xiaohongshu-search

Xiaohongshu viral note query

1. Overview

Xiaohongshu popular note search tool, supports searching Xiaohongshu popular viral notes by keyword, and intelligently ranks and recommends based on a three-dimensional score of relevance, popularity, and timeliness. It also provides popular note recommendations and niche track guidance, helping creators, brands, and MCN agencies discover popular trends and gain creative inspiration. Note: This tool is executed only in the main Agent and is not dispatched to sub-Agents.

2. Features

🔍 Intelligent keyword search — Supports exact keyword search, multi-keyword combinations (comma-separated), and site-wide popular queries (empty keyword)
📊 Three-dimensional score ranking — When there is a keyword, the total score (out of 15) is calculated by weighting relevance (out of 10), popularity (out of 3), and timeliness (out of 2); site-wide popular notes are sorted by interaction count
🧠 Fine-grained intent understanding — Prioritizes extracting niche direction words from the user's description, identifies generalized words, and automatically recommends 10 niche directions
⏱️ Flexible time range — Defaults to querying the last 7 days; when data is insufficient, automatically expands the time range (1 day→3 days→7 days→30 days); updated daily at 7 AM
🔥 Popular note recommendations — When there are few results, automatically displays recent popular recommended notes and popular topic tags
📈 Niche track guidance — After each query, proactively recommends 10 related niche directions to help users explore in depth
🏷️ Keyword expansion recommendations — The script returns the relatedSearches field and automatically displays related search suggestions
📩 Scheduled subscription push — Supports creating calendar subscription tasks and automatically pushing the latest popular notes at the set time
📄 HTML report generation — Automatically generates {keyword}_热门数据.html visualization files
🛡️ Strong data explanation — The inclusion standard for popular notes is an interaction count of 1000+; the data explanation and ranking basis are displayed at the top

3. One-click installation

Authentication

Get API Key

Please go to RedFox Hub to get the API KEY

Configure API Key

Option 1: Using OpenClaw as an example, add REDFOX_API_KEY to ~/.openclaw/openclaw.json:

{ "env": { "REDFOX_API_KEY": "your_api_key_here" } }

Option 2: Terminal configuration

export REDFOX_API_KEY="your_api_key_here"

Dependency installation

This Skill uses the Python 3 standard library and requires no additional third-party dependencies. Just ensure that Python 3.x is installed on the system.

Environment variable configuration

Environment variable	Description	Required	How to obtain
`REDFOX_API_KEY`	RedFox Data API Key	Yes	RedFox Hub

4. Usage Guide

⚠️ Core execution rules (must be followed)

Generalized words must be asked about before querying: When a generalized word is identified,it is absolutely forbidden to call the script directly, and you must first output niche word recommendations and wait for the user to choose before executing the query
Correct execution order: Keyword extraction → Determine whether it is a generalized term → If it is a generalized term, ask the user → After the user replies, call the script
Mandatory wait rule: After outputting the subcategory term recommendations, execution must stop and wait for the user's next turn reply of "expand" or "do not expand",and no script call may continue to be executed in the same conversation

Common generalized terms: General terms: overarching vocabulary with a high level of abstraction and broad coverage, without specific scenario/attribute modifiers, such as industry categories, and can include multiple subcategories. Features: ① Semantically a superordinate concept (e.g., "beauty" includes "foundation/lipstick"; "sports" includes "running/yoga"; such as AI); ② In context, often paired with general words such as "field" or "type" (e.g., "beauty field", "sports type").

Common specific terms: Specific terms: instantiated vocabulary with a low level of abstraction and clear reference, containing specific scenario/attribute modifiers, and belonging to a direct subcategory of a general term. Features: ① Semantically a subordinate concept (e.g., "foundation" is a subcategory of "beauty products"; "keto diet" is a subcategory of "dietary methods"); ② The word structure mostly contains modifying components (e.g., "spring" → "spring outfits"; "keto" → "keto diet").

Basic usage (complete the query in 3 steps)

Step 1 — Extract keywords: Extract search keywords from the user's natural language description. Prioritize extracting subdivided direction terms (with specific scenario/attribute modifiers) rather than generalized broad category terms.

Step 2 — Call the script：

python scripts/fetch_xhs_hot_articles.py --keyword <关键词> --start-date <日期>

With niche keywords:python scripts/fetch_xhs_hot_articles.py --keyword <关键词> --start-date <日期>
Without niche keywords (query site-wide trending):python scripts/fetch_xhs_hot_articles.py --keyword "" --start-date <日期>
Separate multiple keywords with commas:python scripts/fetch_xhs_hot_articles.py --keyword "减脂餐,职场穿搭,健身" --start-date <日期>
Pagination parameter:--page-num 1 --page-size 50

Step 3 — View results: The script returns structured JSON, and the results are output according to the display strategy specified in this guide.

Advanced usage

Understanding user intent (before querying the script)

⚠️ Core rule: Semantic understanding should be used, prioritizing the extraction of subdivided direction terms in the user's description rather than generalized broad category terms

1. Determine whether the user mentions niche keywords：

No niche keywords(e.g., "what are the recent trending notes", "what trending content is there recently", "take a look at trending data") → directly call the script, pass an empty string for the keyword "", and query site-wide trending
Has niche keywords → continue extraction and judgment

2. Extract precise search keywords(only when the user mentions a niche):

Analyze the user's description: Extract clear subdivided field terms from the user's self-introduction or needs description
Example analysis:
- User input: "I am a 10k-follower Xiaohongshu blogger in the arts and literature category, and I usually post content related to niche film aesthetic accumulation, book reviews, music reviews, Hong Kong and Taiwan culture, etc. Help me find trending topics in the film field"
- Analysis result: Subdivided directions mentioned by the user = niche films, book reviews, music reviews, Hong Kong and Taiwan culture
- Combine the previous context with "film" to obtain subdivided terms = niche films, Hong Kong and Taiwan films, film music reviews
- Search keywords: niche films, Hong Kong and Taiwan films, film music reviews
- ❌ Wrong approach: Extract only the generalized term "film" for searching

3. Keyword type judgment(only when keywords are extracted):

Subdivided terms/vertical niches(Words containing specific scenario/attribute modifiers, such as "workplace outfit", "fat-loss meal", "petite outfit") → search directly, no need to ask about expansion
Generic words/categories(Pure broad-category words, such as "outfit", "food", "beauty", with no modifiers at all) → execute the expansion strategy
Judgment principle: has modifiers (scenario/audience/style/intent) = niche word, search directly; no modifiers = generic word, needs expansion

Generic word expansion strategy

Generic word handling process (⚠️ You must wait for the user's explicit reply before calling the script!)：

Step 1: Generate niche words(Calling the script to search data is prohibited)

Expansion word generation principles:
- Moderate word granularity: do not add combinations to words, avoid being too narrow (e.g., "middle-class outfit" is too narrow, no data can be found)
- Must cover different scenarios: 2-3 each of trend words, audience words, scenario words, and intent words
Output example:
```
我识别到「中产」是较大的分类，已查询近期热门趋势，推荐以下细分方向：
老钱、轻奢、品质生活、松弛感、高级感穿搭、体面、法式穿搭、律师、医生、品质家居
回复「拓展」将同时搜索这10个词，回复「不拓展」将继续搜索「中产」
```
Step 2: Wait for the user's reply
- ❌ Prohibited: calling the script when the user has not replied
- ✅ Correct: only wait for the user's explicit reply of "expand" or "do not expand" before executing
Step 3: Execute according to the user's explicit reply
- User replies "expand" → call the script to search 10 niche words
- User replies "do not expand" or "continue" → call the script to search the original keyword
- User does not reply or replies with other content → identify the corresponding intent

Time range and data query

Time range：

The database only contains data from yesterday to 30 days ago
Default definition of "recent": the last 7 days (startDate = today - 7 days)
Date calculation(convert the user's expression into startDate):
- Today: directly use yesterday's date, startDate = yesterday
- Recent/last 7 days: startDate = today - 7 days
- Last N days: startDate = today - N days
- Example: user says "last 15 days" → startDate = today - 15 days

Automatic adjustment when data is insufficient (⚠️ Prioritize extending time, changing words is prohibited!)：

Handling principle: when data is insufficient, only the time range can be extended; keywords cannot be replaced or expanded
Adjustment order: automatically extend the time range in the following order
1. Last 1 day → last 3 days
2. Last 3 days → last 7 days
3. Last 7 days → last 30 days
Notify the user: when automatically adjusting, notify the user: "This keyword has relatively little data in the last X days, so the time range has been automatically extended to the last Y days"
Prohibited actions: ❌ Do not replace keywords, recommend other words, or trigger the generic word expansion process just because data is insufficient

Apology explanation for out-of-range or not-yet-updated data：

When the user says "today": reply "Very sorry, today's data has not been updated yet. The most recently available data has been displayed for you"
When the time requested by the user exceeds 30 days: reply "Very sorry, currently only data from the last 30 days is supported. The closest data has been displayed for you"

Output file：

Filtered recommendation data:{keyword}_热门数据.html

Preliminary notes (must be communicated to the user before displaying data)

Data notes: The popular notes scope covers articles with 1,000+ interactions, and yesterday's data is updated every morning at 7 AM. Article interaction data is current as of the time of ingestion, not real-time data; after ingestion, interaction data may continue to grow.
Sorting notes(when there is a keyword search): weighted calculation based on three dimensions: relevance (out of 10), popularity (out of 3), and timeliness (out of 2), for a total score of 15
Sorting notes(site-wide popular/no keyword): sorted by interaction count, with no score field

Data display strategy (core)

⚠️ Mandatory output rules：

✅ Must strictly follow the format specified in this step for output
❌ Do not add any analysis or interpretation before the output
❌ Do not offer suggestions or solutions on your own initiative
❌ Do not ask the user about their true purpose or needs
✅ Directly read the JSON data returned by the script and output it according to the corresponding strategy

Data field descriptions：

articles: normal note data (main display content)
latestHotArticles: recommended popular notes (auxiliary content, 10 items displayed by default, table does not include the score field)
hotTopics: popular topics (returned by the API, for reference only, not displayed in the conversation)

A. Number of articles ≥ 10

Display content:

Time range notes: Must inform the user of the query time range, e.g. "📅 Query time range: May 8 - May 19"
Normal note data (when there is a keyword, sorted by totalScore descending; when site-wide popular, sorted by interaction count)
Related search recommendations (relatedSearches)

Markdown table format：

⚠️ The table field order must strictly follow the order below：

Note: The total score field needs to be displayed in bold (using**分数**format)

Example (with keyword): 📅 Query time range: May 8 - May 19

Note title	Author	Interactions	Publish time	Relevance	Popularity	Timeliness	Total score
Must-read for workplace newcomers: 5 tips to help you quickly integrate into the team	Workplace Growth Community	10.0w	2026-05-15	9.8	3.0	2.0	14.8

Example (site-wide popular): 📅 Query time range: May 8 - May 19

Note title	Author	Interactions	Publish time
Popular note title	Author name	10.0w	2026-05-15

🔤 Related search recommendations: workplace communication, workplace promotion, workers

B. Number of articles < 10 but > 0

Display content:

Time range notes: Must inform the user of the query time range, e.g. "📅 Query time range: May 8 - May 19"
Tip information: "💡In the current time period for the current keyword, only X results were found. You can try expanding the keywords or extending the time period. We have also recommended recent popular notes for you."
Normal note data
Keyword expansion recommendations (relatedSearches)
Recommended popular notes (latestHotArticles, with a "Recommended Popular Notes" title section, displaying 10 items by default)
Recommended popular topics

Markdown format example：

📅 The collection principle for viral notes is articles with 1000+ interactions. Query time range: May 8 - May 19

Only 3 results were found in the current time period for the current keyword:

When there is a keyword:

Note title	Author	Interactions	Publish time	Relevance	Popularity	Timeliness	Total score
Note 1	Author 1	10.0w	2026-05-15	9.8	3.0	2.0	14.8

For site-wide popular content (no scoring fields):

Note title	Author	Interactions	Publish time
Note 1	Author 1	10.0w	2026-05-15

🔤 Keyword expansion recommendations: workplace communication, workplace promotion, office workers

💡We have recommended recent popular notes for your reference, which may be helpful to you：

⚠️ The recommended popular notes table does not require scoring fields. The format is:

Note title	Author	Interactions	Publish time
Popular note 1	Author A	8.5w	2026-05-14
Popular note 2	Author B	6.2w	2026-05-13

📈 You can also try searching the following popular tracks:：

Outfit, food, makeup, film and television, workplace, cute pets, home, travel, transportation, interests, technology, internet, healthcare, astrology and relationships, weddings, photography, education, parenting, personal care, trendy shoes and bags, life, scientific exploration, news and information, sports

C. articles count = 0

⚠️ You must strictly follow the format below for output. Do not add suggestions or analysis on your own.

Display content:

**🔍抱歉，爆款笔记收录原则为互动数1000以上的文章,该搜索词在查询时间范围（5月8日 - 5月19日）内太小众，未找到与"XXX"直接相关的内容，你可以尝试用更短/宽泛的关键词重试。**

**推荐搜索词**：从脚本返回的relatedSearches字段中提取推荐词，以加粗形式展示；

💡我们为您推荐了近期的热门笔记供参考，或许对您有帮助：

⚠️ 推荐热门笔记表格不需要评分字段，格式为：
| 笔记标题 | 作者 | 互动数 | 发布时间 |
|---------|------|--------|----------|
| [热门笔记1](url) | [作者A](https://www.xiaohongshu.com/user/profile/xxx) | 8.5w | 2026-05-14 |
| [热门笔记2](url) | [作者B](https://www.xiaohongshu.com/user/profile/xxx) | 6.2w | 2026-05-13 |

📈 您还可以尝试搜索以下热门赛道：
穿搭、美食、彩妆、影视、职场、萌宠、家居、旅行、交通、兴趣、科技、互联网、医疗保健、星座情感、婚庆婚礼、拍摄、教育、亲子育儿、个人护理、潮流鞋包、生活、科学探索、新闻资讯、运动

Output rules：

✅ You must directly output the above format content
❌ Do not add extra analysis or suggestions
❌ Do not explain why no data can be found
❌ Do not proactively provide other search options
❌ Do not ask the user for their real purpose

Display logic (pagination)

When the articles count > 10：

By default, initially display only the first 10 items of data
You must prompt the user:

💡 当前共找到 X 条相关笔记（X为实际返回条数），已展示前10条。是否需要查看全部？

Wait for the user to reply:

If the user replies "yes"/"view all"/"all" → display all data
If the user replies "no"/"not needed"/"don't need" → do not display more

Format when displaying all data：

📊 全部结果（共X条，X为实际返回条数）：

有关键词时：
| 笔记标题 | 作者 | 互动数 | 发布时间 | 相关性 | 热度 | 时效 | **总分** |

全站热门时：
| 笔记标题 | 作者 | 互动数 | 发布时间 |

| ...（全部数据）...

Subscription service inquiry (must be executed)

When the articles count > 0, you must ask after the result output is completed：

📬 订阅服务
1️⃣ 是否需要订阅当前搜索条件笔记，订阅后将定时推送给您？
2️⃣ 暂不需要

Handle user replies：

If the user chooses 1️⃣ → use calendar_create Tool creates a schedule and subscribes to the current search criteria
User selects 2️⃣ → End the current conversation

Subscription implementation steps：

Notify the data update time and ask about the push time：

📅 数据更新时间：每日早上7点更新昨日数据

请告诉我您希望推送的具体时间~

After the user selects, call calendar_create tool：
- title：小红书热门笔记订阅：{关键词}
- description: Record the current search parameters (keywords, time range)
- start_time: Set according to the time selected by the user
- remind_type: Set as a recurring reminder
- Other parameters use the current query parameters
Prompt after successful subscription：

✅ 订阅创建成功！

📌 订阅信息：

- 关键词：{关键词}
- 时间范围：{当前时间范围}
- 推送时间：{用户选择的时间}
- 数据更新：每日早上7点更新昨日数据

到达设定时间后，将自动为您推送最新的小红书热门笔记。

⚠️ Mandatory rules：

✅ Must ask immediately after the result output is completed
✅ When the user chooses to subscribe, must use calendar_create tool
✅ Parameters must use the current query parameters (keywords, time range, etc.)
❌ Do not skip this step
❌ Do not ask before displaying the results

Recommended niche tracks (⚠️ Must be executed after displaying the data)

Core rules: After displaying the popular data,must proactively ask the user whether they need to query more specific niche tracks

Recommended niche term generation：

Based on the keywords of the current query, generate 10 related niche direction terms
Generation principles：
- Moderate term size: Avoid being too specific (no data found) or too broad (scope too large)
- Cover different dimensions: 2-3 each of scene terms, audience terms, style terms, and intent terms
- Reference data performance: Prioritize recommending directions with higher recent popularity

Output format：

以上是「{当前关键词}」的热门数据。

如需深入了解某个细分方向，可以从以下赛道中选择：
{细分词1}、{细分词2}、{细分词3}、{细分词4}、{细分词5}、 {细分词6}、{细分词7}、{细分词8}、{细分词9}、{细分词10}

回复具体关键词，我将为您查询该赛道的热门笔记。

Example output：

以上是「减脂餐」的热门数据。

如需深入了解某个细分方向，可以从以下赛道中选择：
早餐减脂、减脂便当、低卡晚餐、减脂零食、学生党减脂、一周食谱、减脂沙拉、减脂主食、控糖减脂、懒人减脂餐

回复具体关键词，我将为您查询该赛道的热门笔记。

Special cases handling：

If the user queries a very specific term (such as "one-week fat-loss meal recipes"), no more specific directions may be recommended; terms that have already been expanded do not need to be asked for expansion again

If the user queries an empty keyword (site-wide popular),directly use the popular track listRecommendation, format as follows:

以上是全站热门数据。

如需深入了解某个细分赛道，可以从以下热门赛道中选择：
穿搭、美食、彩妆、影视、职场、萌宠、家居、旅行、交通、兴趣、科技、互联网、医疗保健、星座情感、婚庆婚礼、拍摄、教育、亲子育儿、个人护理、潮流鞋包、生活、科学探索、新闻资讯、运动

回复具体关键词，我将为您查询该赛道的热门笔记。

Self-check before output [Must be executed]

Before output, check item by item whether every field of the output format is complete:

[] Intent recognition: Is the user's search intent accurately recognized?
[] Note data: Are all filtered results returned by the script faithfully displayed (display 10 items when max_items=10), each containing the sequence number (bold), note title, work link, author name, author link, publish time, interaction count, and recommendation reason (bold)?
[] HTML card: Does it include an html card?
[] Recommended niche terms: Are 10 niche terms listed?

If any field is missing or incomplete, it must be completed before output.

Common command quick reference table

Scenario	Command
Keyword search (default last 7 days)	`python scripts/fetch_xhs_hot_articles.py --keyword "关键词" --start-date <日期>`
Site-wide popular	`python scripts/fetch_xhs_hot_articles.py --keyword "" --start-date <日期>`
Multi-keyword search	`python scripts/fetch_xhs_hot_articles.py --keyword "词1,词2,词3" --start-date <日期>`
Paginated query	`python scripts/fetch_xhs_hot_articles.py --keyword "关键词" --start-date <日期> --page-num 1 --page-size 50`
Generate HTML report	Automatically generated by the script `{keyword}_热门数据.html`
Subscription creation	After replying 1️⃣, use `calendar_create` tool to create a scheduled task

5. Use Cases

Use Case 1: Xiaohongshu Blogger Seeking Topic Inspiration

Role: Xiaohongshu Content Creator
Need: Wants to learn which notes are currently popular in the "fat-loss meal" niche, in order to find a topic direction for the next note
How to Use: Enter the specific keyword "fat-loss meal"; the system returns a list of highly rated popular notes, and after displaying them automatically recommends 10 specific directions (such as "breakfast fat-loss", "fat-loss bento", etc.)
Expected Benefits: Quickly identify high-value topics through three-dimensional rating and sorting, while gaining inspiration for specific sub-niches, improving the quality of content output

Use Case 2: Brand Market Research

Role: Brand Marketing Personnel
Need: Understand the overall popular trends in the "outfit" niche, but is unsure of the specific sub-directions
How to Use: After entering the broad term "outfit", the system recommends 10 specific directions (such as "petite outfit", "French outfit", "workplace outfit", etc.); reply "expand" to batch search all directions
Expected Benefits: Cover multiple sub-fields at once, fully grasp the popularity distribution of the niche, and provide data support for the brand's content marketing strategy

Use Case 3: MCN Agency Creator Content Planning

Role: MCN Agency Content Director
Need: Needs to view popular notes across multiple fields at once (fat-loss meals, fitness, workplace outfits) to plan the content schedule for the creator matrix under the agency
How to Use: Use commas to separate multiple keywords --keyword "减脂餐,健身,职场穿搭" for cross-domain queries
Expected Benefits: One command covers multiple categories, efficiently obtaining cross-domain popular trends and improving content planning efficiency

Use Case 4: Scheduled Daily Push of Popular Notes

Role: Xiaohongshu Operations Personnel
Need: Hopes to automatically receive a list of popular notes in a specific niche every morning, to continuously track trends
How to Use: After searching keywords, select "1️⃣ Subscribe", set the push time, and create a scheduled calendar task
Expected Benefits: No need to manually repeat searches; obtain the latest popular notes on a daily schedule, continuously track niche dynamics, and maintain creative sensitivity

6. Project Architecture

Directory Structure

xiaohongshu-search/
├── SKILL.md                               # Skill 定义与使用文档（本文件）
├── scripts/
│   └── fetch_xhs_hot_articles.py          # 核心搜索脚本，调用红狐 API 获取小红书热门笔记
└── references/
    └── xhs_hot_article_format.md          # 数据字段格式参考文档

Tech Stack

Component	Technology	Description
Runtime Environment	Python 3.x	Standard library, no third-party dependencies
Data Interface	Redfox API (Redfox)	Authenticated via REDFOX_API_KEY
Output Format	JSON (stdout) + HTML (file)	JSON is output via stdout for AI parsing; HTML is a visual report file
Display Format	Markdown table	The AI agent renders JSON as a table for display
Execution Restriction	Main Agent only	Not executed in sub-agents

Core Module Description

Module	Path	Features
Search Script	`scripts/fetch_xhs_hot_articles.py`	Calls the Red Fox API to retrieve popular Xiaohongshu notes, supports --keyword, --start-date, --page-num, --page-size parameters, and automatically generates an HTML report
Data Format Reference	`references/xhs_hot_article_format.md`	Detailed explanation of the data field formats and output specifications returned by the interface
SKILL Definition	`SKILL.md`	Defines Skill metadata, intent understanding rules, generalization word expansion strategy, display strategy, subscription logic, and self-check list

Resource Index

Core Script：scripts/fetch_xhs_hot_articles.py — Calls the Red Fox API to retrieve popular Xiaohongshu note data and generate an HTML report
Reference Documentation：references/xhs_hot_article_format.md — When to read: when you need to understand data field formats and output specifications

7. FAQ

Installation

Q: Which dependencies need to be installed? A: This tool uses the Python 3 standard library and requires no additional third-party dependencies. Just ensure Python 3.x is installed on your system.

Q: How do I get an API Key? A: Please visit Red Fox Hub to register and obtain an API Key, and configure the environment variable according to the "One-Click Installation" section of this document.

Usage

Q: What is the difference between generalization words and segment words? A: Generalization words are summary terms with a high level of abstraction and broad coverage (such as "beauty" and "outfit"), without specific scenario/attribute modifiers; segment words contain specific modifiers (such as "liquid foundation" and "petite outfit"). Generalization words trigger the expansion strategy, while segment words are searched directly.

Q: What is the time range of the data? A: The database only contains data from yesterday to 30 days ago. By default, it queries the most recent 7 days; when data is insufficient, the time range is automatically expanded (1 day → 3 days → 7 days → 30 days). Yesterday's data is updated every morning at 7 AM.

Q: Why do some tables have a score field while others do not? A: When there is a keyword search, results are sorted by totalScore (relevance + popularity + timeliness), and the score field is displayed; site-wide popular results are sorted by interaction count, with no score field. The recommended popular notes table also does not include scores.

Q: How can I view the complete results when there are more than 10 items? A: When the results exceed 10 items, the system will prompt "Do you need to view all?". Reply "yes", "view all", or "all" to display the complete data.

Q: Why does it recommend niche tracks after every query? A: This is a designed feature — to help users further discover more precise niche directions from the current query results and deeply mine the value of the data. If the query is already a segment word, the system will intelligently determine whether to skip the recommendation.

Troubleshooting

Q: What should I do if the search results are empty? A: The inclusion standard for popular notes is an interaction count of 1000+. If the keyword is too niche or the time range is too short, there may be no data. The system will automatically expand the time range, and you can also choose to retry from the search terms recommended in relatedSearches.

Q: It says "Today's data has not been updated yet"? A: The database updates yesterday's data every morning at 7 AM, and the current day's data has not yet been stored. The system will automatically display the most recently available data.

Q: The script reports an error when executed? A: Common causes: (1) REDFOX_API_KEY is not configured or has expired; (2) the Python version is lower than 3.x; (3) network connection issues. Please check them one by one.

Security Permission

Q: How should the API Key be stored securely? A: It is recommended to use Option 1 (configure it in the env field of openclaw.json) to avoid leaking it in terminal history. Do not hard-code the API Key in scripts or upload it to public repositories.

Q: What are the data sources and copyright? A: The data comes from public Xiaohongshu notes indexed by the Red Fox API. The copyright of the notes belongs to the original authors. This tool is for learning and content creation reference only.

Q: Why must it be executed in the main Agent? A: The Xiaohongshu search tool involves complete intent understanding, generalization term expansion, and data presentation strategy. The process is complex and depends on global context judgment. It is executed only in the main Agent to ensure consistent behavior and complete rules.

About this skill

1. Overview

2. Features

3. One-click installation

Authentication

Get API Key

Configure API Key

Dependency installation

Environment variable configuration

4. Usage Guide

⚠️ Core execution rules (must be followed)

Basic usage (complete the query in 3 steps)

Advanced usage

Understanding user intent (before querying the script)

Generic word expansion strategy

Time range and data query

Preliminary notes (must be communicated to the user before displaying data)

Data display strategy (core)

A. Number of articles ≥ 10

B. Number of articles < 10 but > 0

C. articles count = 0

Display logic (pagination)

Subscription service inquiry (must be executed)

Recommended niche tracks (⚠️ Must be executed after displaying the data)

Self-check before output [Must be executed]

Common command quick reference table

5. Use Cases

Use Case 1: Xiaohongshu Blogger Seeking Topic Inspiration

Use Case 2: Brand Market Research

Use Case 3: MCN Agency Creator Content Planning

Use Case 4: Scheduled Daily Push of Popular Notes

6. Project Architecture

Directory Structure

Tech Stack

Core Module Description

Resource Index

7. FAQ

Installation

Usage

Troubleshooting

Security Permission