Claude-skill-registry content-performance-analyzer

Analyzes content marketing metrics to identify top performers, trends, and optimization opportunities. Use when reviewing blog posts, social media, or campaign performance. Accepts CSV data with engagement metrics and provides actionable insights.

install
source · Clone the upstream repo
git clone https://github.com/majiayu000/claude-skill-registry
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/content-performance-analyzer" ~/.claude/skills/majiayu000-claude-skill-registry-content-performance-analyzer && rm -rf "$T"
manifest: skills/data/content-performance-analyzer/SKILL.md
source content

Content Performance Analyzer

Transform raw content metrics into actionable insights for improving your content marketing strategy.

Capabilities

  • Analyze engagement metrics (views, clicks, shares, comments)
  • Identify top-performing content patterns
  • Calculate performance benchmarks
  • Detect content trends over time
  • Generate optimization recommendations
  • Compare performance across channels/formats

Supported Metrics

MetricDescriptionBenchmark Calculation
Views/ImpressionsTotal reachAverage, growth rate
Engagement Rate(Likes+Comments+Shares)/ReachIndustry comparison
Click-Through RateClicks/Impressions% benchmark
Time on PageAverage reading timeContent length correlation
Bounce RateSingle-page sessionsQuality indicator
Conversion RateDesired actions/Total visitorsGoal tracking

Instructions

  1. Import Data: Accept CSV or structured data with content metrics
  2. Validate Fields: Ensure required metrics are present
  3. Calculate KPIs: Compute averages, rates, and benchmarks
  4. Identify Patterns: Find top performers and common traits
  5. Trend Analysis: Detect performance changes over time
  6. Generate Recommendations: Provide actionable next steps

Input Format

CSV with these columns (minimum):

content_id,title,publish_date,content_type,views,engagement,clicks

Optional enhanced columns:

channel,category,word_count,time_on_page,conversions,shares,comments

Output Format

# Content Performance Report

## Executive Summary
- Total content pieces analyzed: X
- Date range: [start] to [end]
- Overall engagement rate: X%

## Top Performers
| Rank | Title | Views | Engagement Rate | Key Success Factor |
|------|-------|-------|-----------------|-------------------|
| 1 | ... | ... | ... | ... |

## Performance by Category
[Chart/Table of metrics by content type]

## Trends Identified
1. [Trend 1 with data support]
2. [Trend 2 with data support]

## Recommendations
1. **Quick Win**: [Immediate action]
2. **Strategic**: [Medium-term improvement]
3. **Experiment**: [Test suggestion]

## Detailed Metrics
[Full breakdown tables]

Example Usage

Input: CSV file with 30 days of blog post metrics

Analysis Request:

Analyze this content performance data and identify:
1. Top 5 performing posts by engagement rate
2. Best performing content categories
3. Optimal publish day/time patterns
4. Content length vs performance correlation
5. Recommendations for next month's content calendar

Analysis Types

1. Performance Ranking

  • Sort by chosen metric
  • Calculate percentile rankings
  • Identify outliers (over/under performers)

2. Comparative Analysis

  • Content type comparison
  • Time period comparison
  • Channel/platform comparison

3. Correlation Analysis

  • Length vs engagement
  • Publish time vs views
  • Topic vs conversion

4. Trend Detection

  • Week-over-week changes
  • Seasonal patterns
  • Growth/decline indicators

Best Practices

  1. Minimum Data: Need 10+ content pieces for meaningful analysis
  2. Time Range: 30+ days provides better trend visibility
  3. Consistent Metrics: Ensure same measurement methods
  4. Segment Analysis: Break down by type for deeper insights
  5. Action Focus: Every insight should lead to an action

Benchmarks Reference

Content TypeGood EngagementGreat Engagement
Blog Post2-3%>5%
Social Media1-3%>5%
Video3-5%>8%
Newsletter15-25% open>30% open

Limitations

  • Requires structured data input
  • Cannot access external analytics platforms directly
  • Benchmarks are industry averages; your baseline may differ
  • Correlation ≠ causation in trend analysis
  • Historical data quality affects insight accuracy