anysite-trend-analysis

What this skill does

# anysite Trend Analysis

Discover emerging trends and track viral content across social platforms using anysite MCP. Identify what's gaining momentum before it peaks.

## Overview

- **Detect emerging trends** across multiple platforms
- **Track viral content** and identify breakout topics
- **Monitor hashtag performance** and trending keywords
- **Analyze topic momentum** and growth patterns
- **Identify market shifts** through social listening

**Coverage**: 75% - Good for Twitter, Reddit, YouTube, LinkedIn, Instagram

## Supported Platforms

- **Twitter/X**: Trending topics, viral tweets, hashtag tracking
- **Reddit**: Trending posts, subreddit activity, upvote velocity
- **YouTube**: Trending videos, search trends, rising channels
- **LinkedIn**: Professional trends, industry discussions
- **Instagram**: Trending hashtags, viral content

## v2 Tool Interface

All data fetching uses the universal `execute()` meta-tool. Always call `discover(source, category)` first if you need to verify endpoint names or available parameters.

**Core tools**:
- `execute(source, category, endpoint, params)` - Fetch data. Returns first page + `cache_key`.
- `get_page(cache_key, offset, limit)` - Load more results from a previous execute.
- `query_cache(cache_key, conditions, sort_by, aggregate, group_by)` - Filter, sort, or aggregate cached data without new API calls.
- `export_data(cache_key, format)` - Export full dataset as CSV, JSON, or JSONL.

**Error handling**: If execute() returns an error with `llm_hint`, follow the hint to fix the request (e.g., correcting a parameter name or adjusting the query).

## Quick Start

**Step 1: Search for Trending Content**

By platform:
- Twitter: `execute("twitter", "search", "search_tweets", {"query": "<topic>", "count": 100})` sorted by engagement
- Reddit: `execute("reddit", "search", "search", {"query": "<topic>"})` sorted by upvotes
- YouTube: `execute("youtube", "search", "search_videos", {"query": "<topic>", "count": 50})` by recent
- LinkedIn: `execute("linkedin", "post", "search_posts", {"keywords": "<topic>"})` by engagement
- Instagram: `execute("instagram", "search", "search_users", {"query": "<topic>"})` for hashtag/topic discovery

**Step 2: Analyze Momentum**

Use `query_cache()` to filter and sort cached results:
```
query_cache(cache_key, sort_by="engagement_desc", conditions=[{"field": "date", "op": ">", "value": "2024-01-01"}])
```

Check indicators:
- Engagement velocity (growth rate)
- Cross-platform presence
- Comment volume and sentiment
- Share/retweet patterns

**Step 3: Track Over Time**

Monitor changes:
- Daily engagement growth
- New platform adoption
- Mainstream vs. niche spread
- Peak timing prediction

**Step 4: Report Insights**

Use `export_data(cache_key, "csv")` to generate downloadable reports.

Deliver:
- Trending topics list
- Momentum indicators
- Strategic recommendations
- Early warnings or opportunities

## Common Workflows

### Workflow 1: Multi-Platform Trend Detection

**Scenario**: Identify what's trending in tech/AI space

**Steps**:

1. **Search Across Platforms**
```
# Twitter
execute("twitter", "search", "search_tweets", {"query": "AI OR artificial intelligence", "count": 100})
→ Filter for: Posted within 24-48h, high engagement
→ Save cache_key as twitter_cache

# Reddit
execute("reddit", "search", "search", {"query": "artificial intelligence"})
→ Filter: r/technology, r/MachineLearning, r/singularity
→ Save cache_key as reddit_cache

# YouTube
execute("youtube", "search", "search_videos", {"query": "AI news", "count": 50})
→ Filter: Published this week, views >10k
→ Save cache_key as youtube_cache

# LinkedIn
execute("linkedin", "post", "search_posts", {"keywords": "artificial intelligence"})
→ Filter: High engagement, recent
→ Save cache_key as linkedin_cache
```

2. **Use query_cache to Filter Results**
```
# Filter Twitter for high-engagement posts
query_cache(twitter_cache, sort_by="engagement_desc", conditions=[{"field": "likes", "op": ">", "value": 100}])

# Filter Reddit for specific subreddits
query_cache(reddit_cache, conditions=[{"field": "subreddit", "op": "contains", "value": "technology"}])

# Aggregate YouTube view counts
query_cache(youtube_cache, aggregate={"field": "views", "op": "avg"})
```

3. **Load More Results if Needed**
```
# If execute() returned next_offset, paginate
get_page(twitter_cache, offset=10, limit=50)
get_page(reddit_cache, offset=10, limit=50)
```

4. **Extract Common Themes**
```
Analyze content for recurring:
- Keywords and phrases
- Company/product mentions
- Events or announcements
- Questions or concerns
```

5. **Calculate Trend Score**
```
For each theme:
- Platform count (how many platforms)
- Total engagement
- Growth velocity
- Sentiment distribution
```

6. **Identify Breakout Trends**
```
Trends with:
- Presence on 3+ platforms
- Engagement growing >50% daily
- Positive or controversial sentiment
- Coverage by influencers/media
```

**Expected Output**:
- Top 5-10 trending themes
- Platform-by-platform breakdown
- Momentum indicators
- Strategic implications

### Workflow 2: Hashtag Performance Tracking

**Scenario**: Monitor hashtag growth and adoption

**Steps**:

1. **Search by Hashtag**
```
# Instagram - discover users/content around the hashtag
execute("instagram", "search", "search_users", {"query": "sustainability"})
→ Save cache_key as ig_cache

# Twitter
execute("twitter", "search", "search_tweets", {"query": "#sustainability", "count": 100})
→ Track tweet volume over time
→ Save cache_key as tw_cache

# LinkedIn
execute("linkedin", "post", "search_posts", {"keywords": "sustainability"})
→ Check professional adoption
→ Save cache_key as li_cache
```

2. **Calculate Velocity with query_cache**
```
# Sort by recency and engagement
query_cache(tw_cache, sort_by="date_desc")
query_cache(ig_cache, sort_by="followers_desc")

Hashtag velocity:
- Posts in last 24h vs. previous 24h
- Engagement rate change
- New accounts using hashtag
- Geographic spread
```

3. **Analyze Content Evolution**
```
Compare early vs. recent posts:
- Topic shifts
- Audience changes
- Influencer involvement
- Commercial adoption
```

4. **Export Results**
```
export_data(tw_cache, "csv")
export_data(ig_cache, "json")
→ Share downloadable reports
```

5. **Predict Peak**
```
Based on growth curve:
- Early stage (accelerating)
- Peak stage (plateauing)
- Decline stage (slowing)
```

**Expected Output**:
- Hashtag performance report
- Growth trajectory
- Peak timing estimate
- Strategic recommendations

### Workflow 3: Reddit Trend Mining

**Scenario**: Find emerging discussions in specific communities

**Steps**:

1. **Search Target Subreddits**
```
execute("reddit", "posts", "get", {"subreddit": "technology"})
→ Get top posts from last week
→ Save cache_key as reddit_tech_cache
```

2. **Analyze Post Momentum**
```
# Sort cached posts by engagement
query_cache(reddit_tech_cache, sort_by="upvotes_desc")

# Aggregate engagement metrics
query_cache(reddit_tech_cache, aggregate={"field": "upvotes", "op": "avg"})

For each high-momentum post:
  execute("reddit", "search", "search", {"query": "<post topic>"})
  → Deeper analysis

Calculate:
- Upvotes per hour
- Comment velocity
- Award count
- Controversial score
```

3. **Extract Discussion Themes**
```
From high-momentum posts:
- What problems are discussed?
- What solutions are proposed?
- What companies/products mentioned?
- What sentiment (positive, negative, concerned)?
```

4. **Track Cross-Pollination**
```
Check if trending Reddit topics appear on:
- Twitter: execute("twitter", "search", "search_tweets", {"query": "<topic>"})
- LinkedIn: execute("linkedin", "post", "search_posts", {"keywords": "<topic>"})
- YouTube: execute("youtube", "search", "search_videos", {"query": "<topic>"})
```

**Expected Output**:
- Top Reddit trends
- Community sentiment
- Mainstream potential
- Early mover opportunities

## MCP Tools Reference (v2)

### Twitter/X
- `execute("twitter", "search", "search_tweets",