anysite-person-analyzer

What this skill does

# Person Intelligence Analyzer

Comprehensive multi-platform intelligence analysis combining LinkedIn, Twitter/X, Reddit, GitHub, and web presence data to create actionable intelligence reports with cross-platform personality insights.

## v2 Tool Interface

All data fetching uses the unified v2 MCP 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 download URL.

### v2 Error Handling

All `execute()` calls may return structured errors with `llm_hint` fields. When an error occurs:
- **412 errors**: Resource not found (e.g., user alias incorrect). Follow the `llm_hint` to resolve (typically: search first, then use the returned alias/URN).
- **422 errors**: Wrong parameter format (e.g., passed alias instead of URN). Check `llm_hint` for the correct format.
- **Rate limits**: Continue with data from other sources. Note limitations in report.

## Analysis Workflow

Execute phases sequentially, adapting depth based on available data and user requirements.

### Phase 1: Initial Data Collection

**Starting with LinkedIn Profile URL:**
1. Use `execute("linkedin", "user", "user", {"user": "<profile_url_or_alias>", "with_experience": true, "with_education": true, "with_skills": true})` with full parameters
2. Extract and save the **full URN** (format: `urn:li:fsd_profile:ACoAAABCDEF`) from the response - this is critical for all subsequent API calls
3. Also extract: company URN, current role, location, connections count
4. Record profile completeness for confidence scoring
5. Save the `cache_key` from the response for later use with `query_cache()` or `export_data()`

**IMPORTANT - URN Format:**
Always use the complete URN format `urn:li:fsd_profile:ACoAAABCDEF` from the profile response for all subsequent calls to `execute("linkedin", "user", "user_posts", ...)`, `execute("linkedin", "user", "user_comments", ...)`, and `execute("linkedin", "user", "user_reactions", ...)`. Do not use shortened versions or profile URLs.

**Starting with Name + Context:**
1. Use `execute("linkedin", "search", "search_users", {"query": "<name>", "title": "<title>", "company": "<company>", "location": "<location>"})` with all available filters
2. If multiple matches: present top 3-5 candidates with distinguishing details
3. After user confirmation, proceed with confirmed profile

**Critical Data Points to Capture:**
- Current company and role (with start date)
- Previous roles (last 2-3 positions)
- Education background
- Skills and endorsements
- Connection count (indicator of network size)
- Profile headline and summary

### Phase 2: Activity & Engagement Analysis

**Content Analysis (Posts):**
1. Use `execute("linkedin", "user", "user_posts", {"urn": "<full_fsd_profile_URN>", "count": 20, "posted_after": <unix_timestamp>})` with the full URN (format: `urn:li:fsd_profile:ACoAAABCDEF`)
   - Count: 20-50 depending on activity level
   - posted_after: Unix timestamp for last 90 days for active users, 180 days if low activity
2. If response includes `next_offset`, use `get_page(cache_key, offset, limit)` to load additional posts
3. Analyze for:
   - Topics and themes (use clustering: technical, leadership, industry trends, personal)
   - Engagement metrics (likes, comments per post - calculate averages)
   - Posting frequency (calculate posts per week/month)
   - Content style (thought leadership, sharing, personal stories, company updates)
   - Language and tone
4. Use `query_cache(cache_key, sort_by={"field": "reactions", "order": "desc"})` to find their most engaging posts

**Engagement Analysis (Comments & Reactions):**
1. Use `execute("linkedin", "user", "user_comments", {"urn": "<full_fsd_profile_URN>", "count": 30})` with the full URN (format: `urn:li:fsd_profile:ACoAAABCDEF`)
2. Use `execute("linkedin", "user", "user_reactions", {"urn": "<full_fsd_profile_URN>", "count": 50})` with the full URN (format: `urn:li:fsd_profile:ACoAAABCDEF`)
3. Analyze for:
   - Who they engage with (seniority levels, industries)
   - Topics that spark their engagement
   - Engagement style (supportive, challenging, informational)
   - Response patterns (quick reactions vs thoughtful comments)

**CRITICAL:** All three tools (`execute("linkedin", "user", "user_posts", ...)`, `execute("linkedin", "user", "user_comments", ...)`, `execute("linkedin", "user", "user_reactions", ...)`) require the complete URN in the format `urn:li:fsd_profile:ACoAAABCDEF` obtained from Phase 1. Using LinkedIn profile URLs or partial URNs will result in 422 errors (check `llm_hint` in error response for guidance).

**Output: Engagement Profile**
- Primary content themes (ranked by frequency)
- Engagement level: High/Medium/Low (posts per month, reactions per week)
- Influence indicators: follower count, average post engagement rate
- Communication style: formal/casual, technical/general, etc.

### Phase 3: Company Intelligence

**Current Company Deep Dive:**
1. Use `execute("linkedin", "company", "company", {"company": "<company_alias_or_url>"})` with company alias/URL from profile
2. Extract:
   - Company size, industry, specialties
   - Growth indicators (employee count trends if available)
   - Company description and mission
   - Recent updates/news
   - Save `cache_key` for later filtering with `query_cache()`

3. Use `execute("linkedin", "company", "company_posts", {"urn": "<company_URN_with_company_prefix>", "count": 20})` (count: 20)
   - Note: Company sub-endpoints require `company:{id}` prefix, NOT `fsd_company`. Convert: `urn:li:fsd_company:1441` -> use `company:1441`
   - Analyze company communication themes
   - Identify strategic priorities
   - Note any mentions of funding, hiring, expansion

4. Use `execute("duckduckgo", "search", "search", {"query": "<search_terms>"})` for recent news:
   - "[Company name] funding news"
   - "[Company name] expansion launch product"
   - Prioritize results from last 6 months

**Company Social Media Presence:**

5. **Company Twitter/X Analysis:**
   - Use `execute("twitter", "search", "search_users", {"query": "[Company Name] official", "count": 5})` to find official company account
   - If found, use `execute("twitter", "user", "user", {"user": "<username>"})` for profile stats
   - Use `execute("twitter", "user", "user_posts", {"user": "<username>", "count": 20})` (count: 20-30) to analyze:
     - Product announcements and launches
     - Company culture and values
     - Engagement with customers and community
     - Hiring announcements (growth signals)
     - Technical content (if tech company)
   - Use `execute("twitter", "search", "search_posts", {"query": "[Company Name]", "count": 20})` for company mentions:
     - Customer sentiment (complaints vs praise)
     - Industry discussion about the company
     - Competitor comparisons
     - Notable tweets from employees
   - Use `query_cache(cache_key, sort_by={"field": "favorite_count", "order": "desc"})` to surface most-engaged tweets

6. **Company Reddit Presence:**
   - Use `execute("reddit", "search", "search_posts", {"query": "[Company Name]", "count": 20})` for company mentions
   - Look for:
     - r/startups discussions about the company
     - Industry-specific subreddit mentions (r/SaaS, r/artificial, etc.)
     - Customer experiences and reviews
     - Technical discussions about their product/platform
     - Hiring experiences (Glassdoor-like insights)
     - Founder/team AMAs or discussions
   - Use `query_cache(cache_key, aggregate={"field": "subreddit", "function": "count"}, group_by="subreddit")` to see which subreddits discuss the company most
   - Sentiment analysis: positive/negative/neutra