anysite-person-analyzer

name

anysite-person-analyzer

description

Deep multi-platform intelligence analysis combining LinkedIn (profile, posts, activity), Twitter/X (tweets, engagement), Reddit (discussions, community), web presence (articles, GitHub, blogs), and company intelligence. Use when analyzing people for networking, sales, partnerships, or recruitment. Accepts LinkedIn URL or name+context. Produces comprehensive cross-platform reports with conversation strategies and strategic value assessment for AnySite.

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/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):

1. 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

2. 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

3. 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

4. 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):

1. 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

2. 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

3. 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):

1. 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

2. 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:

1. 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"})

2. 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"})

3. Company-Specific Context:

   • execute("duckduckgo", "search", "search", {"query": "[Name] [Company] announcement"})
   • Look for: press releases, product launches, executive quotes

4. 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:

1. Always analyze: LinkedIn (mandatory) + Web Search
2. High priority: Twitter/X (if found) - usually most revealing for tech audience
3. Medium priority: Reddit (if active) - shows technical depth and community engagement
4. Low priority: Instagram - only if B2C focus or strong personal brand element
5. 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:

1. 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

2. 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

3. 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)


  [X posts/month]
  [Average likes, comments per post]
  [Description]
  [Topics they comment on most]




  [count]
  [count]
  [total count]
  [created date]


  [tweets per day/week]
  [% original tweets vs retweets vs replies]
  [list top 3-5 themes]
  [avg likes, retweets per tweet]
  [any strong opinions or viral tweets]
  [code snippets, technical discussions level]


  [types of accounts: VCs, founders, engineers, etc.]
  [professional/casual/humorous/technical]




  [list top 3-5 subreddits]
  [post/comment karma if visible]


  [% asking questions vs answering vs discussions]
  [level of detail in technical responses]
  [helpful, expert, casual participant]
  [any popular posts or helpful answers]




  [professional characteristics]
  [casual/personal characteristics]
  [technical/community characteristics]
  [topics/interests mentioned across platforms]


  [which platform has highest activity]
  [where they get most responses]
  [professional insights on LinkedIn, hot takes on Twitter, deep tech on Reddit]


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




  [Sector]
  [Employee count]
  [Startup/Scale-up/Enterprise]
  [Brief description]
  [@handle or "Not found"] ([follower count if found])
  [Active/Mentioned/Not found]


  [Key developments from last 6 months]
  [Hiring, funding, expansion signals]
  [Brief competitive context]
  [If relevant]


[Themes from company LinkedIn posts, strategic priorities]




  [Followers, following, tweets]
  [Product announcements, culture, technical content, engagement]
  [Key tweets from last 30 days]
  [tweets per week]
  [avg likes, retweets]
  [Hiring, funding, launches]


  [Where company is discussed]
  [Number of mentions found]
  [Positive/Mixed/Negative - with examples]
 
  [What users like]
  [Pain points mentioned]
  [What people ask about]
  [Links to significant discussions]


  [How company is viewed on social vs LinkedIn]
  [Real feedback from Twitter/Reddit vs official messaging]
  [Hiring activity, expansion mentions across platforms]
  [Company values in practice vs stated]
  [How they're compared to competitors on social]




  [List if any]
  [List if any]
  [Medium, Substack, Dev.to, personal blog]
  [Notable press mentions]
  [Open source contributions, personal projects if technical]


  [contribution level, popular repos]
  [reputation, areas of expertise]
  [blog posts, tutorials, documentation]


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




  - [Why: specific post/tweet/comment from which platform]
  - [Why: company context or cross-platform theme]
  - [Why: shared interest/industry trend across platforms]
  - [Why: technical interest from Reddit/GitHub]
  - [Why: personal interest from Twitter]




  [Best days/times based on activity]
  [Professional, comment on specific post]
  "[Example referencing their LinkedIn content]"

 (if active):
  [Best days/times]
  [Casual reply to tweet, quote tweet with value-add]
  "[Example referencing their tweet or discussion]"

 (if active):
  [When they're most active]
  [Helpful comment in their frequented subreddit]
  "[Technical question or insight in relevant subreddit]"


  [Email/LinkedIn DM/Twitter DM - ranked by likelihood]
  [Optimal day/time synthesized from all platforms]
  [How to position AnySite relevance based on their interests]


[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]







 [High/Medium/Low]
 ICP Fit: [Yes/No - reasoning]
 Decision Authority: [Level]
 Buying Signals: [List any indicators]

 [High/Medium/Low]
 [Specific opportunities if any]

 [High/Medium/Low]
 [Influence level, connection value]

 [High/Medium/Low]
 [Specific expertise value]


 [Critical/High/Medium/Low]
 [Contact within: X days/weeks]


 [Specific action item with reasoning]
 [Follow-up action]
 [Long-term nurture plan if applicable]


 
 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]
  [List specific execute() calls made]
  [List cache

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.