# Anysite Content Analytics > Track and analyze content performance across Instagram, YouTube, LinkedIn, Twitter/X, and Reddit using anysite MCP server. Measure engagement metrics, analyze post effectiveness, benchmark content strategy, identify top-performing content, and optimize posting strategies. Use when users need to measure content ROI, optimize social strategy, identify viral content patterns, or analyze content engagement across platforms. - Skill: `anysiteio/anysite-content-analytics` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add anysiteio/anysite-content-analytics` - Raw SKILL.md: https://api.skillmd.com/api/skills/anysiteio/anysite-content-analytics/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Data & Analytics - Author: anysiteio (https://skillmd.com/u/anysiteio) - Updated: 2026-09-10 - Page: https://skillmd.com/skills/anysiteio/anysite-content-analytics --- # 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 ## v2 Tool Interface All data fetching uses the anysite MCP v2 universal meta-tools: - **`execute(source, category, endpoint, params)`** - Fetch data from any source. Returns first page + `cache_key`. - **`get_page(cache_key, offset, limit)`** - Load more items from a previous execute() when `next_offset` is returned. - **`query_cache(cache_key, conditions?, sort_by?, aggregate?, group_by?)`** - Filter, sort, and aggregate cached data without new API calls. - **`export_data(cache_key, format)`** - Export full dataset as CSV, JSON, or JSONL. Returns a download URL. ### Error Handling v2 responses may include `llm_hint` fields with guidance on how to resolve errors. Common patterns: - **412**: Entity not found - verify the identifier (username, URN, URL). - **422**: Invalid parameter format - check URN prefix format or param types. - Always check `llm_hint` in error responses for specific resolution steps. ## Quick Start **Step 1: Collect Content Data** Platform-specific: - Instagram: `execute("instagram", "user", "user_posts", {"user": "username", "count": 50})` - LinkedIn: `execute("linkedin", "user", "user_posts", {"urn": "fsd_profile:ACoAAA...", "count": 50})` - Twitter: `execute("twitter", "user", "user_posts", {"user": "username", "count": 100})` - YouTube: `execute("youtube", "channel", "channel_videos", {"channel": "channel_id", "count": 30})` **Step 2: Analyze Engagement** Use `query_cache()` on the returned `cache_key` to analyze without re-fetching: ``` query_cache(cache_key, sort_by="likes desc", aggregate="avg:likes,comments") ``` 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 **Step 5: Export Results** ``` export_data(cache_key, "csv") ``` Returns a download URL for the full dataset. ## Common Workflows ### Workflow 1: Instagram Content Audit **Steps**: 1. **Get All Posts** ``` execute("instagram", "user", "user_posts", {"user": "username", "count": 100}) → returns cache_key + first page of results ``` If more posts exist (response includes `next_offset`): ``` get_page(cache_key, offset=next_offset, limit=50) ``` 2. **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) ``` Use `query_cache` to sort and filter: ``` query_cache(cache_key, sort_by="likes desc", aggregate="avg:likes,comments") ``` 3. **Identify Top Performers** ``` query_cache(cache_key, sort_by="likes desc") Top 10%: Analyze for common patterns - Topics/themes - Visual style - Caption style and length - Hashtag strategy ``` 4. **Analyze Content Mix** ``` query_cache(cache_key, group_by="type", aggregate="count:id,avg:likes,avg:comments") Results show: - Reels: X% of posts, Y% of engagement - Carousels: X% of posts, Y% of engagement - Single images: X% of posts, Y% of engagement ``` 5. **Benchmark Against Competitors** ``` For each competitor: execute("instagram", "user", "user_posts", {"user": "competitor", "count": 50}) Compare: - Posting frequency - Engagement rates - Content types - Top themes ``` 6. **Export Results** ``` export_data(cache_key, "csv") ``` **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** ``` execute("linkedin", "user", "user_posts", {"urn": "fsd_profile:ACoAAA...", "count": 100}) → returns cache_key + first page ``` For company page posts: ``` execute("linkedin", "company", "company_posts", {"urn": {"type": "company", "value": "1441"}, "count": 100}) ``` Use `get_page(cache_key, offset, limit)` if more posts exist. 2. **Categorize Content** ``` Group by type: - Text-only posts - Image posts - Video posts - Article shares - LinkedIn articles - Polls ``` 3. **Analyze Engagement by Type** ``` query_cache(cache_key, aggregate="avg:comment_count,avg:share_count", group_by="type") For each content type: - Average reactions - Average comments - Average shares - Engagement rate ``` 4. **Topic Analysis** ``` Extract themes from top posts: - Industry insights - Personal stories - How-to/educational - Company news - Thought leadership ``` 5. **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** ``` execute("youtube", "channel", "channel_videos", {"channel": "channel_id", "count": 50}) → returns cache_key + first page ``` Use `get_page(cache_key, offset, limit)` for additional videos. 2. **Analyze Each Video** ``` For each video: execute("youtube", "video", "video", {"video": "video_id"}) Metrics: - Views - Likes/dislikes - Comments - View velocity (views per day since upload) ``` 3. **Identify Patterns** ``` query_cache(cache_key, sort_by="views desc") Analyze top 20% by views: - Video length - Titles (keywords, style) - Thumbnail patterns - Topics/themes - Upload timing ``` 4. **Engagement Analysis** ``` Check comments: execute("youtube", "video", "video_comments", {"video": "video_id", "count": 100}) Analyze: - Comment quality - Questions asked - Sentiment - Engagement timing ``` 5. **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 (v2) ### Instagram - `execute("instagram", "user", "user_posts", {"user": username, "count": N})` - Get posts with engagement - `execute("instagram", "post", "post", {"post": post_id})` - Get detailed post metrics - `execute("instagram", "post", "post_likes", {"post": post_id, "count": N})` - Analyze likers - `execute("instagram", "post", "post_comments", {"post": post_id, "count": N})` - Get comments ### LinkedIn - `execute("linkedin", "user", "user_posts", {"urn": "fsd_profile:ACoAAA...", "count": N})` - Get user post history - `execute("linkedin", "company", "company_posts", {"urn": {"type": "company", "value": "ID"}, "count": N})` - Company page posts ### Twitter/X - `execute("twitter", "user", "user_posts", {"user": username, "count": N})` - Get tweets - `execute("twitter", "search", "search_posts", {"query": query, "count": N})` - Find trending tweets ### YouTube - `execute("youtube", "channel", "channel_videos", {"channel": channel, "count": N})` - All videos - `execute("youtube", "video", "video", {"video": video_id})` - Video details and metrics - `execute("youtube", "video", "video_comments", {"video": video_id, "count": N})` - Comments ### Reddit - `execute("reddit", "user", "user_posts", {"username": username, "count": N})` - User's posts - `execute("reddit", "search", "search_posts", {"query": query, "count": N})` - Find popular posts ### Pagination & Analysis - `get_page(cache_key, offset, limit)` - Fetch next page of results from any execute() call - `query_cache(cache_key, conditions?, sort_by?, aggregate?, group_by?)` - Filter/sort/aggregate cached results - `export_data(cache_key, "csv"|"json"|"jsonl")` - Export dataset as downloadable file ## Key Metrics **Engagement Rate**: - Formula: (Likes + Comments + Shares) / Followers x 100 - Instagram benchmark: 3-6% - LinkedIn benchmark: 2-5% of connections - Twitter benchmark: 0.5-1% **Content Performance Score**: ``` Score = (Engagement Rate x 40) + (Comments/Likes Ratio x 30) + (Share Rate x 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** (via `export_data(cache_key, "csv")`): - Post URL, date, type - Likes, comments, shares - Engagement rate - Performance rank **JSON Export** (via `export_data(cache_key, "json")`): - Full post data with metadata - Time-series engagement data - Historical trends ## Reference Documentation - **[METRICS_GUIDE.md](references/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!