skills/anysiteio/agent-skills/anysite-content-analytics

anysite-content-analytics

SKILL.md

anysite Content Analytics

Measure and optimize content performance across social platforms using anysite MCP. Track engagement, identify top performers, and refine your content strategy.

Overview

  • Track post performance across Instagram, YouTube, LinkedIn, Twitter/X
  • Analyze engagement metrics (likes, comments, shares, views)
  • Identify top content and viral patterns
  • Benchmark against competitors for strategy insights
  • Optimize posting strategy based on data

Coverage: 80% - Strong for Instagram, YouTube, LinkedIn, Twitter, Reddit

Supported Platforms

  • Instagram: Posts, Reels, likes, comments, engagement rates
  • YouTube: Videos, views, likes, comments, watch time indicators
  • LinkedIn: Posts, articles, reactions, comments, shares
  • Twitter/X: Tweets, retweets, likes, replies
  • Reddit: Posts, upvotes, comments, awards

Quick Start

Step 1: Collect Content Data

Platform-specific:

  • Instagram: get_instagram_user_posts(username, count=50)
  • LinkedIn: get_linkedin_user_posts(urn, count=50)
  • Twitter: get_twitter_user_posts(user, count=100)
  • YouTube: get_youtube_channel_videos(channel, count=30)

Step 2: Analyze Engagement

Calculate metrics:

  • Engagement rate: (likes + comments + shares) / followers
  • Best performing content: Top 10% by engagement
  • Content types: Video vs. image vs. text
  • Posting frequency: Posts per week

Step 3: Identify Patterns

Look for:

  • Best posting times (day of week, time)
  • Top-performing topics/themes
  • Optimal content length
  • High-engagement formats

Step 4: Optimize Strategy

Based on findings:

  • Double down on top content types
  • Post more during peak engagement times
  • Replicate successful topics
  • Adjust content mix

Common Workflows

Workflow 1: Instagram Content Audit

Steps:

  1. Get All Posts
get_instagram_user_posts(username, count=100)
  1. Calculate Metrics
For each post:
- Engagement rate = (likes + comments) / follower_count
- Engagement per hour = engagement / hours_since_posted
- Content type (Reel, carousel, single image, video)
  1. Identify Top Performers
Sort by engagement rate
Top 10%: Analyze for common patterns
- Topics/themes
- Visual style
- Caption style and length
- Hashtag strategy
  1. Analyze Content Mix
Count by type:
- Reels: X% of posts, Y% of engagement
- Carousels: X% of posts, Y% of engagement
- Single images: X% of posts, Y% of engagement
  1. Benchmark Against Competitors
For each competitor:
  get_instagram_user_posts(competitor, count=50)
Compare:
- Posting frequency
- Engagement rates
- Content types
- Top themes

Expected Output:

  • Content performance report
  • Top 10 performing posts
  • Content type effectiveness
  • Posting frequency analysis
  • Competitive benchmark

Workflow 2: LinkedIn Content Strategy Analysis

Steps:

  1. Collect Post History
get_linkedin_user_posts(urn, count=100)
  1. Categorize Content
Group by type:
- Text-only posts
- Image posts
- Video posts
- Article shares
- LinkedIn articles
- Polls
  1. Analyze Engagement by Type
For each content type:
- Average reactions
- Average comments
- Average shares
- Engagement rate
  1. Topic Analysis
Extract themes from top posts:
- Industry insights
- Personal stories
- How-to/educational
- Company news
- Thought leadership
  1. Posting Timing Analysis
Group posts by:
- Day of week
- Time of day
Calculate average engagement for each group

Expected Output:

  • Best content types for engagement
  • Top topics by engagement
  • Optimal posting times
  • Content frequency recommendations

Workflow 3: YouTube Channel Performance Analysis

Steps:

  1. Get Channel Videos
get_youtube_channel_videos(channel, count=50)
  1. Analyze Each Video
For each video:
  get_youtube_video(video_id)

Metrics:
- Views
- Likes/dislikes
- Comments
- View velocity (views per day since upload)
  1. Identify Patterns
Analyze top 20% by views:
- Video length
- Titles (keywords, style)
- Thumbnail patterns
- Topics/themes
- Upload timing
  1. Engagement Analysis
Check comments:
  get_youtube_video_comments(video_id, count=100)

Analyze:
- Comment quality
- Questions asked
- Sentiment
- Engagement timing
  1. Content Mix Optimization
Compare:
- Long-form (>10 min) vs short (<5 min)
- Tutorial vs entertainment vs review
- Series vs one-offs

Expected Output:

  • Video performance rankings
  • Optimal video length
  • Best topics and formats
  • Title and thumbnail insights
  • Upload strategy recommendations

MCP Tools Reference

Instagram

  • get_instagram_user_posts(user, count) - Get posts with engagement
  • get_instagram_post(post_id) - Get detailed post metrics
  • get_instagram_post_likes(post, count) - Analyze likers
  • get_instagram_post_comments(post, count) - Get comments

LinkedIn

  • get_linkedin_user_posts(urn, count) - Get post history
  • get_linkedin_company_posts(urn, count) - Company page posts

Twitter/X

  • get_twitter_user_posts(user, count) - Get tweets
  • search_twitter_posts(query, count) - Find trending tweets

YouTube

  • get_youtube_channel_videos(channel, count) - All videos
  • get_youtube_video(video) - Video details and metrics
  • get_youtube_video_comments(video, count) - Comments

Reddit

  • reddit_user_posts(username, count) - User's posts
  • search_reddit_posts(query, count) - Find popular posts

Key Metrics

Engagement Rate:

  • Formula: (Likes + Comments + Shares) / Followers × 100
  • Instagram benchmark: 3-6%
  • LinkedIn benchmark: 2-5% of connections
  • Twitter benchmark: 0.5-1%

Content Performance Score:

Score = (Engagement Rate × 40) +
        (Comments/Likes Ratio × 30) +
        (Share Rate × 30)

Viral Potential Indicators:

  • Engagement rate >2x average
  • High share rate (>5% of engagement)
  • Rapid engagement velocity (50% within 24h)
  • Quality comments (questions, discussions)

Output Formats

Chat Summary:

  • Top 5 performing posts
  • Key insights and patterns
  • Recommendations for optimization

CSV Export:

  • Post URL, date, type
  • Likes, comments, shares
  • Engagement rate
  • Performance rank

JSON Export:

  • Full post data with metadata
  • Time-series engagement data
  • Historical trends

Reference Documentation

  • METRICS_GUIDE.md - Detailed metrics definitions, calculation formulas, and benchmarks

Ready to analyze content? Ask Claude to help you track performance, identify top content, or optimize your posting strategy!

Weekly Installs
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GitHub Stars
14
First Seen
Feb 16, 2026
Installed on
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