anysite-person-analyzer

# 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.

## 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 `get_linkedin_profile` with full parameters (education, experience, skills)
2. Extract and save the **full URN** (format: `urn:li:fsd_profile:ACoAAABCDEF`) - 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

**IMPORTANT - URN Format:** 
Always use the complete URN format `urn:li:fsd_profile:ACoAAABCDEF` from the profile response for all subsequent calls to `get_linkedin_user_posts`, `get_linkedin_user_comments`, and `get_linkedin_user_reactions`. Do not use shortened versions or profile URLs.

**Starting with Name + Context:**
1. Use `search_linkedin_users` with all available filters:
   - Name, title, company keywords, location, school
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 `get_linkedin_user_posts` with the full URN (format: `urn:li:fsd_profile:ACoAAABCDEF`)
   - Count: 20-50 depending on activity level
   - Posted after filter: last 90 days for active users, 180 days if low activity
2. Analyze for:
   - Topics and themes (use clustering: technical, leadership, industry trends, personal)
   - E