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Agent Skill · 研究检索

traffic-analysis(流量分析)

traffic-analysis 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

traffic-analysis 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 39,065。

总安装 1,469周安装 60GitHub Stars 142下载 470核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:traffic-analysis(流量分析)
来源仓库:https://github.com/vivy-yi/xiaohongshu-skills
仓库路径:skills/traffic-analysis
安装命令:
npx skills add https://github.com/vivy-yi/xiaohongshu-skills --skill traffic-analysis
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/vivy-yi/xiaohongshu-skills --skill traffic-analysis

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

traffic-analysis 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。它支持基于关键词、任务场景或来源线索进行信息聚合与筛选,适用于流量分析研究。

通过 npx skills add 命令从指定 GitHub 仓库安装,需结合原始 README 确认具体用法。安装前应核实权限范围、维护状态,并评估是否涉及联网、命令执行或文件读写操作。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/traffic-analysis

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/vivy-yi/xiaohongshu-skills --skill traffic-ana…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

traffic-analysis 怎么安装?

在终端执行 npx skills add https://github.com/vivy-yi/xiaohongshu-skills --skill traffic-analysis。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

traffic-analysis 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

traffic-analysis 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

traffic-analysis 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

traffic-analysis 用的人多吗?

公开数据是累计安装 1,469 次,近一周安装 60 次,下载 470 次,GitHub 142 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

traffic-analysis 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
traffic-analysis

流量分析 · 本页

1,469142只读未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Traffic Analysis (流量分析)

Overview

Traffic analysis is the systematic examination of where Xiaohongshu content views come from, helping creators understand which traffic sources perform best and how to optimize content strategy for maximum reach and engagement.

When to Use

Use when:

  • Content views fluctuate unexpectedly
  • Need to understand which traffic sources are most valuable
  • Planning content optimization for specific acquisition channels
  • Comparing performance across different traffic sources
  • Diagnosing why content succeeded or failed
  • Optimizing posting strategy for better discovery

Do NOT use when:

  • Account has no published content yet
  • Looking for real-time minute-by-minute monitoring (use platform analytics)
  • Analyzing paid advertising performance (use dedicated ad analytics tools)

Core Pattern

Before (ignoring traffic sources):

❌ "My post got 1000 views, good job!"
❌ "Why did views drop? Must be the algorithm"
❌ "All traffic is the same, 100 views = 100 views"

After (traffic-source driven):

✅ "Post got 1000 views: 70% from search (high intent), 20% from discovery (broad), 10% from followers (loyal)"
✅ "Views dropped because discovery traffic fell 60%, but search traffic stayed strong - optimize cover for discovery"
✅ "Search traffic converts 3x better than discovery - double down on SEO-optimized titles"

4 Traffic Sources Framework:

  1. Discovery Page (发现页) - Algorithm-driven, broad reach, lower conversion
  2. Search Page (搜索页) - User intent, targeted, higher conversion
  3. Follow Page (关注页) - Existing followers, highest engagement, loyal audience
  4. Other Sources - Profile visits, shares, external links

Quick Reference

Traffic SourceCharacteristicsConversion RateOptimization Strategy
DiscoveryAlgorithm-recommended, viral potential2-4%Eye-catching covers, trending topics
SearchUser-initiated, specific intent6-10%SEO-rich titles, keyword optimization
FollowersLoyal audience, consistent10-15%Consistent posting, community engagement
ExternalShares, profile visitsVariesCross-platform promotion

Implementation

Step 1: Access Traffic Source Data

Xiaohongshu Creator Center (free, native):

  1. Open Creator Center app
  2. Navigate to: 数据分析 → 内容数据
  3. Select specific post
  4. View "流量来源" section

Qiangua Data (recommended for deeper analysis):

  1. Search your account
  2. Click "内容分析"
  3. View traffic source breakdown for each post
  4. Export data for Excel analysis

Step 2: Analyze Traffic Source Distribution

For each post, document:

  • Total views: Overall reach
  • Discovery traffic: Views from recommendation algorithm
  • Search traffic: Views from search results
  • Follower traffic: Views from existing followers
  • Other traffic: Profile visits, shares, external

Calculate percentages:

Discovery % = (Discovery views ÷ Total views) × 100
Search % = (Search views ÷ Total views) × 100
Follower % = (Follower views ÷ Total views) × 100

Step 3: Evaluate Traffic Quality by Source

Key metrics per traffic source:

  • Engagement rate: (Likes + Comments + Saves) ÷ Views
  • Follower conversion: New followers ÷ Views
  • Save rate: Saves ÷ Views (indicates content value)

Traffic quality assessment:

  • Search traffic: Usually 2-3x higher engagement (high user intent)
  • Follower traffic: Highest engagement, most loyal (10-15% engagement rate)
  • Discovery traffic: Lower engagement but viral potential (2-4% engagement rate)
  • External traffic: Highly variable, depends on source

Step 4: Identify Traffic Patterns

Analyze last 10-20 posts to find patterns:

Discovery-dominant posts (60%+ from discovery):

  • Indicate algorithm-friendly content
  • Viral potential but lower conversion
  • Optimization: Improve covers, use trending hashtags

Search-dominant posts (50%+ from search):

  • Indicate strong SEO/keyword optimization
  • Higher quality traffic
  • Optimization: Focus on keyword-rich titles, niche topics

Follower-dominant posts (70%+ from followers):

  • Indicate strong community loyalty
  • Limited growth potential (existing audience)
  • Optimization: Balance follower content with discovery-optimized content

Step 5: Optimize Content Strategy by Traffic Source

For Discovery Traffic:

  • Use eye-catching cover images
  • Include trending topics and hashtags
  • Post during peak hours (7-9pm)
  • Focus on broad appeal content

For Search Traffic:

  • Optimize titles with keywords
  • Use descriptive, searchable language
  • Focus on niche, specific topics
  • Target long-tail search terms

For Follower Traffic:

  • Maintain consistent posting schedule
  • Engage with comments consistently
  • Build community through series content
  • Respond to follower requests and questions

Step 6: Track Traffic Source Trends

Build weekly traffic source log:

Week | Total Views | Discovery % | Search % | Follower % | Best Source
-----|-------------|-------------|-----------|------------|------------
W1   | 5,000       | 60%         | 25%       | 15%        | Discovery
W2   | 8,000       | 70%         | 20%       | 10%        | Discovery
W3   | 4,000       | 40%         | 40%       | 20%        | Mixed

Trend analysis:

  • Discovery share growing? Algorithm likes your content
  • Search share growing? SEO optimization working
  • Follower share growing? Community loyalty increasing
  • All declining? Content may need refresh

Step 7: Diagnose Traffic Changes

Sudden traffic drop?

  1. Check which source declined
  2. Discovery drop: Algorithm may have shifted, test new content types
  3. Search drop: Competitors may target same keywords, diversify topics
  4. Follower drop: Posting frequency or engagement may have slipped

Sudden traffic spike?

  1. Identify winning source
  2. Analyze what made that post successful
  3. Replicate pattern across future content

Common Mistakes

MistakeWhy HappensFix
Ignoring traffic sources, only looking at total viewsEasier to track one numberAlways analyze traffic breakdown - 1000 discovery views ≠ 1000 search views
Assuming all traffic is equally valuableNot all views convert the sameTrack engagement/conversion by source - search converts 3x better
Optimizing for wrong traffic sourceChasing views instead of growthFocus on search traffic for sustainable growth, discovery for viral spikes
Neglecting follower trafficObsessed with new viewsLoyal followers provide consistent engagement - don't ignore them
Not tracking traffic source trends over timeReactive instead of proactiveBuild weekly traffic log to spot patterns before problems occur
Blaming "algorithm" for all changesEasy scapegoatCheck specific traffic source - algorithm only affects discovery traffic
Over-optimizing for discovery onlyHigh visibility, low conversionBalance discovery (volume) with search (quality) for sustainable growth

Real-World Impact

Case Study: Niche Beauty Account

  • Before: Averaged 2000 views/post, 60% from discovery, 3% engagement rate
  • Analysis: Discovered search traffic had 8% engagement vs 2% for discovery
  • Action: Shifted strategy to SEO-optimized titles, keyword-rich content
  • After: Averaged 3500 views/post, 45% from search, 6.5% overall engagement rate
  • Result: 75% more views, 117% higher engagement, faster follower growth

Data-Backed Insights:

  • Search traffic converts to followers at 3-5x the rate of discovery traffic
  • Posts with 50%+ search traffic grow followers 2x faster
  • Discovery-heavy accounts (80%+) experience more volatile, unpredictable performance
  • Balanced traffic sources (40-40-20) provide most stable, sustainable growth

Related Skills

REQUIRED: Use data-analytics (overall data analysis framework) REQUIRED: Use data-metrics-understanding (understand what metrics mean)

Recommended for deeper analysis:

  • qiangua-data - Advanced traffic analysis tools and benchmarks
  • content-performance-analysis - Analyze which content resonates with each traffic source
  • user-persona-analysis - Understand how different user segments discover your content

Use traffic-analysis BEFORE:

  • content-planning (align content with best traffic sources)
  • title-writing (optimize titles for search vs discovery)
  • cover-design (design covers for discovery algorithm)

Related traffic optimization skills:

  • algorithm-mechanism (understand how discovery algorithm works)
  • viral-strategy (optimize content for viral discovery traffic)

平台分布

Codex

36.77%
按下载量换算173

Claude

27.85%
按下载量换算131

Cursor

17.63%
按下载量换算83

Gemini CLI

8.52%
按下载量换算40

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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