Xiaohongshu Similar Account
- 244 installs
- 316 repo stars
- Updated August 4, 2026
- redfox-data/redfox-community
Use xiaohongshu-similar-account for development tasks
About
xiaohongshu-similar-account: A skill for development. This provides functionality for development workflows.
- xiaohongshu-similar-account
Xiaohongshu Similar Account by the numbers
- 244 all-time installs (skills.sh)
- +17 installs in the week ending Aug 4, 2026 (Skillselion tracking)
- Ranked #1,593 of 4,347 Backend & APIs skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 244 |
|---|---|
| repo stars | ★ 316 |
| Last updated | August 4, 2026 |
| Repository | redfox-data/redfox-community ↗ |
What it does
Use xiaohongshu-similar-account for development tasks
Files
小红书相似账号推荐
1. 简介
小红书创作者对标账号匹配工具 -- 输入小红书账号 ID 或 "赛道 + 粉丝数 + 账号等级"，智能推荐可直接复制的同阶对标（粉丝量接近）和可参考的高阶标杆（粉丝量 3-5 倍），帮助创作者快速找到起号参考与投放目标。
适用对象：小红书创作者、KOL 运营、品牌方、MCN 机构。
---
2. 功能特性
核心功能：
- 同阶对标匹配 -- 按粉丝量级匹配相似账号，可直接复制玩法
- 高阶标杆推荐 -- 推荐粉丝量 3-5 倍的高阶账号，提供追赶方向
- 账号数据分析 -- 展示粉丝数、近 30 天互动数等关键指标
- HTML 报告生成 -- 自动生成可视化对标报告
特色亮点：
- 双模式输入：支持按账号 ID 精准查询，也支持按赛道 + 粉丝数 + 等级灵活筛选
- 智能赛道映射：支持口语化输入（如 "做饭" 自动映射为 "美味佳肴"）
- 25 个标准赛道 + 7 个标准账号等级，覆盖小红书全品类
- 粉丝数智能处理：单数值自动设 maxFans=该值 / minFans=0，区间按实际传参
- 订阅服务：支持按查询条件订阅每日对标账号推送
---
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
无需安装第三方依赖，使用 Python 标准库（json、argparse、os、sys、urllib、ssl）即可。
---
4. 使用指南
基础使用
输入方式 A：按小红书账号 ID 查询：
用户输入示例："我的ID是27493135897"
python scripts/xiaohongshu_account_recommender.py --red_id "27493135897"输入方式 B：按赛道 + 粉丝数 + 账号等级查询：
用户输入示例："做饭，3000粉，素人"
python scripts/xiaohongshu_account_recommender.py --track "美味佳肴" --min_fans 0 --max_fans 3000 --level "素人"操作流程（严格按顺序执行）
步骤 1：接收用户输入，解析参数
- 输入方式 A：小红书账号 ID，参数为
redId - 输入方式 B：赛道 + 粉丝数 + 账号等级（可选），参数映射：
track：赛道类型（自动映射，如 "做饭" -> "美味佳肴"）maxFans/minFans：粉丝数处理规则见下方level：账号等级（自动映射，如 "小白" -> "素人"）
粉丝数处理规则（重要）：
- 单个粉丝数（如 "3000 粉"）：maxFans=3000, minFans=0（不要传空字符串）
- 粉丝区间（如 "1000-3000 粉"）：maxFans=3000, minFans=1000
- 未指定粉丝数：maxFans 和 minFans 都传空字符串
步骤 2：调用脚本查询对标账号
API 接口：POST https://redfox.hk/story/api/xhsUser/querySimilarAccounts
步骤 3：按标准模板输出结果
输出顺序： 1. 开场白（只显示有数据的组） 2. 同阶对标表格（有数据才展示） 3. 高阶标杆表格（有数据才展示） 4. 分析总结（有数据才展示） 5. 订阅服务（必须输出，无论有无数据） 6. 展示 HTML 文件（读取脚本输出的 html_path 并展示）
有数据时的输出格式：
✨ 为你匹配到【可直接抄的同阶对标（10个）】和【可追赶的高阶标杆（10个）】的2组对标，可按需参考：
| 数据说明：数据获取时间为xxx，和实时数据存在差别。
👉 【可直接抄的同阶对标（10个）】（可直接复制玩法）
| 账号名 | 粉丝数 | 近30天互动数 | 推荐理由 |
| --- | --- | --- | --- |
| [账号名](账号链接) | 粉丝数 | 总互动数 | 推荐理由 |
👉 【可追赶的高阶标杆（10个）】（模式成熟可参考）
| 账号名 | 粉丝数 | 近30天互动数 | 推荐理由 |
| --- | --- | --- | --- |
| [账号名](账号链接) | 粉丝数 | 总互动数 | 推荐理由 |
📬 **订阅服务**
1️⃣ 是否订阅"现查询条件"的对标账号推送，每日下午7点更新最新数据。你可选择推送频率和时间~
2️⃣ 暂不需要无数据时的输出格式：
✨ 暂未匹配到符合条件的对标账号，请尝试调整筛选条件。
| 数据说明：数据获取时间为入库时刻，和实时数据存在差别。
📬 **订阅服务**
1️⃣ 是否订阅"现查询条件"的对标账号推送，每日下午7点更新最新数据。你可选择推送频率和时间~
2️⃣ 暂不需要步骤 4：生成并展示 HTML 报告（必须执行）
账号推荐输出结束后，脚本自动： 1. 将所有数据存入临时 JSON 文件：./{账号名/对标账号}_{时间戳}.json 2. 读取 JSON 生成 HTML 文件：./{账号名/对标账号}_{时间戳}.html 3. 输出 JSON 标记：{"status": "success", "html_path": "..."}
AI 必须读取 html_path 并在对话结尾展示该 HTML 文件。
高级使用
赛道映射示例：
| 用户输入 | 匹配结果 |
|---|---|
| 美妆、化妆 | 化妆美容 |
| 护肤、美容 | 个人护理 |
| 穿搭、时尚 | 时尚穿搭 |
| 美食、做饭、探店 | 美味佳肴 |
| 旅行、旅游 | 旅行度假 |
| 家居、装修、改造 | 居家装修 |
| 健身、运动、减肥 | 体育锻炼 |
| 母婴、育儿 | 亲子育儿 |
| 宠物、猫、狗 | 宠物天地 |
| 数码、手机、科技、互联网、AI | 数码科技 |
| 教育、学习 | 学习教育 |
| 情感、恋爱 | 星座情感 |
| 职场、工作 | 职业发展 |
账号等级映射示例：
| 用户输入 | 匹配结果 |
|---|---|
| 明星、艺人、爱豆、idol | 明星 |
| 品牌、牌子 | 品牌 |
| 企业、公司、商家、官方号 | 企业 |
| 头部kol、头部、头部达人、大v、大号、百万粉 | 头部kol |
| 腰部kol、腰部、腰部达人、中号、十万粉 | 腰部kol |
| 尾部kol、尾部、尾部达人、小号、万粉 | 尾部kol |
| 素人、普通人、小白、新手、新人、个人号 | 素人 |
标准赛道分类（25 个）：
综合全部 | 出行代步 | 休闲爱好 | 影视娱乐 | 数码科技 | 医疗保健 | 综合杂项 | 星座情感 | 时尚穿搭 | 婚庆婚礼 | 拍摄记录 | 学习教育 | 化妆美容 | 居家装修 | 旅行度假 | 亲子育儿 | 个人护理 | 美味佳肴 | 职业发展 | 宠物天地 | 潮流鞋包 | 日常生活 | 科学探索 | 新闻资讯 | 体育锻炼
标准账号等级（7 个）：
明星 | 品牌 | 企业 | 头部kol | 腰部kol | 尾部kol | 素人
数值格式化：< 10000 直接展示原值，>= 10000 格式化为 "X.Xw"
推荐理由：口语化描述，综合分析内容方向、更新频率、互动表现、爆文案例
---
5. 使用场景
1. 新人起号找对标：输入 "做饭，3000 粉，素人"，获取同阶对标注账号作为起号参考，直接复制已验证的玩法模式。 2. 创作者进阶找标杆：输入自己的小红书 ID，获取高阶标杆推荐，找到 3-5 倍粉丝量的可追赶目标。 3. 品牌方筛选投放 KOL：输入赛道 + 粉丝区间 + 账号等级，快速获取符合条件的达人列表，降低投放决策成本。 4. MCN 批量挖掘潜力账号：按不同赛道和粉丝量级批量查询，系统性地挖掘有潜力的签约对象。
---
6. 项目架构
目录结构
xiaohongshu-similar-account/
├── SKILL.md # 本文件
├── scripts/
│ └── xiaohongshu_account_recommender.py # 调用API查询对标账号，支持赛道和等级映射，自动生成HTML报告
└── references/
└── account_template.html # HTML报告模板技术栈
| 技术 | 用途 |
|---|---|
| Python 标准库 (urllib, json, ssl) | HTTP 请求与数据解析 |
| 红狐 API | 小红书相似账号数据来源 |
| HTML / CSS | 可视化对标报告渲染 |
核心模块说明
- xiaohongshu_account_recommender.py：支持按账号 ID 或赛道 + 粉丝数 + 等级两种方式调用红狐相似账号 API，自动处理赛道和等级映射，生成包含同阶对标和高阶标杆的 HTML 报告。
资源索引
| 文件 | 用途 |
|---|---|
| scripts/xiaohongshu_account_recommender.py | 调用 API 查询对标账号，支持赛道和账号等级映射，自动生成 HTML 报告 |
| references/account_template.html | HTML 报告模板 |
---
7. 常见问答
安装
Q: 需要安装什么依赖？ A: 无需安装第三方依赖，使用 Python 标准库即可。
Q: 如何配置 API Key？ A: 请前往 红狐hub 获取 API KEY，通过环境变量 REDFOX_API_KEY 配置。
使用
Q: 粉丝数怎么传？ A: 单个数值（如 "3000 粉"）：maxFans=该值, minFans=0；区间（如 "1000-3000 粉"）：按区间传参。
Q: 赛道匹配不上怎么办？ A: 支持智能语义模糊匹配，参考上方的赛道映射表。无法识别时会列出完整赛道列表供选择。
Q: 没有匹配到对标账号怎么办？ A: 系统会提示 "暂未匹配到符合条件的对标账号，请尝试调整筛选条件"，同时仍然会输出订阅服务入口。
故障排除
Q: 所有数据来源于哪里？ A: 所有数据来源于红狐 API 接口，严禁任何联网搜索。
Xiaohongshu Similar Account
---
Introduction
Quickly find Xiaohongshu accounts you can directly learn from and aspire to catch up with.
Core Value
Based on follower count, niche, engagement data, and content style, this skill matches you with two types of benchmark accounts: same-level benchmarks (similar follower count, directly copyable tactics) and high-level benchmarks (3–5× followers, proven models to aspire to) — each with AI-generated, conversational recommendation reasons and an operations analysis summary.
Who It's For
- 🎬 New creators — Find same-niche benchmark accounts and quickly learn copyable tactics
- 📦 Content ops / MCN agencies — Batch-screen benchmark accounts to develop content strategies
- 🏢 Brands / ad buyers — Evaluate influencer partnership value and match niche KOL candidates
- 📊 Account-starters — Precisely match by niche + followers + level to reduce startup uncertainty
---
Features
Core Capabilities
- Dual input modes: Query by Xiaohongshu account ID directly, or filter by niche + follower count + account level
- Same-level benchmark matching: Recommends accounts with similar follower counts and copyable playbooks, with recommendation reasons (content direction, update frequency, engagement performance, viral post examples)
- High-level benchmark recommendations: Recommends accounts with 3–5× followers as proven, aspirational models
- Smart mapping: Niche keywords and account levels are automatically fuzzy-matched (e.g., "cooking" → "美味佳肴", "newbie" → "素人")
- HTML report generation: Generates a visual HTML report with clickable account names linking to Xiaohongshu profiles; supports PDF/image export
- Subscription push: Subscribe to daily benchmark account updates delivered at 7 PM
---
API Key Acquisition & Security
- This skill requires the environment variable:
REDFOX_API_KEY. REDFOX_API_KEYis provided by RedFoxHub (https://redfox.hk).- Register at RedFoxHub to obtain your
REDFOX_API_KEY. - Configure
REDFOX_API_KEYas a device environment variable before using this skill. - Before providing your key, confirm its source, available scope, validity period, and whether reset/revocation is supported.
- Do not hard-code or expose the key in plaintext in code, prompts, logs, or output files.
---
Usage Guide
Describe what you need in plain language — no commands to memorize.
Quick phrase reference
| Intent | Example phrase | What you get |
|---|---|---|
| Query by ID | "My Xiaohongshu ID is 27493135897, find benchmark accounts" | Same-level and high-level benchmarks for that ID |
| Niche + followers | "Cooking niche, ~3000 followers, recommend benchmarks" | Same follower tier in 美味佳肴 niche |
| Niche + level | "Beauty niche newbie creators, accounts I can learn from" | 化妆美容 niche at 素人 level benchmarks |
| Follower range | "Fashion niche, 1000–5000 follower account recommendations" | 时尚穿搭 niche in specified follower range |
| Download report | "Generate an HTML report for benchmark accounts" | Visual HTML file delivered |
| Subscribe | "Push food-niche benchmark accounts to me daily" | Daily 7 PM auto-push task created |
Sample output
✨ Matched 【Same-level benchmarks to copy (10)】 and 【High-level benchmarks to chase (2)】 — reference as needed: | Data note: Data fetched on 2026-04-10; may differ from real-time platform data.
👉 【Same-level benchmarks to copy (10)】 (directly copyable playbooks)
| Account | Followers | Total engagement | Recommendation reason |
|---|---|---|---|
| Account | 3200 | 15K | Food tutorials, stable updates, high engagement; viral post "5-min breakfast…" got 23K interactions |
| Account | 2800 | 8500 | Daily vlog style, consistent updates, solid engagement base |
| … | … | … | … |
👉 【High-level benchmarks to chase (2)】 (proven models to aspire to)
| Account | Followers | Total engagement | Recommendation reason |
|---|---|---|---|
| Account | 12K | 86K | 12K followers, monetization-ready, review content, daily updates |
| … | … | … | … |
📊 Analysis summary:
- Same-level benchmarks: avg 2.9K followers, avg 11K engagement
- High-level benchmarks: avg 12K followers, avg 86K engagement
- Suggested focus: high-frequency updaters among same-level benchmarks — learn their rhythm and topics
📬 Subscription 1️⃣ Subscribe to benchmark pushes for your current query — daily 7 PM updates. Choose frequency and time~ 2️⃣ Not now
---
Use Cases
| Scenario | Role | Example question | Benefit |
|---|---|---|---|
| New account benchmarks | New creator | "I'm food niche, 3K followers, newbie — find benchmarks" | Quickly find copyable tactics |
| Batch influencer screening | Brand / ad buyer | "Fashion mid-tier KOLs with good engagement" | Efficiently screen KOL ad candidates |
| Content strategy | Content ops / MCN | "Fitness niche 10K follower accounts doing well?" | Learn top niche playbooks, optimize topics |
| Account launch reference | Account-starter | "Pet niche newbie — accounts I can learn from?" | Reduce startup confusion, clarify direction |
| Monitor benchmark changes | Ops team | "Push food-niche benchmarks to me daily" | Automated tracking of latest benchmark data |
---
Important data notes
Supported niches (25)
All categories, mobility, hobbies, film & entertainment, tech, healthcare, misc, astrology & emotions, fashion, weddings, photography, education, beauty, home decor, travel, parenting, personal care, food, career, pets, bags & shoes, daily life, science, news, sports & fitness
Data freshness
Recommended account data comes from API; fetch time is ingestion moment — may differ from Xiaohongshu real-time data.
---
小红书相似账号推荐 / xiaohongshu-similar-account
---
简介
帮你快速找到小红书上可直接复制玩法和可追赶的对标账号。
核心价值
基于小红书账号的粉丝量、赛道、互动数据和内容风格，为你匹配两类对标账号：同阶对标（粉丝量接近，可直接抄玩法）和高阶标杆（粉丝量 3-5 倍，模式成熟可参考），附带 AI 生成的口语化推荐理由和运营分析总结。
适用对象
- 🎬 新人博主 — 找到同赛道的对标账号，快速学习可复制的运营套路
- 📦 内容运营 / MCN — 批量筛选对标账号，制定内容策略和选题方向
- 🏢 品牌方 / 商务 — 评估达人合作价值，匹配赛道 KOL 投放候选人
- 📊 起号参考者 — 根据赛道+粉丝量+等级精准匹配，降低起号迷茫感
---
功能特性
核心功能
- 双模式输入：支持按小红书账号 ID 直接查询，或按赛道 + 粉丝数 + 账号等级组合筛选
- 同阶对标匹配：推荐粉丝量接近的可直接复制账号，附推荐理由（内容方向、更新频率、互动表现、爆文案例）
- 高阶标杆推荐：推荐粉丝量 3-5 倍的模式成熟账号，作为长期追赶目标
- 智能映射：赛道关键词和账号等级自动模糊匹配，如"做饭"→"美味佳肴"、"小白"→"素人"
- HTML 报告生成：生成可视化 HTML 报告，账号名可点击跳转小红书主页，支持导出 PDF/截图
- 订阅推送：支持订阅对标账号每日下午 7 点自动推送更新
---
密钥获取与安全说明
- 本技能需要使用环境变量：
REDFOX_API_KEY。 REDFOX_API_KEY由 红狐 hub (https://redfox.hk)提供。- 请前往 红狐 hub 注册账号，获取
REDFOX_API_KEY。 - 配置设备环境变量
REDFOX_API_KEY后使用本技能。 - 在提供密钥前，请先确认密钥来源、可用范围、有效期及是否支持重置/撤销。
- 禁止在代码、提示词、日志或输出文件中硬编码/明文暴露密钥。
---
使用指南
直接用自然语言描述你的需求即可，无需记忆命令格式。
常用说法速查
| 意图 | 示例话术 | 效果 |
|---|---|---|
| 按 ID 查对标 | "我的小红书 ID 是 27493135897，帮我找对标账号" | 以该账号为基准匹配同阶和高阶对标 |
| 按赛道+粉丝 | "做饭赛道，3000 粉左右，帮我推荐对标账号" | 返回美味佳肴赛道同粉丝量级对标 |
| 按赛道+等级 | "美妆赛道素人博主，找些能学习的账号" | 返回化妆美容赛道素人级别对标 |
| 按粉丝区间 | "穿搭赛道，1000-5000 粉的账号推荐" | 返回时尚穿搭赛道指定粉丝区间对标 |
| 下载报告 | "帮我生成对标账号的 HTML 报告" | 生成可视化 HTML 文件并交付 |
| 订阅推送 | "每天推送美食赛道对标账号给我" | 创建每日下午 7 点自动推送任务 |
输出示例
✨ 为你匹配到【可直接抄的同阶对标（10 个）】和【可追赶的高阶标杆（2 个）】的 2 组对标，可按需参考： | 数据说明：数据获取时间为 2026-04-10，和实时数据存在差别。
👉 【可直接抄的同阶对标（10 个）】（可直接复制玩法）
| 账号名 | 粉丝数 | 总互动数 | 推荐理由 |
|---|---|---|---|
| 账号名 | 3200 | 1.5w | 美食教程为主，更新稳定，互动量很高，爆文「5 分钟快手早餐…」获 2.3w 互动 |
| 账号名 | 2800 | 8500 | 日常 vlog 风格，有持续更新，有一定互动基础 |
| … | … | … | … |
👉 【可追赶的高阶标杆（2 个）】（模式成熟可参考）
| 账号名 | 粉丝数 | 总互动数 | 推荐理由 |
|---|---|---|---|
| 账号名 | 1.2w | 8.6w | 粉丝 1.2w，已具备变现能力，测评类内容，日更勤快 |
| … | … | … | … |
📊 分析总结：
- 同阶对标账号平均粉丝数：2.9k，平均互动量：1.1w
- 高阶标杆账号平均粉丝数：1.2w，平均互动量：8.6w
- 建议优先参考：同阶对标中的高频更新账号，学习其内容节奏和选题方向
📬 订阅服务 1️⃣ 是否订阅"现查询条件"的对标账号推送，每日下午 7 点更新最新数据。你可选择推送频率和时间~ 2️⃣ 暂不需要
---
使用场景
| 场景 | 角色 | 示例问法 | 收益 |
|---|---|---|---|
| 新号找对标学习 | 新人博主 | "我是做美食的，3000 粉素人，帮我找对标" | 快速找到可复制的运营套路 |
| 批量筛选达人 | 品牌方 / 商务 | "穿搭赛道腰部 kol，推荐些互动好的账号" | 高效筛选 KOL 投放候选人 |
| 制定内容策略 | 内容运营 / MCN | "健身赛道 1 万粉的账号，哪些做得好？" | 掌握赛道头部玩法，优化选题方向 |
| 起号方向参考 | 起号参考者 | "宠物赛道小白，有哪些可以学的账号？" | 降低起号迷茫感，明确内容方向 |
| 监控对标变化 | 运营团队 | "每天推送美食赛道对标账号给我" | 自动化追踪对标账号最新数据 |
---
重要数据说明
支持赛道（25 个）
综合全部、出行代步、休闲爱好、影视娱乐、数码科技、医疗保健、综合杂项、星座情感、时尚穿搭、婚庆婚礼、拍摄记录、学习教育、化妆美容、居家装修、旅行度假、亲子育儿、个人护理、美味佳肴、职业发展、宠物天地、潮流鞋包、日常生活、科学探索、新闻资讯、体育锻炼
数据时效
推荐账号数据来源于 API 接口，数据获取时间为入库时刻，与小红书平台实时数据存在差异。
<!DOCTYPE html>
<html lang="zh-CN">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>小红书对标账号推荐</title>
<style>
* { margin: 0; padding: 0; box-sizing: border-box; }
body { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, sans-serif; background: #f5f5f5; padding: 20px; line-height: 1.6; }
.container { max-width: 1000px; margin: 0 auto; background: #fff; border-radius: 12px; box-shadow: 0 2px 12px rgba(0,0,0,0.1); padding: 32px; }
/* 开场白样式 */
.intro { margin-bottom: 24px; }
.intro-text { font-size: 16px; color: #333; margin-bottom: 8px; }
.data-note { font-size: 14px; color: #999; background: #f9f9f9; padding: 8px 12px; border-radius: 4px; display: inline-block; }
/* 表格标题样式 */
.section { margin-bottom: 32px; }
.section-title { font-size: 18px; font-weight: bold; color: #333; margin-bottom: 12px; }
.section-subtitle { font-size: 14px; color: #666; margin-bottom: 16px; }
/* 表格样式 */
table { width: 100%; border-collapse: collapse; }
th, td { padding: 14px 12px; text-align: left; border-bottom: 1px solid #f0f0f0; }
th { background: #fafafa; font-weight: 600; color: #333; font-size: 14px; }
td { color: #666; font-size: 14px; vertical-align: top; }
tr:hover { background: #fafafa; }
a { color: #ff2442; text-decoration: none; }
a:hover { text-decoration: underline; }
.reason { font-size: 13px; color: #888; line-height: 1.6; }
/* 列宽 */
.col-name { width: 18%; }
.col-fans { width: 10%; }
.col-interact { width: 10%; }
.col-reason { width: 62%; }
</style>
</head>
<body>
<div class="container">
<!-- 开场白 -->
<div class="intro">
<div class="intro-text">{{intro_text}}</div>
<div class="data-note">{{data_note}}</div>
</div>
<!-- 同阶对标表格 -->
{{#same_level_section}}
<div class="section">
<div class="section-title">{{same_level_title}}</div>
<div class="section-subtitle">{{same_level_subtitle}}</div>
<table>
<thead>
<tr>
<th class="col-name">账号名</th>
<th class="col-fans">粉丝数</th>
<th class="col-interact">总互动数</th>
<th class="col-reason">推荐理由</th>
</tr>
</thead>
<tbody>
{{#same_level_accounts}}
<tr>
<td><a href="{{url}}" target="_blank">{{nickname}}</a></td>
<td>{{fans}}</td>
<td>{{total_interactive}}</td>
<td class="reason">{{reason}}</td>
</tr>
{{/same_level_accounts}}
</tbody>
</table>
</div>
{{/same_level_section}}
<!-- 高阶标杆表格 -->
{{#high_level_section}}
<div class="section">
<div class="section-title">{{high_level_title}}</div>
<div class="section-subtitle">{{high_level_subtitle}}</div>
<table>
<thead>
<tr>
<th class="col-name">账号名</th>
<th class="col-fans">粉丝数</th>
<th class="col-interact">总互动数</th>
<th class="col-reason">推荐理由</th>
</tr>
</thead>
<tbody>
{{#high_level_accounts}}
<tr>
<td><a href="{{url}}" target="_blank">{{nickname}}</a></td>
<td>{{fans}}</td>
<td>{{total_interactive}}</td>
<td class="reason">{{reason}}</td>
</tr>
{{/high_level_accounts}}
</tbody>
</table>
</div>
{{/high_level_section}}
</div>
</body>
</html>
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
小红书账号推荐脚本
功能：调用API查询对标账号
⚠️ 严禁任何联网搜索：本脚本不使用web_search等联网查询工具，所有数据仅来源于API接口
"""
import argparse
import json
import os
import sys
import urllib.request
import urllib.error
import ssl
# 标准赛道分类
STANDARD_CATEGORIES = [
"综合全部", "出行代步", "休闲爱好", "影视娱乐", "数码科技",
"医疗保健", "综合杂项", "星座情感", "时尚穿搭",
"婚庆婚礼", "拍摄记录", "学习教育", "化妆美容",
"居家装修", "旅行度假", "亲子育儿", "个人护理",
"美味佳肴", "职业发展", "宠物天地", "潮流鞋包",
"日常生活", "科学探索", "新闻资讯", "体育锻炼"
]
# 标准账号等级（7个）
STANDARD_LEVELS = ["明星", "品牌", "企业", "头部kol", "腰部kol", "尾部kol", "素人"]
# 账号等级中文到标准分类的映射
LEVEL_MAPPING = {
# 明星相关
"明星": "明星",
"艺人": "明星",
"爱豆": "明星",
"idol": "明星",
# 品牌相关
"品牌": "品牌",
"牌子": "品牌",
# 企业相关
"企业": "企业",
"公司": "企业",
"商家": "企业",
"官方号": "企业",
# 头部KOL相关
"头部kol": "头部kol",
"头部": "头部kol",
"头部达人": "头部kol",
"大v": "头部kol",
"大号": "头部kol",
"百万粉": "头部kol",
# 腰部KOL相关
"腰部kol": "腰部kol",
"腰部": "腰部kol",
"腰部达人": "腰部kol",
"中号": "腰部kol",
"十万粉": "腰部kol",
# 尾部KOL相关
"尾部kol": "尾部kol",
"尾部": "尾部kol",
"尾部达人": "尾部kol",
"小号": "尾部kol",
"万粉": "尾部kol",
# 素人相关
"素人": "素人",
"普通人": "素人",
"小白": "素人",
"新手": "素人",
"新人": "素人",
"新手博主": "素人",
"个人号": "素人"
}
# 用户常用赛道词到标准分类的映射
CATEGORY_MAPPING = {
# 个人护理相关
"护肤": "个人护理",
"美容": "个人护理",
"护肤美容": "个人护理",
"护肤品": "个人护理",
"保养": "个人护理",
"面部护理": "个人护理",
"皮肤": "个人护理",
# 美妆相关
"美妆": "化妆美容",
"化妆": "化妆美容",
"彩妆": "化妆美容",
"平价美妆": "化妆美容",
"美妆教程": "化妆美容",
# 时尚穿搭相关
"穿搭": "时尚穿搭",
"时尚": "时尚穿搭",
"服装": "时尚穿搭",
"衣服": "时尚穿搭",
"搭配": "时尚穿搭",
"潮流穿搭": "时尚穿搭",
# 美食相关
"美食": "美味佳肴",
"做饭": "美味佳肴",
"烹饪": "美味佳肴",
"菜谱": "美味佳肴",
"烘焙": "美味佳肴",
"探店": "美味佳肴",
"餐厅": "美味佳肴",
# 旅行相关
"旅行": "旅行度假",
"旅游": "旅行度假",
"游记": "旅行度假",
"攻略": "旅行度假",
"酒店": "旅行度假",
# 家居装修相关
"家居": "居家装修",
"装修": "居家装修",
"改造": "居家装修",
"租房改造": "居家装修",
"软装": "居家装修",
# 健身运动相关
"健身": "体育锻炼",
"运动": "体育锻炼",
"减肥": "体育锻炼",
"瑜伽": "体育锻炼",
"瘦身": "体育锻炼",
# 母婴育儿相关
"母婴": "亲子育儿",
"育儿": "亲子育儿",
"宝妈": "亲子育儿",
"宝宝": "亲子育儿",
"亲子": "亲子育儿",
# 宠物相关
"宠物": "宠物天地",
"猫": "宠物天地",
"狗": "宠物天地",
"萌宠": "宠物天地",
"养猫": "宠物天地",
"养狗": "宠物天地",
# 数码科技相关
"数码": "数码科技",
"手机": "数码科技",
"科技": "数码科技",
"电脑": "数码科技",
"测评": "数码科技",
"互联网": "数码科技",
"AI": "数码科技",
"人工智能": "数码科技",
"编程": "数码科技",
"软件": "数码科技",
"硬件": "数码科技",
"智能": "数码科技",
"机器人": "数码科技",
"芯片": "数码科技",
"电子": "数码科技",
"游戏": "数码科技",
"电竞": "数码科技",
# 教育学习相关
"教育": "学习教育",
"学习": "学习教育",
"考研": "学习教育",
"考公": "学习教育",
"英语": "学习教育",
# 情感相关
"情感": "星座情感",
"恋爱": "星座情感",
"星座": "星座情感",
"心理": "星座情感",
# 职场相关
"职场": "职业发展",
"工作": "职业发展",
"求职": "职业发展",
"面试": "职业发展",
# 其他
"vlog": "日常生活",
"日常": "日常生活",
"生活": "日常生活",
}
def match_level(user_input):
"""
匹配用户输入到标准账号等级（中文）
Args:
user_input: 用户输入的账号等级描述
Returns:
匹配的标准账号等级（如："素人"、"头部kol"）或None
"""
if not user_input:
return None
# 直接匹配映射表
for keyword, level in LEVEL_MAPPING.items():
if keyword in user_input:
return level
# 模糊匹配标准分类
for level in STANDARD_LEVELS:
if level in user_input:
return level
# 没有匹配到，返回None
return None
def match_category(user_input):
"""
匹配用户输入到标准赛道分类
Args:
user_input: 用户输入的赛道描述
Returns:
匹配的标准分类
"""
if not user_input:
return "综合全部"
# 直接匹配映射表
for keyword, category in CATEGORY_MAPPING.items():
if keyword in user_input:
return category
# 模糊匹配标准分类
for category in STANDARD_CATEGORIES:
if category in user_input:
return category
# 默认返回"综合全部"
return "综合全部"
def get_api_key():
"""从当前环境变量获取 REDFOX_API_KEY"""
api_key = os.getenv("REDFOX_API_KEY")
if not api_key:
print("❌ 未找到 REDFOX_API_KEY，请配置环境变量：export REDFOX_API_KEY=<你的apikey>", file=sys.stderr)
sys.exit(1)
return api_key
def query_similar_accounts(redId=None, track=None, maxFans=None, minFans=None, level=None):
"""
调用API查询对标账号（统一接口，支持5个参数）
Args:
redId: 小红书账号ID
track: 赛道类型
maxFans: 最大粉丝量
minFans: 最小粉丝量
level: 账号等级（中文：明星、品牌、企业、头部kol、腰部kol、尾部kol、素人）
Returns:
同阶对标和高阶标杆账号列表
"""
url = "https://redfox.hk/story/api/xhsUser/querySimilarAccounts"
# 获取凭证（环境变量 > shell配置文件 > 提示用户配置）
api_key = get_api_key()
# 构建请求参数
payload = {
"redId": redId if redId else "",
"track": track if track else "",
"maxFans": maxFans if maxFans else "",
"minFans": minFans if minFans is not None else "",
"level": level if level else "",
"source": "小红书对标账号-GitHub"
}
headers = {
"Content-Type": "application/json",
"X-API-KEY": api_key
}
# 原生 urllib POST 请求（verify=False）
data = json.dumps(payload).encode("utf-8")
req = urllib.request.Request(url, data=data, headers=headers, method="POST")
ssl_ctx = ssl.create_default_context()
ssl_ctx.check_hostname = False
ssl_ctx.verify_mode = ssl.CERT_NONE
try:
with urllib.request.urlopen(req, context=ssl_ctx, timeout=30) as resp:
result = json.loads(resp.read().decode("utf-8"))
except urllib.error.HTTPError as e:
raise Exception(f"HTTP请求失败: {e.code}, {e.read().decode('utf-8', errors='replace')}")
except urllib.error.URLError as e:
raise Exception(f"请求失败: {e.reason}")
# 检查返回数据
if "data" not in result:
raise Exception(f"API返回格式错误: {result}")
data = result.get("data")
# 处理data为None的情况
if data is None:
msg = result.get("msg", "未知错误")
raise Exception(f"API返回错误: {msg}")
same_level_accounts = data.get("sameLevelAccounts", [])
high_level_accounts = data.get("highLevelAccounts", [])
return same_level_accounts, high_level_accounts
def format_number(num):
"""
格式化数字（万粉显示为w+）
"""
if num is None:
return "0"
if num >= 10000:
return f"{round(num / 10000, 1)}w"
return str(num)
def format_level(level):
"""
格式化账号等级（空值显示为"无"）
"""
if level is None or level == "":
return "无"
return level
def generate_recommendation_reason(account):
"""
生成推荐理由（基于账号所有数据综合分析，不少于20字）
分析维度：粉丝规模、更新频率、互动表现、内容方向、爆文案例、账号等级、作品特点
"""
works = account.get("works") or []
fans = account.get("fans") or 0
note_count = account.get("noteCountSeven") or 0
collected = account.get("collected") or 0
liked = account.get("liked") or 0
total_interactive = collected + liked
total_work = account.get("totalWork") or 0
level = account.get("level") or ""
interactive_seven = account.get("interactiveCountSeven") or 0
# 收集分析点
analysis_points = []
# 1. 粉丝规模分析
if fans >= 500000:
analysis_points.append(f"粉丝规模达{format_number(fans)}，头部影响力")
elif fans >= 100000:
analysis_points.append(f"粉丝{format_number(fans)}，中腰部成熟账号")
elif fans >= 10000:
analysis_points.append(f"粉丝{format_number(fans)}，已具备变现能力")
elif fans >= 1000:
analysis_points.append(f"粉丝{format_number(fans)}，成长期账号")
elif fans > 0:
analysis_points.append(f"粉丝{format_number(fans)}，起号阶段潜力股")
# 2. 更新频率分析
if note_count >= 7:
analysis_points.append("日更勤快，运营活跃度高")
elif note_count >= 4:
analysis_points.append("周更稳定，持续运营中")
elif note_count >= 1:
analysis_points.append("有持续更新")
# 3. 互动表现分析
if interactive_seven >= 10000:
analysis_points.append(f"近7天互动{format_number(interactive_seven)}，热度很高")
elif interactive_seven >= 1000:
analysis_points.append(f"近7天互动{format_number(interactive_seven)}，活跃度不错")
if total_interactive >= 100000:
analysis_points.append(f"累计互动{format_number(total_interactive)}，爆款潜力账号")
elif total_interactive >= 10000:
analysis_points.append(f"累计互动{format_number(total_interactive)}，内容质量稳定")
elif total_interactive >= 1000:
analysis_points.append("有一定互动基础")
# 4. 作品内容方向分析
if works:
titles = [w.get("title", "") for w in works if w.get("title")]
descs = [w.get("desc", "") for w in works if w.get("desc")]
all_text = " ".join(titles + descs)
# 内容方向识别
content_directions = []
if "测评" in all_text or "评测" in all_text:
content_directions.append("测评类")
if "教程" in all_text or "干货" in all_text or "攻略" in all_text:
content_directions.append("干货教程")
if "平价" in all_text or "学生党" in all_text or "便宜" in all_text:
content_directions.append("平价推荐")
if "避雷" in all_text or "踩坑" in all_text:
content_directions.append("避雷测评")
if "开箱" in all_text:
content_directions.append("开箱分享")
if "日常" in all_text or "vlog" in all_text.lower():
content_directions.append("日常vlog")
if "好物" in all_text or "推荐" in all_text:
content_directions.append("好物推荐")
if content_directions:
analysis_points.append("内容方向：" + "/".join(content_directions[:2]))
# 5. 爆文案例
best_work = None
best_interactive = 0
for w in works:
interactive = (w.get("likedCount") or 0) + (w.get("collectedCount") or 0)
if interactive > best_interactive:
best_interactive = interactive
best_work = w
if best_work and best_interactive >= 100:
title = best_work.get("title", "")
if title and len(title) >= 3:
short_title = title[:10] + "..." if len(title) > 10 else title
analysis_points.append(f"爆文「{short_title}」获{format_number(best_interactive)}互动")
# 6. 标题特征分析
if titles:
title_features = []
# 带数字的标题
has_number = sum(1 for t in titles if any(c.isdigit() for c in t))
if has_number >= len(titles) * 0.5:
title_features.append("善用数字标题")
# 带问号的标题
has_question = sum(1 for t in titles if "？" in t or "?" in t)
if has_question >= len(titles) * 0.3:
title_features.append("标题带问句吸引点击")
if title_features:
analysis_points.append("、".join(title_features))
# 7. 账号等级分析
level_tips = {
"头部kol": "头部KOL，商业价值高",
"腰部kol": "腰部达人，性价比优选",
"尾部kol": "尾部达人，成长潜力大",
"素人": "素人博主，真实种草力强"
}
if level in level_tips:
analysis_points.append(level_tips[level])
# 8. 作品数量分析
if total_work >= 500:
analysis_points.append(f"作品{total_work}篇，内容积累丰富")
elif total_work >= 100:
analysis_points.append(f"作品{total_work}篇，运营经验足")
# 组合推荐理由，确保不少于20字
if len(analysis_points) >= 3:
# 取前3-4个要点组合
result = "，".join(analysis_points[:4])
elif len(analysis_points) >= 1:
result = "，".join(analysis_points)
# 如果字数不够，补充基础信息
if len(result) < 20:
if fans > 0:
result += f"，粉丝{format_number(fans)}"
if total_interactive > 0:
result += f"，互动{format_number(total_interactive)}"
else:
# 兜底：使用基础数据
result = f"粉丝{format_number(fans)}，互动{format_number(total_interactive)}"
if note_count > 0:
result += f"，近7天更新{note_count}篇"
# 确保不少于20字
if len(result) < 20:
result += "，可参考学习其运营方式"
return result
def generate_content_analysis(account):
"""
生成发文特点分析
分析标题特征、内容特征、情绪方向、定位策略
"""
analysis_parts = []
works = account.get("works", [])
if not works:
return "暂无近期作品数据"
# 分析标题特征
titles = [w.get("title", "") for w in works if w.get("title")]
if titles:
# 提取标题中的关键词模式
title_features = []
# 检查是否带数字
has_number = any(any(c.isdigit() for c in t) for t in titles)
if has_number:
title_features.append("标题带数字")
# 检查是否带问号/感叹号
has_question = any("？" in t or "?" in t for t in titles)
if has_question:
title_features.append("标题带问句")
# 检查标题长度
avg_len = sum(len(t) for t in titles) / len(titles)
if avg_len < 10:
title_features.append("标题简短")
elif avg_len > 20:
title_features.append("标题详细")
if title_features:
analysis_parts.append("标题特征：" + "、".join(title_features))
# 分析内容特征
descs = [w.get("desc", "") for w in works if w.get("desc")]
if descs:
content_features = []
# 检查内容长度
avg_desc_len = sum(len(d) for d in descs) / len(descs)
if avg_desc_len > 200:
content_features.append("内容详实")
elif avg_desc_len < 50:
content_features.append("内容简洁")
# 检查是否带话题标签
has_hashtag = any("#" in d for d in descs)
if has_hashtag:
content_features.append("善用话题标签")
if content_features:
analysis_parts.append("内容特征：" + "、".join(content_features))
# 分析情绪方向
if titles or descs:
emotion_hints = []
# 正面情绪词
positive_words = ["推荐", "好用", "必买", "必看", "干货", "分享", "攻略"]
# 负面/警示情绪词
negative_words = ["避雷", "踩坑", "千万别", "不要", "教训"]
# 疑问/互动情绪词
interactive_words = ["怎么", "如何", "为什么", "你觉得", "大家"]
all_text = " ".join(titles + descs)
if any(word in all_text for word in positive_words):
emotion_hints.append("正向分享风格")
if any(word in all_text for word in negative_words):
emotion_hints.append("避雷警示风格")
if any(word in all_text for word in interactive_words):
emotion_hints.append("互动问答风格")
if emotion_hints:
analysis_parts.append("情绪方向：" + "、".join(emotion_hints))
# 分析定位策略
if works:
# 计算互动率
total_interactive = sum(
(w.get("likedCount") or 0) + (w.get("collectedCount") or 0) + (w.get("sharedCount") or 0)
for w in works
)
strategy_hints = []
if total_interactive > 0:
# 找出互动最高的作品
best_work = max(works, key=lambda w: (
(w.get("likedCount") or 0) + (w.get("collectedCount") or 0) + (w.get("sharedCount") or 0)
))
best_title = best_work.get("title", "")
if best_title:
strategy_hints.append(f"爆款方向：「{best_title[:15]}...」" if len(best_title) > 15 else f"爆款方向：「{best_title}」")
# 统计封面特征
covers = [w.get("cover", "") for w in works if w.get("cover")]
if covers:
strategy_hints.append("首图统一风格")
if strategy_hints:
analysis_parts.append("定位策略：" + "、".join(strategy_hints))
return "；".join(analysis_parts) if analysis_parts else "可参考该账号的内容方向和运营策略"
def format_table(accounts, title_line):
"""
格式化账号表格（严格按照模版）
"""
lines = []
lines.append(title_line)
lines.append("")
lines.append("| 账号名 | 粉丝数 | 近30天互动数 | 推荐理由 |")
lines.append("| --- | --- | --- | --- |")
for account in accounts:
nickname = account.get("nickname") or "未知"
url = account.get("url") or "#"
fans = format_number(account.get("fans"))
interactive_thirty = format_number(account.get("interactiveCountThirty") or 0)
# 生成推荐理由
recommendation = generate_recommendation_reason(account)
# 账号名做成超链接
account_link = f"[{nickname}]({url})"
lines.append(f"| {account_link} | {fans} | {interactive_thirty} | {recommendation} |")
return "\n".join(lines)
def format_account_detail(account, index):
"""
格式化账号详情（严格按照模版）
"""
lines = []
nickname = account.get("nickname") or "未知"
url = account.get("url") or "#"
fans = format_number(account.get("fans"))
collected = account.get("collected") or 0
liked = account.get("liked") or 0
total_interactive = format_number(collected + liked)
# 按模版格式
lines.append(f"{index}. 账号名：[{nickname}]({url})")
lines.append(f" | 粉丝：{fans} | 总互动：{total_interactive} |")
# 推荐理由
recommendation = generate_recommendation_reason(account)
lines.append(f" ✅ 推荐理由：{recommendation}")
# 发文特点
content_analysis = generate_content_analysis(account)
lines.append(f" 📝 发文特点：{content_analysis}")
return "\n".join(lines)
def get_earliest_gmt_create(same_level_accounts, high_level_accounts):
"""
获取所有账号中最早的gmtCreate时间
"""
all_accounts = (same_level_accounts or []) + (high_level_accounts or [])
gmt_creates = []
for account in all_accounts:
gmt = account.get("gmtCreate")
if gmt:
gmt_creates.append(gmt)
if not gmt_creates:
return "入库时刻"
# 返回最早的时间（字符串排序即可，格式为 "YYYY-MM-DD HH:MM:SS"）
return min(gmt_creates)
def generate_analysis_summary(same_level_accounts, high_level_accounts):
"""
生成分析总结（严格按照模版）
"""
lines = []
lines.append("📊 **分析总结**：")
if same_level_accounts:
avg_fans = sum(a.get("fans") or 0 for a in same_level_accounts) / len(same_level_accounts)
interactive_total = sum(a.get("interactiveCountThirty") or 0 for a in same_level_accounts)
avg_interactive = interactive_total / len(same_level_accounts)
lines.append(f"- 同阶对标账号平均粉丝数：{format_number(avg_fans)}，平均互动量：{format_number(avg_interactive)}")
if high_level_accounts:
avg_fans = sum(a.get("fans") or 0 for a in high_level_accounts) / len(high_level_accounts)
interactive_total = sum(a.get("interactiveCountThirty") or 0 for a in high_level_accounts)
avg_interactive = interactive_total / len(high_level_accounts)
lines.append(f"- 高阶标杆账号平均粉丝数：{format_number(avg_fans)}，平均互动量：{format_number(avg_interactive)}")
if same_level_accounts or high_level_accounts:
lines.append("- 建议优先参考：同阶对标中的高频更新账号，学习其内容节奏和选题方向")
return "\n".join(lines)
def format_output(same_level_accounts, high_level_accounts, gmt_create=None):
"""
格式化输出结果（严格按照模版顺序）
"""
output_lines = []
# 获取最早的gmtCreate时间
earliest_gmt = get_earliest_gmt_create(same_level_accounts, high_level_accounts)
# 1. 提示语（只显示有数据的组）
same_count = len(same_level_accounts) if same_level_accounts else 0
high_count = len(high_level_accounts) if high_level_accounts else 0
tips_parts = []
if same_count > 0:
tips_parts.append(f"【可直接抄的同阶对标（{same_count}个）】")
if high_count > 0:
tips_parts.append(f"【可追赶的高阶标杆（{high_count}个）】")
if tips_parts:
output_lines.append(f"✨ 为你匹配到{'和'.join(tips_parts)}的{len(tips_parts)}组对标，可按需参考：")
else:
output_lines.append("✨ 暂未匹配到符合条件的对标账号，请尝试调整筛选条件。")
output_lines.append(f"| 数据说明：数据获取时间为{earliest_gmt}，和实时数据存在差别。")
# 2. 同阶对标表格（有数据才展示）
if same_level_accounts:
table_title = f"👉 【可直接抄的同阶对标（{len(same_level_accounts)}个）】（可直接复制玩法）"
output_lines.append(format_table(same_level_accounts, table_title))
# 3. 高阶标杆表格（有数据才展示）
if high_level_accounts:
table_title = f"👉 【可追赶的高阶标杆（{len(high_level_accounts)}个）】（模式成熟可参考）"
output_lines.append(format_table(high_level_accounts, table_title))
# 4. 分析总结（有数据才展示）
if same_level_accounts or high_level_accounts:
output_lines.append("")
output_lines.append(generate_analysis_summary(same_level_accounts, high_level_accounts))
# 5. 订阅服务
output_lines.append("")
output_lines.append("📬 **订阅服务**")
output_lines.append(' 1️⃣ 是否订阅"现查询条件"的对标账号推送，每日下午7点更新最新数据。你可选择推送频率和时间~')
output_lines.append(" 2️⃣ 暂不需要")
return "\n".join(output_lines)
def generate_html(same_level_accounts, high_level_accounts, gmt_create):
"""
使用模版生成HTML报告文件，内容与输出格式完全一致
"""
import os
earliest_gmt = get_earliest_gmt_create(same_level_accounts, high_level_accounts)
same_count = len(same_level_accounts) if same_level_accounts else 0
high_count = len(high_level_accounts) if high_level_accounts else 0
# 开场白
intro_parts = []
if same_count > 0:
intro_parts.append(f"【可直接抄的同阶对标（{same_count}个）】")
if high_count > 0:
intro_parts.append(f"【可追赶的高阶标杆（{high_count}个）】")
if intro_parts:
intro_text = f"✨ 为你匹配到{'和'.join(intro_parts)}的{len(intro_parts)}组对标，可按需参考："
else:
intro_text = "✨ 暂未匹配到符合条件的对标账号，请尝试调整筛选条件。"
data_note = f"数据说明：数据获取时间为{earliest_gmt}，和实时数据存在差别。"
# 同阶对标HTML
same_level_html = ""
if same_level_accounts:
same_level_title = f"👉 【可直接抄的同阶对标（{same_count}个）】"
same_level_subtitle = "可直接复制玩法"
rows = ""
for acc in same_level_accounts:
nickname = acc.get("nickname") or "未知"
url = acc.get("url") or "#"
fans = format_number(acc.get("fans"))
interactive_thirty = format_number(acc.get("interactiveCountThirty") or 0)
reason = generate_recommendation_reason(acc)
rows += f''' <tr>
<td><a href="{url}" target="_blank">{nickname}</a></td>
<td>{fans}</td>
<td>{interactive_thirty}</td>
<td class="reason">{reason}</td>
</tr>
'''
same_level_html = f''' <div class="section">
<div class="section-title">{same_level_title}</div>
<div class="section-subtitle">{same_level_subtitle}</div>
<table>
<thead>
<tr>
<th class="col-name">账号名</th>
<th class="col-fans">粉丝数</th>
<th class="col-interact">近30天互动数</th>
<th class="col-reason">推荐理由</th>
</tr>
</thead>
<tbody>
{rows} </tbody>
</table>
</div>
'''
# 高阶标杆HTML
high_level_html = ""
if high_level_accounts:
high_level_title = f"👉 【可追赶的高阶标杆（{high_count}个）】"
high_level_subtitle = "模式成熟可参考"
rows = ""
for acc in high_level_accounts:
nickname = acc.get("nickname") or "未知"
url = acc.get("url") or "#"
fans = format_number(acc.get("fans"))
interactive_thirty = format_number(acc.get("interactiveCountThirty") or 0)
reason = generate_recommendation_reason(acc)
rows += f''' <tr>
<td><a href="{url}" target="_blank">{nickname}</a></td>
<td>{fans}</td>
<td>{interactive_thirty}</td>
<td class="reason">{reason}</td>
</tr>
'''
high_level_html = f''' <div class="section">
<div class="section-title">{high_level_title}</div>
<div class="section-subtitle">{high_level_subtitle}</div>
<table>
<thead>
<tr>
<th class="col-name">账号名</th>
<th class="col-fans">粉丝数</th>
<th class="col-interact">近30天互动数</th>
<th class="col-reason">推荐理由</th>
</tr>
</thead>
<tbody>
{rows} </tbody>
</table>
</div>
'''
# 分析总结HTML
summary_html = ""
if same_level_accounts or high_level_accounts:
summary_items = ""
if same_level_accounts:
avg_fans = sum(a.get("fans") or 0 for a in same_level_accounts) / len(same_level_accounts)
interactive_total = sum(a.get("interactiveCountThirty") or 0 for a in same_level_accounts)
avg_interactive = interactive_total / len(same_level_accounts)
summary_items += f' <div class="summary-item">• 同阶对标账号平均粉丝数：{format_number(avg_fans)}，平均互动量：{format_number(avg_interactive)}</div>\n'
if high_level_accounts:
avg_fans = sum(a.get("fans") or 0 for a in high_level_accounts) / len(high_level_accounts)
interactive_total = sum(a.get("interactiveCountThirty") or 0 for a in high_level_accounts)
avg_interactive = interactive_total / len(high_level_accounts)
summary_items += f' <div class="summary-item">• 高阶标杆账号平均粉丝数：{format_number(avg_fans)}，平均互动量：{format_number(avg_interactive)}</div>\n'
summary_items += ' <div class="summary-item">• 建议优先参考：同阶对标中的高频更新账号，学习其内容节奏和选题方向</div>\n'
summary_html = f''' <div class="summary-section">
<div class="summary-title">📊 **分析总结**：</div>
{summary_items} </div>
'''
# 完整HTML
html = f'''<!DOCTYPE html>
<html lang="zh-CN">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>小红书对标账号推荐</title>
<style>
* {{ margin: 0; padding: 0; box-sizing: border-box; }}
body {{ font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, sans-serif; background: #f5f5f5; padding: 20px; line-height: 1.6; }}
.container {{ max-width: 1000px; margin: 0 auto; background: #fff; border-radius: 12px; box-shadow: 0 2px 12px rgba(0,0,0,0.1); padding: 32px; }}
.intro {{ margin-bottom: 24px; }}
.intro-text {{ font-size: 16px; color: #333; margin-bottom: 8px; }}
.data-note {{ font-size: 14px; color: #999; background: #f9f9f9; padding: 8px 12px; border-radius: 4px; display: inline-block; }}
.section {{ margin-bottom: 32px; }}
.section-title {{ font-size: 18px; font-weight: bold; color: #333; margin-bottom: 12px; }}
.section-subtitle {{ font-size: 14px; color: #666; margin-bottom: 16px; }}
table {{ width: 100%; border-collapse: collapse; }}
th, td {{ padding: 14px 12px; text-align: left; border-bottom: 1px solid #f0f0f0; }}
th {{ background: #fafafa; font-weight: 600; color: #333; font-size: 14px; }}
td {{ color: #666; font-size: 14px; vertical-align: top; }}
tr:hover {{ background: #fafafa; }}
a {{ color: #ff2442; text-decoration: none; }}
a:hover {{ text-decoration: underline; }}
.reason {{ font-size: 13px; color: #888; line-height: 1.6; }}
.col-name {{ width: 18%; }}
.col-fans {{ width: 10%; }}
.col-interact {{ width: 10%; }}
.col-reason {{ width: 62%; }}
.summary-section {{ margin-bottom: 32px; }}
.summary-title {{ font-size: 16px; font-weight: bold; color: #333; margin-bottom: 16px; }}
.summary-item {{ font-size: 14px; color: #666; margin-bottom: 8px; padding-left: 16px; position: relative; }}
</style>
</head>
<body>
<div class="container">
<div class="intro">
<div class="intro-text">{intro_text}</div>
<div class="data-note">| {data_note}</div>
</div>
{same_level_html}{high_level_html}{summary_html} </div>
</body>
</html>'''
return html
def save_to_json(same_level_accounts, high_level_accounts, gmt_create, json_path):
"""
将所有数据保存到临时JSON文件
"""
data = {
"gmt_create": gmt_create,
"same_level_accounts": same_level_accounts or [],
"high_level_accounts": high_level_accounts or []
}
with open(json_path, "w", encoding="utf-8") as f:
json.dump(data, f, ensure_ascii=False, indent=2)
return json_path
def generate_html_from_json(json_path, html_path):
"""
从JSON文件读取数据并生成HTML文件
"""
with open(json_path, "r", encoding="utf-8") as f:
data = json.load(f)
same_level_accounts = data.get("same_level_accounts", [])
high_level_accounts = data.get("high_level_accounts", [])
gmt_create = data.get("gmt_create")
html_content = generate_html(same_level_accounts, high_level_accounts, gmt_create)
with open(html_path, "w", encoding="utf-8") as f:
f.write(html_content)
return html_path
def main():
parser = argparse.ArgumentParser(description="小红书账号推荐脚本")
parser.add_argument("--red_id", help="小红书账号ID")
parser.add_argument("--track", help="赛道类型（如：化妆美容、美味佳肴等）")
parser.add_argument("--max_fans", type=int, help="最大粉丝量")
parser.add_argument("--min_fans", type=int, help="最小粉丝量")
parser.add_argument("--level", default="素人", help="账号等级（明星、品牌、企业、头部kol、腰部kol、尾部kol、素人），默认素人")
args = parser.parse_args()
# 判断是否为高等级账号（头部kol、企业、明星、品牌），只展示同阶对标
high_level_types = ["头部kol", "企业", "明星", "品牌"]
is_high_level_account = args.level and args.level in high_level_types
try:
same_level_accounts, high_level_accounts = query_similar_accounts(
redId=args.red_id,
track=args.track,
maxFans=args.max_fans,
minFans=args.min_fans,
level=args.level
)
# 如果是高等级账号，不展示高阶标杆
if is_high_level_account:
high_level_accounts = []
# 获取数据获取时间（取第一个账号的gmtCreate）
gmt_create = None
first_account = None
if same_level_accounts and len(same_level_accounts) > 0:
gmt_create = same_level_accounts[0].get("gmtCreate")
first_account = same_level_accounts[0]
elif high_level_accounts and len(high_level_accounts) > 0:
gmt_create = high_level_accounts[0].get("gmtCreate")
first_account = high_level_accounts[0]
# 生成文件名：账号名+时间戳（用户输入了具体账号）或 对标账号+时间戳（按条件查询）
import time
timestamp = int(time.time())
if args.red_id:
# 用户输入了具体账号ID，使用账号名+时间戳
if first_account and first_account.get("nickname"):
nickname = first_account.get("nickname", "")
safe_nickname = "".join(c for c in nickname if c.isalnum() or c in "-_")[:20]
html_filename = f"./{safe_nickname}_{timestamp}.html"
json_filename = f"./{safe_nickname}_{timestamp}.json"
else:
html_filename = f"./对标账号_{timestamp}.html"
json_filename = f"./对标账号_{timestamp}.json"
else:
# 按条件查询，使用"对标账号"+时间戳
html_filename = f"./对标账号_{timestamp}.html"
json_filename = f"./对标账号_{timestamp}.json"
# 1. 格式化输出（严格按照模版）
result = format_output(same_level_accounts, high_level_accounts, gmt_create)
print(result)
# 2. 将所有数据存入临时JSON文件（必须执行）
save_to_json(same_level_accounts, high_level_accounts, gmt_create, json_filename)
# 3. 读取JSON文件数据生成HTML文件（必须执行）
generate_html_from_json(json_filename, html_filename)
# 4. 输出结果（包含HTML路径供AI展示）
output = {
"status": "success",
"html_path": html_filename
}
print("")
print(json.dumps(output, ensure_ascii=False))
except Exception as e:
# 账号未收录或其他错误
print(f"查询失败: {str(e)}")
if __name__ == "__main__":
main()
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