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traffic-analysis流量分析

Agent Skill

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。

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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。

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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

简介

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。

SKILL.md

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)

适合场景

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安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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

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