve keyword placement and title appeal
- Engagement enhancement for creators seeking higher interaction rates
- Competitive analysis and benchmarking against platform best practices
- Risk mitigation for brand partnerships and sponsored content
- Xiaohongshu content creators and influencers
- Brand marketing teams managing sponsored posts
- Content strategists optimizing for platform algorithms
- E-commerce sellers promoting products on Xiaohongshu
- Social media managers handling multiple creator accounts
xiaohongshu-note-analyzer FAQ
🔴 High (删帖/封号) = explicit violations causing deletion/bans; 🟠 Medium (限流) = gray-area content causing reduced distribution; 🟡 Low (延迟) = potential triggers causing publishing delays; 🟢 Safe = no compliance issues, normal promotion.
Use the keyword layout formula: core keyword in title + first 50 characters + body text + hashtags. Aim for 2-3% keyword density. Distribute 3-5 long-tail keywords naturally throughout the note rather than clustering them.
Use proven formulas: numbers + results (5个技巧), pain point + solution (毛孔粗大?), curiosity hooks (闺蜜问我...), before/after comparisons, authority positioning, or strong emotion words. Include core keyword and match your cover image.
Lead with pain points or stories before mentioning products, mention product drawbacks for authenticity, compare with alternatives objectively, emphasize personal experience over product specs, and avoid pricing/purchase links and brand names in titles.
High-value content: lists/collections, step-by-step tutorials, product comparisons, money-saving guides, and information-dense posts. Include discussion questions, emotional resonance, and practical utility to trigger sharing and saving.
Full instructions (SKILL.md)
Source of truth, from softbread/xiaohongshu-doctor.
name: xiaohongshu-note-analyzer description: 全面分析小红书笔记的内容质量、关键词优化、标题吸引力、敏感内容风险、商业化程度、互动潜力等。适用于发布前审核、内容优化建议、提升笔记曝光率。触发词包括"分析小红书笔记"、"小红书内容审核"、"笔记优化"、"XHS分析",或上传小红书笔记内容请求分析。
小红书笔记分析器 (XiaoHongShu Note Analyzer)
对小红书笔记进行全方位分析,提供优化建议,提升内容质量和曝光率。
分析维度
- 关键词分析 — 搜索热度、关键词布局、标签优化
- 标题/首段吸引力 — 爆款标题元素、首图文案
- 敏感内容风险 — 违规词检测、限流风险评估
- 商业化程度 — 软广硬广识别、自然度评分
- 互动触发潜力 — 讨论点、分享动机、收藏价值
- 内容结构 — 排版、emoji使用、段落节奏
分析流程
1. 提取笔记内容 → 标题、正文、标签、图片描述
2. 关键词分析 → 核心词、长尾词、布局检查
3. 敏感词扫描 → 违规风险、限流风险
4. 商业化评估 → 广告痕迹、自然度
5. 互动潜力评估 → 讨论点、情感共鸣
6. 生成优化建议 → 具体修改方案
1. 关键词分析
检查要点
| 维度 | 优秀 | 待改进 |
|---|---|---|
| 核心关键词 | 标题+首段+正文+标签都包含 | 仅出现1-2处 |
| 长尾关键词 | 3-5个相关长尾词自然分布 | 无长尾词或堆砌 |
| 标签数量 | 5-10个相关标签 | <3个或>15个 |
| 关键词密度 | 2-3%自然出现 | <1%或>5%堆砌 |
关键词布局公式
标题: 必含核心关键词 + 吸引词
首段(前50字): 核心关键词 + 痛点/好奇点
正文: 长尾关键词自然分布
标签: #核心词 #长尾词 #场景词 #人群词
详见 references/keyword-strategy.md
2. 标题/首段吸引力
爆款标题公式
| 类型 | 公式 | 示例 |
|---|---|---|
| 数字型 | 数字+关键词+结果 | "5个技巧让你月瘦10斤" |
| 痛点型 | 痛点+解决方案 | "毛孔粗大?这个方法亲测有效" |
| 好奇型 | 悬念+关键词 | "闺蜜问我怎么突然变白的..." |
| 对比型 | Before/After | "用了这个精华,同事都问我怎么了" |
| 权威型 | 身份+干货 | "皮肤科医生自用的5款防晒" |
| 情绪型 | 强烈情绪词 | "后悔没早点知道!这个神器太绝了" |
首段黄金50字
首段必须包含:
- ✅ 核心关键词
- ✅ 痛点/需求点
- ✅ 吸引继续阅读的钩子
- ✅ 与封面图呼应
详见 references/title-formulas.md
3. 敏感内容风险评估
风险等级
| 等级 | 说明 | 后果 |
|---|---|---|
| 🔴 高危 | 明确违规词 | 删帖/封号 |
| 🟠 中危 | 灰色地带词 | 限流/不推荐 |
| 🟡 低危 | 可能触发审核 | 延迟发布 |
| 🟢 安全 | 无敏感内容 | 正常推荐 |
常见敏感类别
- 医疗健康类 — 疾病名称、药品、治疗效果承诺
- 金融理财类 — 收益承诺、投资建议、借贷
- 政治敏感类 — 时政、领导人、敏感事件
- 虚假宣传类 — 最、第一、100%、绝对
- 引流违规类 — 微信号、外链、二维码暗示
- 低俗擦边类 — 性暗示、身材过度暴露
详见 references/sensitive-words.md
4. 商业化程度评估
自然度评分标准
| 分数 | 描述 | 特征 |
|---|---|---|
| 9-10 | 纯分享 | 无品牌露出,真实体验 |
| 7-8 | 软植入 | 自然提及品牌,不刻意 |
| 5-6 | 明显软广 | 品牌多次出现,有推荐意图 |
| 3-4 | 硬广 | 明显推销,价格引导 |
| 1-2 | 纯广告 | 通篇产品介绍,无真实体验 |
降低商业感技巧
- ✅ 先讲痛点/故事,后引出产品
- ✅ 提及缺点(真实感)
- ✅ 对比其他产品(客观感)
- ✅ 强调个人体验而非产品功能
- ❌ 避免价格、购买链接、促销信息
- ❌ 避免品牌名在标题
5. 互动触发潜力
讨论触发点
| 类型 | 方法 | 示例 |
|---|---|---|
| 提问式 | 结尾抛出问题 | "你们觉得哪个颜色更好看?" |
| 争议式 | 轻度争议观点 | "我觉得XX比XX好用,有人同意吗?" |
| 求助式 | 请求建议 | "姐妹们帮我看看选哪个!" |
| 共鸣式 | 引发情感共鸣 | "有没有和我一样的..." |
| 抽奖式 | 互动福利 | "评论区抽3位送同款" |
分享动机触发
用户分享笔记的原因:
- 实用价值 — 干货教程、省钱攻略
- 社交货币 — 显得有品味/见识
- 情感共鸣 — "说出了我的心声"
- 收藏备用 — 清单、合集、测评
收藏价值评估
高收藏内容特征:
- ✅ 清单/合集形式
- ✅ 步骤教程
- ✅ 对比测评
- ✅ 省钱/避坑指南
- ✅ 信息密度高
输出格式
# 小红书笔记分析报告
## 📊 综合评分: X/10
## 1️⃣ 关键词分析
- **核心关键词**: [识别的关键词]
- **关键词布局**: ✅/❌ [评价]
- **标签优化**: [建议]
## 2️⃣ 标题/首段评估
- **标题类型**: [数字型/痛点型/...]
- **吸引力评分**: X/10
- **优化建议**: [具体建议]
## 3️⃣ 敏感内容风险
- **风险等级**: 🟢/🟡/🟠/🔴
- **检测到的敏感词**: [列表]
- **修改建议**: [具体建议]
## 4️⃣ 商业化程度
- **自然度评分**: X/10
- **商业痕迹**: [分析]
- **降低商业感建议**: [具体建议]
## 5️⃣ 互动潜力
- **讨论触发点**: ✅/❌
- **分享动机**: [分析]
- **收藏价值**: X/10
## 6️⃣ 优化后版本
[提供优化后的标题和首段]
## 📝 修改优先级
1. [最重要的修改]
2. [次重要的修改]
3. [可选优化]
分析示例
详见 references/analysis-examples.md
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