anysite-influencer-discovery

What this skill does

# anysite Influencer Discovery

Find and analyze influencers across social platforms using anysite MCP. Discover content creators, evaluate their reach and engagement, and identify partnership opportunities.

## Overview

- **Discover influencers** across Instagram, Twitter, LinkedIn, YouTube
- **Analyze engagement** and audience quality
- **Track activity** and content patterns
- **Evaluate partnership fit** based on niche and metrics
- **Build influencer lists** with contact information

**Coverage**: 85% - Excellent for Instagram, Twitter, LinkedIn, YouTube influencers.

## v2 Tool Interface

All data fetching uses the anysite v2 meta-tools:

- **execute(source, category, endpoint, params)** - Fetch data. 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, or 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**: Check responses for `llm_hint` fields that provide actionable guidance on failures (e.g., alias not found, URN required).

## Supported Platforms

- ✅ **Instagram**: Profile stats, posts, followers, engagement, Reels
- ✅ **Twitter/X**: User search, followers, tweets, engagement
- ✅ **LinkedIn**: B2B influencers, thought leaders, professional content
- ✅ **YouTube**: Channel search, subscribers, views, video performance
- ✅ **Reddit**: Community influencers, karma, post quality

## Quick Start

**Step 1: Search for Influencers**

By platform:
- Instagram: `execute("instagram", "search", "search_posts", {"query": "niche keywords", "count": 50})` with niche keywords + hashtags
- Twitter: `execute("twitter", "search", "search_users", {"query": "niche keywords", "count": 50})` with niche keywords
- LinkedIn: `execute("linkedin", "search", "search_users", {"keywords": "industry thought leader", "count": 50})` with industry + "thought leader"
- YouTube: `execute("youtube", "search", "search_videos", {"query": "niche", "count": 50})` with niche, then analyze channels

**Step 2: Analyze Profiles**

Get detailed metrics:
- Instagram: `execute("instagram", "user", "user", {"user": "username"})` -> followers, posts, engagement rate
- Twitter: `execute("twitter", "user", "get", {"username": "handle"})` -> followers, tweet frequency
- YouTube: `execute("youtube", "channel", "channel_videos", {"channel": "...", "count": 30})` -> subscribers, views, growth
- LinkedIn: `execute("linkedin", "user", "user", {"user": "alias"})` -> connections, post engagement

**Step 3: Evaluate Engagement**

Check engagement quality:
- Post likes, comments, shares
- Engagement rate (engagement / followers)
- Audience authenticity (comment quality)
- Content consistency (posts per week)

Use `query_cache(cache_key, sort_by=[{"field": "like_count", "order": "desc"}])` to rank posts by engagement without re-fetching.

**Step 4: Build Influencer List**

Export with `export_data(cache_key, "csv")`:
- Name, handle, platform
- Follower count, engagement rate
- Niche/topics, content type
- Contact info (if available)
- Partnership fit score

## Common Workflows

### Workflow 1: Instagram Influencer Discovery

**Scenario**: Find Instagram influencers in sustainable fashion (10k-100k followers)

**Steps**:

1. **Search by Hashtag/Keywords**
```
execute("instagram", "search", "search_posts", {
  "query": "sustainable fashion OR eco friendly fashion",
  "count": 100
})
-> Extract unique user handles from results
-> Use get_page(cache_key, offset, 50) if next_offset returned for more results
```

2. **Analyze Each Creator**
```
For each unique handle:
  execute("instagram", "user", "user", {"user": "username"})
  -> Follower count, bio, profile type

Filter for:
- 10k-100k followers
- Business/Creator account
- Bio mentioning sustainability
```

3. **Evaluate Content**
```
For qualified creators:
  execute("instagram", "user", "user_posts", {"user": "username", "count": 30})

Analyze:
- Post frequency (consistency)
- Engagement rate per post
- Content quality and style
- Brand partnerships visible

Use query_cache(cache_key, sort_by=[{"field": "like_count", "order": "desc"}]) to find top posts
Use query_cache(cache_key, aggregate=[{"field": "like_count", "function": "avg"}]) for average engagement
```

4. **Check Audience Quality**
```
execute("instagram", "post", "post_likes", {"post": "post_id", "count": 100})
execute("instagram", "post", "post_comments", {"post": "post_id", "count": 50})

Look for:
- Real comments (not just emojis)
- Engaged community (questions, discussions)
- Geographic relevance
```

5. **Get Contact Information**
```
From Instagram bio:
- Email addresses
- Website links

If LinkedIn mentioned:
  execute("linkedin", "search", "search_users", {"keywords": "first_name last_name"})
  execute("linkedin", "user", "user", {"user": "alias_from_search"})
```

**Expected Output**:
- 20-40 qualified influencers
- Engagement metrics for each
- Contact information for 60-70%
- Partnership fit scores

Use `export_data(cache_key, "csv")` to generate a downloadable influencer list.

### Workflow 2: LinkedIn Thought Leader Identification

**Scenario**: Find B2B thought leaders in SaaS/sales

**Steps**:

1. **Search for Active Posters**
```
execute("linkedin", "search", "search_users", {
  "keywords": "SaaS sales thought leader",
  "title": "VP Sales OR Head of Sales OR Chief Revenue Officer",
  "count": 100
})
```

2. **Analyze Post Activity**
```
For each candidate:
  execute("linkedin", "post", "get_user_posts", {"user": "urn", "count": 50})

Filter for:
- Posts 2-3x per week minimum
- High engagement (100+ reactions)
- Original content (not just shares)

Use query_cache(cache_key, conditions=[{"field": "comment_count", "operator": ">", "value": 10}])
to filter for high-engagement posts
```

3. **Evaluate Influence**
```
Check post engagement:
- Average reactions per post
- Comment quality and quantity
- Share count
- Follower growth signals

Use query_cache(cache_key, aggregate=[
  {"field": "comment_count", "function": "avg"},
  {"field": "share_count", "function": "avg"}
]) for average metrics
```

4. **Assess Content Quality**
```
Review posts for:
- Expertise demonstration
- Original insights
- Engagement with comments
- Consistency of messaging
```

**Expected Output**:
- 15-25 active thought leaders
- Content themes and topics
- Engagement metrics
- Partnership opportunities (guest posts, quotes, etc.)

Use `export_data(cache_key, "csv")` to export the thought leader list.

### Workflow 3: YouTube Creator Research

**Scenario**: Find YouTube creators in tech reviews

**Steps**:

1. **Search for Niche Content**
```
execute("youtube", "search", "search_videos", {
  "query": "tech review 2026",
  "count": 100
})
-> Extract unique channel names
-> Use get_page(cache_key, offset, 50) if more results needed
```

2. **Analyze Channels**
```
For each channel:
  execute("youtube", "channel", "channel_videos", {"channel": "channel_id", "count": 30})

Check:
- Subscriber count
- Upload frequency
- Average views per video
- Video length (long-form vs shorts)

Use query_cache(cache_key, aggregate=[{"field": "view_count", "function": "avg"}]) for average views
```

3. **Evaluate Video Performance**
```
For top videos:
  execute("youtube", "video", "video", {"video": "video_id"})

Metrics:
- View count
- Like/dislike ratio
- Comments count
- Watch time signals (retention)
```

4. **Analyze Audience Engagement**
```
execute("youtube", "video", "video_comments", {"video": "video_id", "count": 100})

Look for:
- Active community
- Technical discussions
- Purchase decisions influenced
```

**Expected Output**:
- 10-20 relevant channels
- Subscriber and view metrics
- Engagement analysis
- Partnership fit assessment

Use `export_data(cache_key, "csv")` to export channel data.

## MCP Tools