# Anysite Influencer Discovery

> Discover and analyze influencers across Instagram, Twitter/X, LinkedIn, YouTube, and Reddit using anysite MCP server. Find content creators by niche, analyze engagement metrics, evaluate audience quality, track influencer activity, and identify partnership opportunities. Supports multi-platform influencer search, profile enrichment, follower analysis, and engagement tracking. Use when users need…

- **Type:** Skill
- **Install:** `agentstack add skill-anysiteio-agent-skills-anysite-influencer-discovery`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [anysiteio](https://agentstack.voostack.com/s/anysiteio)
- **Installs:** 0
- **Category:** [Data & Analytics](https://agentstack.voostack.com/c/data-and-analytics)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [anysiteio](https://github.com/anysiteio)
- **Source:** https://github.com/anysiteio/agent-skills/tree/main/skills/anysite-influencer-discovery

## Install

```sh
agentstack add skill-anysiteio-agent-skills-anysite-influencer-discovery
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# 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 Reference (v2)

### Instagram
- `execute("instagram", "search", "search_posts", {"query": ..., "count": N})` - Find posts by keywords/hashtags
- `execute("instagram", "user", "user", {"user": ...})` - Get profile with followers, bio
- `execute("instagram", "user", "user_posts", {"user": ..., "count": N})` - Get recent posts with engagement
- `execute("instagram", "post", "post_likes", {"post": ..., "count": N})` - Check audience authenticity
- `execute("instagram", "post", "post_comments", {"post": ..., "count": N})` - Analyze engagement quality
- `execute("instagram", "user", "user_friendships", {"user": ..., "count": N, "type": "followers"})` - Get followers list (for analysis)

### Twitter/X
- `execute("twitter", "search", "search_users", {"query": ..., "count": N})` - Find users by keywords/bio
- `execute("twitter", "user", "get", {"username": ...})` - Get profile with followers, tweets
- `execute("twitter", "user_tweets", "get", {"username": ...})` - Get recent tweets with engagement
- `execute("twitter", "search", "search_posts", {"query": ..., "count": N})` - Find influential tweets in niche

### LinkedIn
- `execute("linkedin", "search", "search_users", {"keywords": ..., "count": N})` - Find professionals by keywords/title
- `execute("linkedin", "user", "user", {"user": ...})` - Get complete profile (includes skills with `with_skills: true`)
- `execute("linkedin", "post", "get_user_posts", {"user": "urn", "count": N})` - Get post history and engagement
- `execute("linkedin", "user", "user_skills", {"urn": ..., "count": N})` - Verify expertise (requires URN from profile)

Note: LinkedIn connection count is returned in the profile response (`connection_count` field). No separate endpoint needed.

### YouTube
- `execute("youtube", "search", "search_videos", {"query": ..., "count": N})` - Find videos by keywords
- `execute("youtube", "channel", "channel_videos", {"channel": ..., "count": N})` - Get all videos from channel
- `execute("youtube", "video", "video", {"video": ...})` - Get video metrics (views, likes)
- `execute("youtube", "video", "video_comments", {"video": ..., "count": N})` - Analyze audience engagement

### Reddit
- `execute("reddit", "search", "search_posts", {"query": ..., "count": N})` - Find influential posts in subreddits
- `execute("reddit", "user", "user_posts", {"username": ..., "count": N})` - Get user's post history
- `execute("reddit", "user", "user_comments", {"username": ..., "count": N})` - Analyze community engagement

### Web Scraping
- `execute("webparser", "parse", "parse", {"url": ...})` - Scrape any webpage for contact info, media kits, etc.

### Pagination, Caching & Export
- `get_page(cache_key, offset, limit)` - Fetch additional pages from any execute() result
- `query_cache(cache_key, conditions, sort_by, aggregate, group_by)` - Filter/sort/aggregate cached data
- `export_data(cache_key, "csv"|"json"|"jsonl")` - Export full dataset as downloadable file

## Output Formats

**Chat Summary**:
- Top 10 influencers with key metrics
- Engagement rate comparison
- Partnership recommendations
- Contact information found

**CSV Export** (via `export_data(cache_key, "csv")`):
- Influencer name, handle, platform
- Followers, engagement rate
- Niche, content type
- Email, website
- Fit score (1-100)

**JSON Export** (via `export_data(cache_key, "json")`):
- Complete profile data
- All posts with engagement
- Audience demographics (if available)
- Historical metrics

## Influencer Evaluation Framework

### Reach Metrics
- **Followers**: Total audience size
- **Views**: Average content views
- **Growth**: Follower growth rate

### Engagement Metrics
- **Rate**: Engagement / Followers
- **Quality**: Comment depth and relevance
- **Consistency**: Regular engagement patterns

### Authenticity Indicators
- **Audience Quality**: Real vs. fake followers
- **Comment Quality**: Meaningful discussions
- **Growth Pattern**: Organic vs. purchased
- **Engagement Distribution**: Consistent vs. spiky

### Content Quality
- **Production Value**: Visual/audio quality
- **Originality**: Unique vs. repurposed
- **Consistency**: Regular posting schedule
- **Niche Alignment**: On-brand content

### Partnership Fit
- **Audience Overlap**: Match with target market
- **Brand Alignment**: Values and messaging
- **Professionalism**: Past partnerships, disclosure
- **Availability**: Contact information, responsiveness

## Advanced Features

### Micro-Influencer Strategy

Focus on 10k-50k followers for higher engagement:
```
Benefits:
- Higher engagement rates (5-10% vs. 1-3%)
- More authentic audience connections
- Lower partnership costs
- Niche expertise

Discovery approach:
- Use hashtag searches via execute("instagram", "search", "search_posts", ...)
- Use query_cache() to filter by engagement rate vs. reach
- Prioritize niche relevance over size
```

### Multi-Platform Presence Analysis

Identify influencers active across platforms:
```
1. Find on Instagram/Twitter
2. Search LinkedIn for professional presence:
   execute("linkedin", "search", "search_users", {"keywords": "name"})
3. Check for YouTube channel:
   execute("youtube", "search", "search_videos", {"query": "creator name", "count": 10})
4. Look for website/blog:
   execute("webparser", "parse", "parse", {"url": "website_url"})

Benefits:
- Multiple touchpoints
- Diverse content formats
- Professional credibility
- Larger total reach
```

### Audience Demographics Research

Analyze who follows the influencer:
```
Instagram:
- execute("instagram", "user", "user_friendships", {"user": "username", "count": 100, "type": "followers"})
- Analyze follower profiles for patterns
- Use query_cache(cache_key, group_by="location") to segment by geography

LinkedIn:
- Check who engages with posts
- Identify follower job titles/industries from post comments

YouTube:
- Analyze comment demographics via execute("youtube", "video", "video_comments", ...)
- Check subscriber locations (if available)
```

## Reference Documentation

- **[DISCOVERY_CRITERIA.md](references/DISCOVERY_CRITERIA.md)** - Influencer evaluation criteria, scoring frameworks, and niche identification strategies

## Troubleshooting

**No Influencers Found**:
- Broaden search keywords
- Try multiple hashtags
- Search across multiple platforms
- Reduce minimum follower requirements

**Low Engagement Rates**:
- Use `query_cache(cache_key, conditions=[{"field": "engagement_rate", "operator": ">", "value": 0.03}])` to filter
- Focus on micro-influencers (smaller = higher engagement)
- Check for bot followers (sudden spikes)

**No Contact Information**:
- Check bio for email/website
- Look for LinkedIn profile via `execute("linkedin", "search", "search_users", {"keywords": "name"})`
- Try website domain: `execute("webparser", "parse", "parse", {"url": "domain"})`
- Search for media kit or press page

**API Errors**:
- Check `llm_hint` in error responses for actionable guidance
- LinkedIn endpoints requiring URN: get URN from profile response first, do not guess aliases
- Use `execute("linkedin", "search", "search_users", ...)` to find correct aliases before fetching profiles

---

**Ready to discover influencers?** Ask Claude to help you find content creators, analyze engagement, or build influencer lists for your marketing campaigns!

## 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.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-anysiteio-agent-skills-anysite-influencer-discovery
- Seller: https://agentstack.voostack.com/s/anysiteio
- Browse the marketplace: https://agentstack.voostack.com/browse

---
Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.