| 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:
- 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.
