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Agent Skill · 研究检索

content-performance-analysis(内容表现分析)

用于辅助文档、README、Markdown、说明文和内容稿件的整理与改写。它适合让 Agent 提炼结构、补齐章节、统一术语、检查链接或把零散材料整理成可读文档。使用时应保留项目已有事实、命令和路径,不要把未确认的信息写成确定结论;涉及对外文案时,还需要控制语气,避免过度营销或夸大能力。

content-performance-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,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 33,756。

总安装 2,305周安装 99GitHub Stars 142下载 808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:content-performance-analysis(内容表现分析)
来源仓库:https://github.com/vivy-yi/xiaohongshu-skills
仓库路径:skills/content-performance-analysis
安装命令:
npx skills add https://github.com/vivy-yi/xiaohongshu-skills --skill content-performance-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 content-performance-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。

系统性评估单个帖子与整体内容组合的表现差异。识别爆款共性特征与低效内容改进方向,支持格式对比分析。输出数据驱动的策略调整建议,如优化发布时间或选题角度。

安装方式:通过 GitHub 仓库安装,命令为 npx skills add https://github.com/vivy-yi/xiaohongshu-skills --skill content-performance-analysis。注意:适用于小红书等平台,需导入实际互动数据以提升准确性。

来源仓库内路径:skills/content-performance-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 content-per…;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

content-performance-analysis 怎么安装?

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

content-performance-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。

content-performance-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。

content-performance-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。

content-performance-analysis 用的人多吗?

公开数据是累计安装 2,305 次,近一周安装 99 次,下载 808 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。

content-performance-analysis 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

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。
  • 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。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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
content-performance-analysis

内容表现分析 · 本页

2,305142只读未明确有提醒

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

Content Performance Analysis (内容效果分析)

Overview

Content performance analysis is the systematic evaluation of individual posts and overall content portfolio to identify success patterns, understand what resonates with the audience, and make data-driven decisions about content strategy.

When to Use

Use when:

  • Post performance is inconsistent or unpredictable
  • Need to understand why certain content went viral
  • Identifying patterns in top-performing content
  • Recognizing underperforming content that needs improvement
  • Comparing different content formats (carousel vs video vs single image)
  • Deciding which content types to focus on
  • Planning content optimization based on past performance

Do NOT use when:

  • Account has fewer than 5 published posts (insufficient data)
  • Looking for real-time performance during first hours (wait 3-7 days)
  • Analyzing paid advertising performance (use ad analytics tools)

Core Pattern

Before (guessing what works):

❌ "This post should do well, I worked hard on it"
❌ "I don't know why this post went viral, lucky I guess"
❌ "All my content is pretty similar, performance is random"

After (data-driven content insights):

✅ "Top 5 posts all use carousel format with before/after structure"
✅ "Posts with question titles get 2.3x more comments than statement titles"
✅ "Video content underperforms images - shift strategy to graphic content"
✅ "Posts published on Tuesday outperform Sunday by 40%"

3 Analysis Dimensions Framework:

  1. Engagement Data - Likes, comments, shares, saves (audience response)
  2. Growth Data - New followers, profile visits (conversion impact)
  3. Viral Data - Exposure, discovery traffic (reach and algorithm favor)

Quick Reference

MetricWhat It RevealsGood BenchmarkAnalysis Method
Engagement RateContent resonance8-12% average(Likes+Comments+Shares+Saves)÷Views×100
Save RateContent value/reuse3-5% is goodSaves÷Views×100
Comment RateDiscussion spark2-4% averageComments÷Views×100
Follower ConversionContent converts to fans1-3%New Followers÷Views×100
Viral ScoreAlgorithm favorViews÷Followers>10 = viral hit

Implementation

Step 1: Collect Post Performance Data

From Xiaohongshu Creator Center:

  1. Open Creator Center → 内容数据
  2. Select time range (last 30 days recommended)
  3. Export or manually record data for each post:

- Title - Content type (image/video/carousel) - Publish date/time - Views (浏览量) - Likes (点赞数) - Comments (评论数) - Shares (转发数) - Saves (收藏数) - New followers gained

From Qiangua Data (recommended for efficiency):

  1. Account analysis → Content performance
  2. Export all posts with metrics to Excel
  3. Sort by different metrics to identify patterns

Step 2: Calculate Key Performance Indicators

For each post, calculate:

Engagement Rate:

Engagement Rate = (Likes + Comments + Shares + Saves) ÷ Views × 100
  • Excellent: >15%
  • Good: 8-15%
  • Average: 5-8%
  • Below Average: <5%

Save Rate (content value):

Save Rate = Saves ÷ Views × 100
  • Excellent: >7%
  • Good: 4-7%
  • Average: 2-4%
  • Low: <2%

Comment Rate (engagement depth):

Comment Rate = Comments ÷ Views × 100
  • Excellent: >5%
  • Good: 3-5%
  • Average: 1-3%
  • Low: <1%

Viral Score:

Viral Score = Views ÷ Follower Count
  • Viral Hit: >10 (reached 10x beyond existing audience)
  • Strong Performance: 5-10
  • Expected Performance: 1-5
  • Underperforming: <1

Step 3: Identify Top and Bottom Performing Content

Top Performers (analyze last 10-20 posts):

  1. Sort by Engagement Rate - Find top 5
  2. Sort by Viral Score - Find top 5
  3. Sort by Save Rate - Find top 5

Bottom Performers:

  1. Sort by Engagement Rate - Find bottom 5
  2. Identify posts with Viral Score <1 (underperformed existing audience)

Step 4: Extract Success Patterns from Top Content

Analyze top 5 posts for common patterns:

Content Format Patterns:

  • Single image vs carousel vs video
  • Carousel slide count (3-5 slides optimal)
  • Video length (under 60 seconds optimal)

Content Structure Patterns:

  • Hook/intro style (question, statement, before/after)
  • Main content organization (list, tutorial, story, comparison)
  • Call-to-action presence and type

Title Patterns:

  • Question vs statement
  • Length (short vs long)
  • Keyword usage
  • Emotional triggers (curiosity, urgency, benefit)

Visual Patterns:

  • Cover design style
  • Color scheme
  • Text overlay presence
  • Face presence vs product-only

Topic Patterns:

  • Content category (educational, entertainment, inspiration)
  • Specific subtopics
  • Target audience segment

Timing Patterns:

  • Day of week
  • Time of day
  • Seasonal relevance

Document findings:

Pattern: Carousel format
- Frequency in top 5: 4/5 posts (80%)
- Average engagement: 14.2%
- Common structure: Before/after transformation

Pattern: Question-based titles
- Frequency in top 5: 3/5 posts (60%)
- Average engagement: 13.8%
- Comment rate: 4.1% (above average)

Step 5: Diagnose Underperforming Content

For bottom 5 posts, analyze:

Content Quality Issues:

  • Low production value (poor images, bad lighting)
  • Unclear value proposition
  • Weak or confusing message
  • Inadequate detail or depth

Content Format Issues:

  • Suboptimal format for topic (e.g., complex topic in single image)
  • Wrong carousel length (too short or too long)
  • Video length issues (too long, boring intro)

Title and Cover Issues:

  • Uncompelling cover (low CTR)
  • Confusing or unappealing title
  • Mismatch between title and content
  • Lack of keywords for search

Targeting Issues:

  • Content too broad/narrow for audience
  • Off-brand or irrelevant to niche
  • Missing audience pain point

Timing Issues:

  • Posted at low-traffic times
  • Competing with major events/holidays
  • Seasonal mismatch

Create improvement plan for each underperforming post type:

Issue: Low CTR on single-image posts
Diagnosis: Covers lack visual hook, text-only, no face
Fix: Add face, use before/after format, bold overlay text
Test: Create 3 new posts with improved covers, measure CTR improvement

Step 6: Build Content Performance Matrix

Create a matrix to visualize performance by content type:

Content Type | Avg Engagement | Avg Viral Score | Post Count | Verdict
-------------|----------------|-----------------|------------|--------
Carousel     | 12.4%          | 6.2             | 8          | ⭐⭐⭐ Primary
Video        | 7.8%           | 3.1             | 5          | ⭐⭐ Secondary
Single Image | 9.2%           | 4.5             | 7          | ⭐⭐ Secondary

Strategy decisions:

  • Primary format (⭐⭐⭐): Use for 60-70% of content
  • Secondary format (⭐⭐): Use for 20-30% of content
  • Deprecated format: Phase out or significantly improve

Step 7: Develop Replicable Content Formulas

Based on top performers, create 3-5 proven content formulas:

Formula 1: Problem-Solution Tutorial

  • Format: 5-slide carousel
  • Title: Question-based ("Struggling with X?")
  • Structure: Problem → Solution → Step-by-step → Result → CTA
  • Performance: 14.2% engagement, 8.3 viral score
  • Best for: Educational content, how-to topics

Formula 2: Before-After Transformation

  • Format: 3-slide carousel
  • Title: Benefit-driven ("How I achieved X in Y days")
  • Structure: Before → Process → After
  • Performance: 15.8% engagement, 12.1 viral score
  • Best for: Transformation topics, results showcase

Formula 3: List-Based Tips

  • Format: 4-6 slide carousel
  • Title: Number-based ("7 tips for X")
  • Structure: Hook → 7 tips (1 per slide) → Summary → CTA
  • Performance: 11.3% engagement, 5.7 viral score
  • Best for: Tips sharing, quick value content

Use formulas to:

  • Plan content calendar
  • Maintain consistency
  • Scale content production
  • Predict performance

Step 8: Track Performance Trends Over Time

Build weekly content performance log:

Week | Posts | Avg Engagement | Top Format | Viral Hits | Insights
-----|-------|----------------|------------|------------|----------
W1   | 4     | 9.2%           | Carousel   | 1          | Carousels outperforming
W2   | 5     | 10.5%          | Video      | 2          | Video improvement working
W3   | 3     | 12.8%          | Carousel   | 1          | Question titles boosting comments

Trend analysis:

  • Engagement rate rising? Content strategy is working
  • Certain formats consistently winning? Double down
  • Viral hits increasing? Algorithm favor improving
  • Specific topics always perform well? Create series

Common Mistakes

MistakeWhy HappensFix
Judging content by views onlyViews are vanity metricFocus on engagement rate and saves - they indicate true resonance
Analyzing posts too soon (first 24 hours)Early data is misleadingWait 3-7 days for post to fully perform before analyzing
Comparing posts with different audience sizesUnfair comparisonUse engagement rate % not raw numbers for fair comparison
Ignoring outlier posts (viral hits)Assume they're flukesDeep analyze viral hits - they contain valuable insights
Changing strategy too frequentlyImpatience with slow growthCollect data from 10+ posts before changing strategy
Focusing on averages onlyAverages hide patternsLook at top/bottom 20% to extract winning/losing patterns
Not tracking by content formatMiss format-specific insightsAlways analyze by format (carousel vs video vs single)
Ignoring save rateSaves predict long-term successHigh save rate = evergreen content that drives ongoing traffic
Over-optimizing for one metricUnbalanced strategyBalance engagement (likes) with value (saves) and growth (followers)
Not documenting success formulasReinventing the wheelCreate 3-5 proven formulas, reuse consistently

Real-World Impact

Case Study: Fashion Account Turnaround

  • Before: 6.8% average engagement, unpredictable performance, no clear content direction
  • Analysis: Discovered carousels with before/after structure averaged 14.5% engagement vs 5.2% for single images
  • Action: Shifted 80% of content to carousel format using proven formulas
  • After 30 days: 11.2% average engagement (+65%), follower growth rate doubled, 3 viral hits
  • Key insight: Content format matters more than production quality

Data-Backed Insights:

  • Accounts that document and reuse content formulas grow 3x faster
  • Posts following proven formats outperform experimental content 70% of the time
  • High-save content (>7% save rate) continues getting views for 30+ days
  • Top-performing content often follows 1 of 5 predictable structures
  • Analyzing bottom performers reveals as much as analyzing top performers

Related Skills

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

Recommended for deeper analysis:

  • traffic-analysis - Analyze which traffic sources drive best content performance
  • content-planning - Apply insights to plan future content
  • viral-strategy - Optimize content for viral potential

Use content-performance-analysis BEFORE:

  • content-planning (align plan with proven content formulas)
  • cover-design (design covers based on top-performing patterns)
  • title-writing (write titles using successful patterns)
  • graphic-content-creation / short-video-production (create content in winning formats)

Skills that provide context:

  • account-positioning (ensure content aligns with positioning)
  • persona-building (maintain consistent voice across content)

平台分布

Codex

33.83%
按下载量换算273

Claude

33.7%
按下载量换算272

Cursor

19.03%
按下载量换算154

Gemini CLI

9.84%
按下载量换算80

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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