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)

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)

Use query_cache to sort and filter:

query_cache(cache_key, sort_by="likes desc", aggregate="avg:likes,comments")

1. 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

1. 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

1. Benchmark Against Competitors

For each competitor:
  execute("instagram", "user", "user_posts", {"user": "competitor", "count": 50})
Compare:
- Posting frequency
- Engagement rates
- Content types
- Top themes

1. 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.

1. Categorize Content

Group by type:
- Text-only posts
- Image posts
- Video posts
- Article shares
- LinkedIn articles
- Polls

1. 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

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

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.

1. 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)

1. 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

1. Engagement Analysis

Check comments:
  execute("youtube", "video", "video_comments", {"video": "video_id", "count": 100})

Analyze:
- Comment quality
- Questions asked
- Sentiment
- Engagement timing

1. Content Mix Optimization

Compare:
- Long-form (>10 min) vs short (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!

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [anysiteio](https://github.com/anysiteio)
- **Source:** [anysiteio/agent-skills](https://github.com/anysiteio/agent-skills)
- **License:** MIT

Install and usage instructions live in the source repository linked above.