From anysite-skills
Analyzes people across LinkedIn, Twitter/X, Reddit, GitHub, blogs, and company data for networking, sales, partnerships, recruitment. Generates cross-platform reports with strategies.
npx claudepluginhub anysiteio/agent-skills --plugin anysite-cliThis skill uses the workspace's default tool permissions.
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.
Gathers competitive intelligence via web scraping, LinkedIn profiles, GitHub activity, Glassdoor sentiment, social media, and community insights. Profiles companies, founders, and strategies for analysis, comparisons, and threat assessment.
Audits digital footprints for employer impressions, generating credit-report-style dashboards of signals from social media, public content, and online presence.
Audits and optimizes LinkedIn profiles via browser snapshots, scores sections like visuals/headline/experience/SSI, creates high-engagement posts and personal branding content.
Share bugs, ideas, or general feedback.
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.
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.All execute() calls may return structured errors with llm_hint fields. When an error occurs:
llm_hint to resolve (typically: search first, then use the returned alias/URN).llm_hint for the correct format.Execute phases sequentially, adapting depth based on available data and user requirements.
Starting with LinkedIn Profile URL:
execute("linkedin", "user", "user", {"user": "<profile_url_or_alias>", "with_experience": true, "with_education": true, "with_skills": true}) with full parametersurn:li:fsd_profile:ACoAAABCDEF) from the response - this is critical for all subsequent API callscache_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:
execute("linkedin", "search", "search_users", {"query": "<name>", "title": "<title>", "company": "<company>", "location": "<location>"}) with all available filtersCritical Data Points to Capture:
Content Analysis (Posts):
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)
next_offset, use get_page(cache_key, offset, limit) to load additional postsquery_cache(cache_key, sort_by={"field": "reactions", "order": "desc"}) to find their most engaging postsEngagement Analysis (Comments & Reactions):
execute("linkedin", "user", "user_comments", {"urn": "<full_fsd_profile_URN>", "count": 30}) with the full URN (format: urn:li:fsd_profile:ACoAAABCDEF)execute("linkedin", "user", "user_reactions", {"urn": "<full_fsd_profile_URN>", "count": 50}) with the full URN (format: urn:li:fsd_profile:ACoAAABCDEF)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
Current Company Deep Dive:
Use execute("linkedin", "company", "company", {"company": "<company_alias_or_url>"}) with company alias/URL from profile
Extract:
cache_key for later filtering with query_cache()Use execute("linkedin", "company", "company_posts", {"urn": "<company_URN_with_company_prefix>", "count": 20}) (count: 20)
company:{id} prefix, NOT fsd_company. Convert: urn:li:fsd_company:1441 -> use company:1441Use execute("duckduckgo", "search", "search", {"query": "<search_terms>"}) for recent news:
Company Social Media Presence:
Company Twitter/X Analysis:
execute("twitter", "search", "search_users", {"query": "[Company Name] official", "count": 5}) to find official company accountexecute("twitter", "user", "user", {"user": "<username>"}) for profile statsexecute("twitter", "user", "user_posts", {"user": "<username>", "count": 20}) (count: 20-30) to analyze:
execute("twitter", "search", "search_posts", {"query": "[Company Name]", "count": 20}) for company mentions:
query_cache(cache_key, sort_by={"field": "favorite_count", "order": "desc"}) to surface most-engaged tweetsCompany Reddit Presence:
execute("reddit", "search", "search_posts", {"query": "[Company Name]", "count": 20}) for company mentionsquery_cache(cache_key, aggregate={"field": "subreddit", "function": "count"}, group_by="subreddit") to see which subreddits discuss the company mostCompany Context Analysis:
A. Twitter/X Analysis (if handle found or identifiable):
Find Twitter Handle:
execute("twitter", "search", "search_users", {"query": "[First Name] [Last Name] [Company]", "count": 5}) with name if not foundProfile Analysis:
execute("twitter", "user", "user", {"user": "<username>"}) with usernameContent Analysis:
execute("twitter", "user", "user_posts", {"user": "<username>", "count": 50}) (count: 50-100 recent tweets)next_offset, use get_page(cache_key, offset, limit) to load more tweets up to 100query_cache(cache_key, aggregate={"field": "favorite_count", "function": "avg"}) to compute average engagementTopic Discovery:
execute("twitter", "search", "search_posts", {"query": "[topic] from:@username", "count": 20}) with person's key interestsB. Reddit Activity (if username discoverable):
Find Reddit Presence:
execute("reddit", "search", "search_posts", {"query": "<name_or_company>", "count": 20}) with name/company mentionsContent Analysis:
execute("reddit", "search", "search_posts", {"query": "author:[username]", "count": 20}) with username if knownquery_cache(cache_key, aggregate={"field": "subreddit", "function": "count"}, group_by="subreddit") to identify most active subredditsTopic Expertise:
execute("reddit", "search", "search_posts", {"query": "[topic] [username or company]", "count": 20}) for specific topicsC. Instagram Presence (optional, if B2C relevant or personal brand focus):
Profile Discovery:
execute("instagram", "search", "search_posts", {"query": "#[name] #[company]", "count": 10}) with hashtagsexecute("instagram", "user", "user", {"user": "<handle>"}) if handle knownContent Style:
execute("instagram", "user", "user_posts", {"user": "<handle>", "count": 20}) (count: 20-30)get_page(cache_key, offset, limit) to continueD. 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]"})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"})GitHub/Tech Presence (if technical role):
execute("duckduckgo", "search", "search", {"query": "[Name] site:github.com"})E. Parse Key Pages:
execute("webparser", "parse", "parse", {"url": "<page_url>"}) for high-value sources:
Platform Priority Strategy:
Cross-Platform Analysis:
Data Export (optional):
export_data(cache_key, "csv") or export_data(cache_key, "json") to save collected datasets for the userConnection Strategy:
Conversation Topics (ranked by relevance, synthesized across all platforms):
Engagement Approach:
Cross-Platform Personality Synthesis:
Value Assessment for AnySite:
Analyze fit across multiple dimensions:
A. Direct Business Value:
B. Partnership Potential:
C. Network & Influence:
D. Talent & Advisory:
Prioritization Matrix:
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]
Insufficient Data:
Multiple Profile Matches:
v2 Error Handling:
llm_hint field in error responses for resolution guidancePrivacy Considerations:
Users may request analysis depth adjustment:
Quick Analysis (10-15 min):
Standard Analysis (20-30 min) - DEFAULT:
Deep Dive (45-60 min):
Platform-Specific Focus: Users can also request focus on specific platforms:
Default to Standard Analysis unless specified.