Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add redfox-data/redfox-community-dsh --skill xiaohongshu-titlegit clone --depth 1 https://github.com/redfox-data/redfox-community-dshWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/redfox-data/redfox-community-dsh/xiaohongshu-title)<a href="https://agentmods.dev/skills/redfox-data/redfox-community-dsh/xiaohongshu-title"><img src="https://agentmods.dev/badge/skills/redfox-data/redfox-community-dsh/xiaohongshu-title/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/redfox-data/redfox-community-dsh/xiaohongshu-title"><img src="https://agentmods.dev/badge/skills/redfox-data/redfox-community-dsh/xiaohongshu-title.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00041 | $0.03078 |
| Opus 5 | $0.00020 | $0.01539 |
| Sonnet 5 | $0.00008 | $0.00616 |
| Haiku 4.5 | $0.00004 | $0.00308 |
Grade A, and why
xiaohongshu-title scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 273 lines — stays where its author put it; the contents beside it link to each section on GitHub.
小红书标题生成
1. 简介
基于用户输入的任何信息生成小红书爆款标题的专业工具 -- 通过查询小红书平台真实爆款数据,分析爆款标题的共同特征,结合用户输入的核心主题,自动生成 10 个高匹配度的小红书爆款标题,每个标题附带匹配指数、参考爆款和详细推荐理由。
适用对象:小红书创作者、内容运营、文案策划、需要标题灵感的写作者。
2. 功能特性
核心功能:
- 智能理解用户输入 -- 无论输入什么内容,都能提取核心关键词
- 爆款数据查询 -- 基于用户关键词查询小红书热门笔记标题
- 爆款规律分析 -- 从标题结构、关键词、情绪表达等维度分析爆款特征
- 批量标题生成 -- 一次性生成 10 个符合爆款规律的新标题
- 匹配指数评分 -- 每个标题附带 8-10 分的匹配指数
特色亮点:
- 泛化词拓展策略:对大类词(如 "美妆"、"穿搭")自动推荐 10 个细分方向供用户选择
- 精准关键词理解:严格区分产品形态(如 "防晒霜" 只查霜状产品,不扩展到喷雾、帽子等)
- 数据不足自动扩展时间范围(禁止换词)
- 统一输出格式:标题X + 匹配指数 + 参考爆款 + 推荐理由 + 分隔线
3. 一键安装
鉴权
获取 API Key
请前往 红狐hub 获取API KEY
配置 API Key
方案1: 以OpenClaw为例,将REDFOX_API_KEY添加到~/.openclaw/openclaw.json中
{ "env": { "REDFOX_API_KEY": "ak_xxxx..." } }
方案2: 终端配置:export REDFOX_API_KEY="ak_xxxx..."
export REDFOX_API_KEY="ak_xxxx..."
依赖安装
依赖 requests 库(标准 Python 库),如未安装可执行 pip install requests。
4. 使用指南
基础使用
核心原则:无论用户输入什么,最终目标都是生成小红书爆款标题。
示例理解:
- "我想写关于护肤的内容" -> 基于 "护肤" 生成爆款标题
- "上班族早餐" -> 基于 "上班族早餐" 生成爆款标题
- "防晒霜" -> 只查询防晒霜(膏状/乳状),不包含防晒喷雾、防晒衣等
完整操作流程(严格按顺序执行)
第一步:理解用户输入并提取关键词
- 分析用户输入,从输入中提取核心主题和关键词。
- 判断关键词类型:
- 细分词 / 垂直赛道(含具体场景/属性修饰)-> 直接查询数据
- 具体产品词(如 "防晒霜"、"睫毛膏")-> 直接查询该产品数据,不扩展到其他产品
- 泛化词 / 大分类(纯大类词,如 "穿搭"、"美食")-> 执行泛化词拓展策略
精准关键词理解规则:
- 用户输入的是什么产品就只查询什么产品,不随意扩展到相关但不同的产品
- 霜 != 喷雾 != 衣:用户输入 "防晒霜",要的是有防晒作用的霜,不包含防晒喷雾、防晒衣
- 膏 != 液 != 粉:用户输入 "睫毛膏",要的是膏状的睫毛产品,不包含假睫毛、睫毛增长液
- 液 != 霜 != 喷雾:用户输入 "粉底液",要的是液体状的粉底,不包含气垫、BB 霜
第二步:泛化词拓展策略(仅当关键词为泛化词时执行)
- 查询近 7 天网页资讯中和泛化词相关的小红书热点并生成细分词(禁止调用脚本搜索数据)
- 输出 10 个细分方向词(趋势词、人群词、场景词、意图词各 2-3 个)
- 必须等待用户回复「拓展」或「不拓展」后再执行,不得在同一次对话中继续执行任何脚本调用
拓展词输出示例:
我识别到「美妆」是较大的分类,已查询近期热门趋势,推荐以下细分方向:
柔焦底妆、养肤妆、亚裔妆、油皮定妆、通勤妆、夏日妆容、新手妆容、平价彩妆、妆容教程、彩妆测评
回复「拓展」将同时搜索这10个词,回复「不拓展」将继续搜索「美妆」
第三步:时间范围与数据查询
- 数据库只包含昨天至 30 天前的数据
- "最近" 默认定义为最近 7 天(startDate = 今天 - 6 天)
- 日期格式必须为 yyyy-MM-dd
数据不足时的自动调整(优先扩展时间,禁止换词):
- 处理原则:数据不足 5 条时,只能扩展时间范围,不能更换或拓展关键词
- 调整顺序:近 1 天 -> 近 3 天 -> 近 7 天 -> 近 30 天
- 每次扩展后提示用户:"该关键词近 X 天数据不足 5 条,已自动扩展时间范围至近 Y 天"
- 最终兜底:即使扩展到近 30 天仍不足 5 条,如实展示现有数据,但必须提示用户数据不足
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 273 lines · 41 tokens per session scan A 716aabea8da6
xiaohongshu-title is a skill published in the GitHub repository redfox-data/redfox-community-dsh (6 stars, last pushed yesterday), licensed MIT. It adds 41 tokens to every session and 3,078 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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