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fracabu

content-performance-analyzer

by fracabu

A production-ready framework for generating scalable Claude Code Agent Skills, transforming domain expertise into reusable AI capabilities.

1🍴 0📅 Dec 18, 2025

SKILL.md


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

Score

Total Score

75/100

Based on repository quality metrics

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