Person Intelligence Analyzer Skill

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:

Use execute("linkedin", "user", "user", {"user": "<profile_url_or_alias>", "with_experience": true, "with_education": true, "with_skills": true}) with full parameters
Extract and save the full URN (format: urn:li:fsd_profile:ACoAAABCDEF) from the response - this is critical for all subsequent API calls
Also extract: company URN, current role, location, connections count
Record profile completeness for confidence scoring
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:

Use execute("linkedin", "search", "search_users", {"query": "<name>", "title": "<title>", "company": "<company>", "location": "<location>"}) with all available filters
If multiple matches: present top 3-5 candidates with distinguishing details
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):

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
If response includes next_offset, use get_page(cache_key, offset, limit) to load additional posts
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
Use query_cache(cache_key, sort_by={"field": "reactions", "order": "desc"}) to find their most engaging posts

Engagement Analysis (Comments & Reactions):

Use execute("linkedin", "user", "user_comments", {"urn": "<full_fsd_profile_URN>", "count": 30}) with the full URN (format: urn:li:fsd_profile:ACoAAABCDEF)
Use execute("linkedin", "user", "user_reactions", {"urn": "<full_fsd_profile_URN>", "count": 50}) with the full URN (format: urn:li:fsd_profile:ACoAAABCDEF)
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:

Use execute("linkedin", "company", "company", {"company": "<company_alias_or_url>"}) with company alias/URL from profile
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()
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
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:

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
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/neutral community perception
- Pain points mentioned by users/customers

Company Context Analysis:

Business model and revenue streams
Technology stack (if tech company)
Market position and competitors
Recent achievements or challenges
Cultural indicators from company posts
Social sentiment (Twitter mentions, Reddit discussions)
Community engagement (how company responds on social platforms)
Growth signals (hiring tweets, expansion announcements on Twitter)
Customer pain points (Reddit complaints, Twitter issues)

Phase 4: Multi-Platform Intelligence Enrichment

A. Twitter/X Analysis (if handle found or identifiable):

Find Twitter Handle:
- Check LinkedIn profile bio/description for @username
- Use execute("twitter", "search", "search_users", {"query": "[First Name] [Last Name] [Company]", "count": 5}) with name if not found
- Verify match by checking bio, profile description
Profile Analysis:
- Use execute("twitter", "user", "user", {"user": "<username>"}) with username
- Extract: follower count, following count, tweet count, bio, location
- Note: verification status, profile creation date
Content Analysis:
- Use execute("twitter", "user", "user_posts", {"user": "<username>", "count": 50}) (count: 50-100 recent tweets)
- If response includes next_offset, use get_page(cache_key, offset, limit) to load more tweets up to 100
- Analyze for:
  - Technical expertise signals (code snippets, tech discussions)
  - Industry opinions and hot takes
  - Personal interests and hobbies
  - Engagement with other thought leaders
  - Retweets vs original content ratio
- Calculate: tweets per day, avg engagement rate
- Use query_cache(cache_key, aggregate={"field": "favorite_count", "function": "avg"}) to compute average engagement
Topic Discovery:
- Use execute("twitter", "search", "search_posts", {"query": "[topic] from:@username", "count": 20}) with person's key interests
- Identify recurring themes and expertise areas
- Note controversial or strongly-held opinions

B. Reddit Activity (if username discoverable):

Find Reddit Presence:
- Search for username from other platforms
- Use execute("reddit", "search", "search_posts", {"query": "<name_or_company>", "count": 20}) with name/company mentions
- Look for: "AMA" posts, technical discussions, community contributions
Content Analysis:
- Use execute("reddit", "search", "search_posts", {"query": "author:[username]", "count": 20}) with username if known
- Analyze for:
  - Subreddit preferences (which communities they're active in)
  - Technical depth of contributions
  - Helping behavior vs self-promotion ratio
  - Community reputation indicators
- Use query_cache(cache_key, aggregate={"field": "subreddit", "function": "count"}, group_by="subreddit") to identify most active subreddits
Topic Expertise:
- Use execute("reddit", "search", "search_posts", {"query": "[topic] [username or company]", "count": 20}) for specific topics
- Identify where they're seen as expert/helpful
- Note any popular posts or discussions they started

C. Instagram Presence (optional, if B2C relevant or personal brand focus):

Profile Discovery:
- Check if mentioned in LinkedIn or Twitter
- Use execute("instagram", "search", "search_posts", {"query": "#[name] #[company]", "count": 10}) with hashtags
- Use execute("instagram", "user", "user", {"user": "<handle>"}) if handle known
Content Style:
- Use execute("instagram", "user", "user_posts", {"user": "<handle>", "count": 20}) (count: 20-30)
- If more posts needed, use get_page(cache_key, offset, limit) to continue
- Analyze for: personal brand vs professional content
- Note: visual style, posting frequency, engagement rate

D. Web Intelligence & Media Presence:

Professional Presence:
- execute("duckduckgo", "search", "search", {"query": "[Name] [Company] speaker conference"})
- execute("duckduckgo", "search", "search", {"query": "[Name] interview podcast"})
- execute("duckduckgo", "search", "search", {"query": "[Name] article blog post"})
Expertise & Thought Leadership:
- execute("duckduckgo", "search", "search", {"query": "[Name] expertise [primary topic from posts]"})
- Check for: publications, talks, media mentions
- execute("duckduckgo", "search", "search", {"query": "[Name] [key topic] site:medium.com OR site:dev.to OR site:substack.com"})
Company-Specific Context:
- execute("duckduckgo", "search", "search", {"query": "[Name] [Company] announcement"})
- Look for: press releases, product launches, executive quotes
GitHub/Tech Presence (if technical role):
- execute("duckduckgo", "search", "search", {"query": "[Name] site:github.com"})
- Look for: open source contributions, personal projects

E. Parse Key Pages:

Use execute("webparser", "parse", "parse", {"url": "<page_url>"}) for high-value sources:
- Personal blog/website (if mentioned in any profile)
- Recent interviews or podcast appearances
- Conference speaker profiles
- Company "About Team" pages
- Notable Medium/Substack articles
- Popular Reddit AMAs or discussions
Extract: bio, expertise areas, quotes, interests, unique perspectives

Platform Priority Strategy:

Always analyze: LinkedIn (mandatory) + Web Search
High priority: Twitter/X (if found) - usually most revealing for tech audience
Medium priority: Reddit (if active) - shows technical depth and community engagement
Low priority: Instagram - only if B2C focus or strong personal brand element
Context-dependent: GitHub - critical for engineering roles, less for business roles

Cross-Platform Analysis:

Compare tone across platforms (professional LinkedIn vs casual Twitter)
Identify platform-specific content themes
Note engagement levels per platform
Synthesize consistent interests vs platform-specific behavior

Phase 5: Cross-Platform Strategic Analysis & Report Generation

Data Export (optional):

Use export_data(cache_key, "csv") or export_data(cache_key, "json") to save collected datasets for the user
Useful for: LinkedIn posts dataset, Twitter tweets dataset, Reddit mentions dataset
Returns download URL the user can share or archive

Connection Strategy:

Conversation Topics (ranked by relevance, synthesized across all platforms):
- Top 3-5 topics from their LinkedIn posts/comments
- Hot takes or strong opinions from Twitter/X
- Technical discussions from Reddit
- Industry trends they've engaged with across platforms
- Shared interests or connections (if any)
- Recent company achievements to acknowledge
Engagement Approach:
- Best channels: LinkedIn comment, Twitter reply, Reddit comment, DM, email
- Channel preference: Note where they're most active/responsive
- Timing: based on posting patterns per platform (e.g., "most active on Twitter evenings, LinkedIn Tuesday mornings")
- Ice-breakers: reference specific post/comment/tweet that relates to AnySite
- Platform-specific tone: professional LinkedIn vs casual Twitter vs technical Reddit
Cross-Platform Personality Synthesis:
- Professional persona (LinkedIn) vs Personal persona (Twitter/Reddit)
- Technical depth indicators (Reddit discussions, GitHub activity)
- Communication style differences per platform
- Authentic interests (topics mentioned across multiple platforms)

Value Assessment for AnySite:

Analyze fit across multiple dimensions:

A. Direct Business Value:

Potential customer: Does their company match AnySite ICP?
- B2B SaaS, AI companies, data-intensive businesses
- Size indicators: 10-500 employees, growth stage
- Pain points: mentions of data extraction, API integrations, agent development
Decision maker level: C-suite, VP, Director, Manager
Budget authority indicators

B. Partnership Potential:

Technology synergies (complementary tools/platforms)
Channel partnership opportunities
Integration possibilities
Co-marketing potential

C. Network & Influence:

Network size and quality (10k+ connections = super-connector)
Industry influence (thought leader, frequent speaker)
Investor connections (VC, angels in their network)
Potential for introductions

D. Talent & Advisory:

Expertise match for advisor/mentor role
Potential hire for future scaling
Domain knowledge that fills gaps

Prioritization Matrix:

Tier 1 (Hot Lead): Decision maker + ICP match + high engagement
Tier 2 (Warm Lead): Mid-level + ICP match OR influencer + relevant network
Tier 3 (Long-term Nurture): Potential future value, build relationship
Tier 4 (Low Priority): No clear fit, maintain basic connection

Output Format

Generate comprehensive markdown report with sections:

# Person Intelligence Report: [Name]

**Generated:** [Date]
**Analysis Depth:** [Quick/Standard/Deep]
**Confidence Score:** [0-100%] based on data availability

## Executive Summary
[2-3 sentences: who they are, what they do, why they matter to AnySite]

## Professional Profile
- **Current Role:** [Title] at [Company] (since [date])
- **Location:** [City, Country]
- **Experience:** [X years in industry/role]
- **Education:** [Degree, Institution]
- **Network Size:** [LinkedIn connections count]
- **LinkedIn Profile:** [URL]
- **Twitter/X:** [@handle or "Not found"] ([follower count if found])
- **Reddit:** [u/username or "Not found/searched"]
- **GitHub:** [username or "Not found"] (if technical role)
- **Personal Website:** [URL if found]

## Key Background
[2-3 paragraphs covering:]
- Career trajectory and notable positions
- Expertise and specializations
- Notable achievements or credentials

## Multi-Platform Activity Analysis

### LinkedIn Activity (Last 90 Days)

#### Content Themes
1. **[Theme 1]** (40% of posts)
   - Key topics: [list]
   - Example post: "[quote or summary]"

2. **[Theme 2]** (30% of posts)
   - Key topics: [list]

3. **[Theme 3]** (20% of posts)

#### Engagement Patterns
- **Posting Frequency:** [X posts/month]
- **Engagement Rate:** [Average likes, comments per post]
- **Response Style:** [Description]
- **Active Topics:** [Topics they comment on most]

### Twitter/X Activity (if found)

#### Profile Stats
- **Followers:** [count]
- **Following:** [count]
- **Tweets:** [total count]
- **Account Age:** [created date]

#### Content Analysis (Recent 50-100 tweets)
- **Posting Frequency:** [tweets per day/week]
- **Content Mix:** [% original tweets vs retweets vs replies]
- **Primary Topics:** [list top 3-5 themes]
- **Engagement Level:** [avg likes, retweets per tweet]
- **Notable Takes:** [any strong opinions or viral tweets]
- **Technical Depth:** [code snippets, technical discussions level]

#### Community Engagement
- **Engages with:** [types of accounts: VCs, founders, engineers, etc.]
- **Tone:** [professional/casual/humorous/technical]

### Reddit Activity (if found)

#### Subreddit Preferences
- **Most Active In:** [list top 3-5 subreddits]
- **Karma:** [post/comment karma if visible]

#### Contribution Style
- **Activity Type:** [% asking questions vs answering vs discussions]
- **Technical Depth:** [level of detail in technical responses]
- **Community Reputation:** [helpful, expert, casual participant]
- **Notable Contributions:** [any popular posts or helpful answers]

### Cross-Platform Synthesis

#### Personality Comparison
- **LinkedIn Persona:** [professional characteristics]
- **Twitter Persona:** [casual/personal characteristics]
- **Reddit Persona:** [technical/community characteristics]
- **Consistency:** [topics/interests mentioned across platforms]

#### Platform Preferences
- **Most Active:** [which platform has highest activity]
- **Best Engagement:** [where they get most responses]
- **Content Types:** [professional insights on LinkedIn, hot takes on Twitter, deep tech on Reddit]

#### Communication Style
[Synthesized description: formal/casual, technical depth, storytelling approach, cross-platform consistency or variation]

## Company Intelligence: [Company Name]

### Company Overview
- **Industry:** [Sector]
- **Size:** [Employee count]
- **Stage:** [Startup/Scale-up/Enterprise]
- **Mission:** [Brief description]
- **Twitter:** [@handle or "Not found"] ([follower count if found])
- **Reddit Presence:** [Active/Mentioned/Not found]

### Strategic Context
- **Recent News:** [Key developments from last 6 months]
- **Growth Indicators:** [Hiring, funding, expansion signals]
- **Market Position:** [Brief competitive context]
- **Technology Focus:** [If relevant]

### Company LinkedIn Content Analysis
[Themes from company LinkedIn posts, strategic priorities]

### Company Social Media Presence

#### Twitter/X Activity (if found)
- **Account Stats:** [Followers, following, tweets]
- **Content Mix:** [Product announcements, culture, technical content, engagement]
- **Recent Highlights:** [Key tweets from last 30 days]
- **Posting Frequency:** [tweets per week]
- **Engagement Level:** [avg likes, retweets]
- **Notable Announcements:** [Hiring, funding, launches]

#### Reddit Community Sentiment (if mentioned)
- **Primary Subreddits:** [Where company is discussed]
- **Discussion Volume:** [Number of mentions found]
- **Sentiment Analysis:** [Positive/Mixed/Negative - with examples]
- **Common Topics:**
  - **Praise:** [What users like]
  - **Complaints:** [Pain points mentioned]
  - **Questions:** [What people ask about]
- **Notable Threads:** [Links to significant discussions]

#### Social Intelligence Synthesis
- **Brand Perception:** [How company is viewed on social vs LinkedIn]
- **Customer Insights:** [Real feedback from Twitter/Reddit vs official messaging]
- **Growth Signals:** [Hiring activity, expansion mentions across platforms]
- **Cultural Indicators:** [Company values in practice vs stated]
- **Competitive Context:** [How they're compared to competitors on social]

## External Intelligence

### Web Presence
- **Speaking/Conferences:** [List if any]
- **Publications/Interviews:** [List if any]
- **Blog Posts/Articles:** [Medium, Substack, Dev.to, personal blog]
- **Media Mentions:** [Notable press mentions]
- **GitHub Projects:** [Open source contributions, personal projects if technical]

### Technical Footprint (if applicable)
- **GitHub Activity:** [contribution level, popular repos]
- **Stack Overflow:** [reputation, areas of expertise]
- **Technical Writing:** [blog posts, tutorials, documentation]

### Additional Context
[Insights from parsed webpages, quotes, expertise areas, unique perspectives]

## Connection Strategy

### Recommended Conversation Topics
1. **[Topic 1]** - [Why: specific post/tweet/comment from which platform]
2. **[Topic 2]** - [Why: company context or cross-platform theme]
3. **[Topic 3]** - [Why: shared interest/industry trend across platforms]
4. **[Topic 4]** - [Why: technical interest from Reddit/GitHub]
5. **[Topic 5]** - [Why: personal interest from Twitter]

### Platform-Specific Engagement

**LinkedIn:**
- **Timing:** [Best days/times based on activity]
- **Approach:** [Professional, comment on specific post]
- **Ice-breaker:** "[Example referencing their LinkedIn content]"

**Twitter/X** (if active):
- **Timing:** [Best days/times]
- **Approach:** [Casual reply to tweet, quote tweet with value-add]
- **Ice-breaker:** "[Example referencing their tweet or discussion]"

**Reddit** (if active):
- **Timing:** [When they're most active]
- **Approach:** [Helpful comment in their frequented subreddit]
- **Ice-breaker:** "[Technical question or insight in relevant subreddit]"

**Direct Outreach:**
- **Best Channel:** [Email/LinkedIn DM/Twitter DM - ranked by likelihood]
- **Timing:** [Optimal day/time synthesized from all platforms]
- **Value Proposition:** [How to position AnySite relevance based on their interests]

### Potential Pain Points
[Inferred from their role, company, posts across platforms - where AnySite could help]
- [Pain point 1 with evidence from platform]
- [Pain point 2 with evidence from platform]
- [Pain point 3 with evidence from platform]

## Strategic Value for AnySite

### Primary Classification
**[Tier 1/2/3/4]: [Customer/Partner/Influencer/Advisor/Talent]**

### Value Dimensions
**Customer Potential:** [High/Medium/Low]
- ICP Fit: [Yes/No - reasoning]
- Decision Authority: [Level]
- Buying Signals: [List any indicators]

**Partnership Potential:** [High/Medium/Low]
- [Specific opportunities if any]

**Network Value:** [High/Medium/Low]
- [Influence level, connection value]

**Advisory/Talent Value:** [High/Medium/Low]
- [Specific expertise value]

### Action Priority
**Priority Level:** [Critical/High/Medium/Low]
**Recommended Timeline:** [Contact within: X days/weeks]

### Next Steps
1. [Specific action item with reasoning]
2. [Follow-up action]
3. [Long-term nurture plan if applicable]

## Analysis Metadata
- **Platforms Analyzed:**
  - LinkedIn: [Profile, Posts, Comments, Reactions]
  - Twitter/X: [Found and analyzed / Not found / Not searched]
  - Reddit: [Activity found / No activity / Not searched]
  - GitHub: [Projects found / Not found / Not applicable]
  - Web: [Articles/interviews found]
- **Data Sources:** [List specific execute() calls made]
- **Cache Keys:** [List cache_key values for re-query or export]
- **Data Freshness:**
  - LinkedIn posts: [date range analyzed]
  - Twitter: [date range if analyzed]
  - Reddit: [date range if analyzed]
- **Total Data Points:** [approximate: X posts, Y tweets, Z comments analyzed]
- **Confidence Factors:**
  - Profile completeness: [High/Medium/Low]
  - Activity data: [High/Medium/Low - per platform]
  - External validation: [High/Medium/Low]
  - Cross-platform consistency: [High/Medium/Low]
- **Limitations:** [Any data gaps, platforms not accessible, or constraints]

Error Handling & Edge Cases

Insufficient Data:

If posts/comments are minimal: focus more on company analysis and role-based inferences
If profile is sparse: use web search more heavily
If company is small/unknown: focus on person's expertise and network

Multiple Profile Matches:

Always confirm with user before proceeding with deep analysis
Present distinguishing factors clearly

v2 Error Handling:

Check llm_hint field in error responses for resolution guidance
412 errors: Resource not found -- search first to find correct alias/URN
422 errors: Wrong parameter format -- typically alias passed where URN required
Continue with available data from other sources on error
Note limitations in report
Suggest manual verification steps

Privacy Considerations:

Only analyze publicly available information
No speculation on private/personal matters
Focus on professional context

Customization Parameters

Users may request analysis depth adjustment:

Quick Analysis (10-15 min):

LinkedIn: Profile + last 10 posts + company basics
Company: LinkedIn company profile only
Twitter/X: Person profile check only (if handle found)
Web: 2-3 targeted searches
Reddit/GitHub: Skip unless specifically requested
Output: Essential info only

Standard Analysis (20-30 min) - DEFAULT:

LinkedIn: Full profile + 20-50 posts + comments/reactions + company analysis
Company: LinkedIn + Twitter account + Reddit mentions search (NEW)
Twitter/X: Person profile + 50 recent tweets (if found)
Reddit: Search for person username + activity (if found)
Web: 5-7 strategic searches + parse 2-3 key pages
GitHub: Quick check for presence (if technical role)
Output: Full workflow as described above

Deep Dive (45-60 min):

LinkedIn: Extended analysis (100+ posts), all activity types, detailed company research
Company: LinkedIn + Twitter (30 posts) + Reddit (comprehensive mentions) + sentiment analysis (NEW)
Twitter/X: Person 100+ tweets, thread analysis, engagement patterns (if found)
Reddit: Person comprehensive comment history, subreddit analysis (if found)
Web: 10-15 searches, parse 5-10 webpages, deep technical footprint
GitHub: Detailed repo analysis, contribution patterns (if technical)
Instagram: Profile and content analysis (if relevant)
Output: Comprehensive cross-platform synthesis with deep insights

Platform-Specific Focus: Users can also request focus on specific platforms:

"Focus on Twitter presence" -> Deep Twitter analysis for person AND company, standard LinkedIn
"Technical profile only" -> LinkedIn + GitHub + Reddit + Stack Overflow (person focused)
"Business profile" -> LinkedIn + web presence + media, skip Reddit/GitHub
"Company deep dive" -> Extended company social analysis across all platforms (NEW)

Default to Standard Analysis unless specified.

Agent Skills: Person Intelligence Analyzer

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