Back to Details

anysite-person-analyzer

Compare original and translation side by side

🇺🇸

Original

English
🇨🇳

Translation

Chinese

Person Intelligence Analyzer

人物智能分析器

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.
结合LinkedIn、Twitter/X、Reddit、GitHub和网络足迹数据的全面跨平台智能分析，生成包含跨平台个性洞察的可落地智能报告。

Analysis Workflow

分析工作流

Execute phases sequentially, adapting depth based on available data and user requirements.
按顺序执行各个阶段，根据可用数据和用户需求调整分析深度。

Phase 1: Initial Data Collection

第一阶段：初始数据收集

Starting with LinkedIn Profile URL:
  1. Use 
    get_linkedin_profile
    with full parameters (education, experience, skills)
  2. Extract and save the full URN (format: 
    urn:li:fsd_profile:ACoAAABCDEF
    ) - this is critical for all subsequent API calls
  3. Also extract: company URN, current role, location, connections count
  4. Record profile completeness for confidence scoring
IMPORTANT - URN Format: Always use the complete URN format 
urn:li:fsd_profile:ACoAAABCDEF
from the profile response for all subsequent calls to 
get_linkedin_user_posts
, 
get_linkedin_user_comments
, and 
get_linkedin_user_reactions
. Do not use shortened versions or profile URLs.
Starting with Name + Context:
  1. Use 
    search_linkedin_users
    with all available filters:
    • Name, title, company keywords, location, school
  2. If multiple matches: present top 3-5 candidates with distinguishing details
  3. After user confirmation, proceed with confirmed profile
Critical Data Points to Capture:
  • Current company and role (with start date)
  • Previous roles (last 2-3 positions)
  • Education background
  • Skills and endorsements
  • Connection count (indicator of network size)
  • Profile headline and summary
从LinkedIn个人资料链接开始：
  1. 使用
    get_linkedin_profile
    工具并传入完整参数（教育经历、工作经验、技能）
  2. 提取并保存完整URN（格式：
    urn:li:fsd_profile:ACoAAABCDEF
    ）——这是后续所有API调用的关键
  3. 同时提取：企业URN、当前职位、所在地、人脉数量
  4. 记录个人资料完整度，用于置信度评分
重要提示 - URN格式： 后续调用
get_linkedin_user_posts
、
get_linkedin_user_comments
和
get_linkedin_user_reactions
这三个工具时，必须使用第一阶段获取的完整格式URN（
urn:li:fsd_profile:ACoAAABCDEF
）。请勿使用简化版本或LinkedIn个人资料链接。
从姓名+背景信息开始：
  1. 使用
    search_linkedin_users
    工具并传入所有可用筛选条件：
    • 姓名、职位、企业关键词、所在地、学校
  2. 如果存在多个匹配结果：展示前3-5个候选人及其差异化细节
  3. 获得用户确认后，基于确认的个人资料继续分析
需捕获的关键数据点：
  • 当前企业和职位（含入职日期）
  • 过往职位（最近2-3个）
  • 教育背景
  • 技能与背书
  • 人脉数量（体现网络规模的指标）
  • 个人资料标题和简介

Phase 2: Activity & Engagement Analysis

第二阶段：动态与互动分析

Content Analysis (Posts):
  1. Use 
    get_linkedin_user_posts
    with the full URN (format: 
    urn:li:fsd_profile:ACoAAABCDEF
    )
    • Count: 20-50 depending on activity level
    • Posted after filter: last 90 days for active users, 180 days if low activity
  2. Analyze for:
    • Topics and themes (use clustering: technical, leadership, industry trends, personal)
    • 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
Engagement Analysis (Comments & Reactions):
  1. Use 
    get_linkedin_user_comments
    with the full URN (format: 
    urn:li:fsd_profile:ACoAAABCDEF
    )
    • Count: 30
  2. Use 
    get_linkedin_user_reactions
    with the full URN (format: 
    urn:li:fsd_profile:ACoAAABCDEF
    )
    • Count: 50
  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 (
get_linkedin_user_posts
, 
get_linkedin_user_comments
, 
get_linkedin_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 errors.
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.
内容分析（帖子）：
  1. 使用
    get_linkedin_user_posts
    工具并传入完整URN（格式：
    urn:li:fsd_profile:ACoAAABCDEF
    ）
    • 数量：根据活跃度选择20-50条
    • 发布时间筛选：活跃用户取最近90天，低活跃用户取最近180天
  2. 分析维度：
    • 主题与话题（使用聚类法：技术类、领导力类、行业趋势类、个人类）
    • 互动指标（每条帖子的点赞、评论数——计算平均值）
    • 发布频率（计算每周/每月发帖量）
    • 内容风格（思想领导力、内容分享、个人故事、企业动态）
    • 语言与语气
互动分析（评论与互动）：
  1. 使用
    get_linkedin_user_comments
    工具并传入完整URN（格式：
    urn:li:fsd_profile:ACoAAABCDEF
    ）
    • 数量：30条
  2. 使用
    get_linkedin_user_reactions
    工具并传入完整URN（格式：
    urn:li:fsd_profile:ACoAAABCDEF
    ）
    • 数量：50条
  3. 分析维度：
    • 互动对象（职位层级、所在行业）
    • 引发他们互动的话题
    • 互动风格（支持型、挑战型、信息型）
    • 响应模式（快速互动vs深度评论）
关键注意事项： 
get_linkedin_user_posts
、
get_linkedin_user_comments
、
get_linkedin_user_reactions
这三个工具都需要第一阶段获取的完整格式URN（
urn:li:fsd_profile:ACoAAABCDEF
）。使用LinkedIn个人资料链接或不完整URN会导致调用错误。
输出：互动画像
  • 核心内容主题（按频率排序）
  • 互动等级：高/中/低（每月发帖量、每周互动数）
  • 影响力指标：粉丝数、帖子平均互动率
  • 沟通风格：正式/非正式、技术向/通用型等

Phase 3: Company Intelligence

第三阶段：企业情报分析

Current Company Deep Dive:
  1. Use 
    get_linkedin_company
    with company URN from profile
  2. Extract:
    • Company size, industry, specialties
    • Growth indicators (employee count trends if available)
    • Company description and mission
    • Recent updates/news
  3. Use 
    get_linkedin_company_posts
    (count: 20)
    • Analyze company communication themes
    • Identify strategic priorities
    • Note any mentions of funding, hiring, expansion
  4. Use 
    duckduckgo_search
    for recent news:
    • "[Company name] funding news"
    • "[Company name] expansion launch product"
    • Prioritize results from last 6 months
Company Social Media Presence:
  1. Company Twitter/X Analysis:
    • Use 
      search_twitter_users
      to find official company account: "[Company Name] official"
    • If found, use 
      get_twitter_user
      for profile stats
    • Use 
      get_twitter_user_posts
      (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 
      search_twitter_posts
      for company mentions: "[Company Name]"
      • Customer sentiment (complaints vs praise)
      • Industry discussion about the company
      • Competitor comparisons
      • Notable tweets from employees
  2. Company Reddit Presence:
    • Use 
      search_reddit_posts
      for company mentions: "[Company Name]"
    • 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
    • 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)
当前企业深度调研：
  1. 使用
    get_linkedin_company
    工具并传入个人资料中的企业URN
  2. 提取信息：
    • 企业规模、行业、核心业务
    • 增长指标（若有可用的员工数量趋势）
    • 企业描述与使命
    • 近期动态/新闻
  3. 使用
    get_linkedin_company_posts
    工具（数量：20条）
    • 分析企业沟通主题
    • 识别战略优先级
    • 记录融资、招聘、扩张相关提及
  4. 使用
    duckduckgo_search
    工具搜索近期新闻：
    • "[企业名称] 融资新闻"
    • "[企业名称] 扩张 产品发布"
    • 优先展示最近6个月的结果
企业社交媒体足迹：
  1. 企业Twitter/X分析：
    • 使用
      search_twitter_users
      工具查找官方企业账号："[企业名称] official"
    • 若找到，使用
      get_twitter_user
      工具获取账号统计数据
    • 使用
      get_twitter_user_posts
      工具（数量：20-30条）分析：
      • 产品公告与发布
      • 企业文化与价值观
      • 与客户及社区的互动
      • 招聘公告（增长信号）
      • 技术内容（若为科技企业）
    • 使用
      search_twitter_posts
      工具搜索企业提及："[企业名称]"
      • 客户情绪（投诉vs好评）
      • 行业对企业的讨论
      • 竞品对比
      • 员工发布的重要推文
  2. 企业Reddit足迹：
    • 使用
      search_reddit_posts
      工具搜索企业提及："[企业名称]"
    • 重点关注：
      • r/startups社区中关于该企业的讨论
      • 行业专属子版块的提及（如r/SaaS、r/artificial等）
      • 客户体验与评价
      • 关于其产品/平台的技术讨论
      • 招聘体验类内容（类似Glassdoor的洞察）
      • 创始人/团队的AMA或讨论
    • 情绪分析：社区感知为正面/负面/中性
    • 用户/客户提及的痛点
企业背景分析：
  • 商业模式与收入来源
  • 技术栈（若为科技企业）
  • 市场地位与竞品
  • 近期成就或挑战
  • 企业帖子体现的文化指标
  • 社交情绪（Twitter提及、Reddit讨论）
  • 社区互动（企业在社交平台的响应方式）
  • 增长信号（Twitter上的招聘推文、扩张公告）
  • 客户痛点（Reddit投诉、Twitter反馈的问题）

Phase 4: Multi-Platform Intelligence Enrichment

第四阶段：跨平台智能补充分析

A. Twitter/X Analysis (if handle found or identifiable):
  1. Find Twitter Handle:
    • Check LinkedIn profile bio/description for @username
    • Use 
      search_twitter_users
      with name if not found: "[First Name] [Last Name] [Company]"
    • Verify match by checking bio, profile description
  2. Profile Analysis:
    • Use 
      get_twitter_user
      with username
    • Extract: follower count, following count, tweet count, bio, location
    • Note: verification status, profile creation date
  3. Content Analysis:
    • Use 
      get_twitter_user_posts
      (count: 50-100 recent tweets)
    • 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
  4. Topic Discovery:
    • Use 
      search_twitter_posts
      with person's key interests: "[topic] from:@username"
    • 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 
      search_reddit_posts
      with name/company mentions
    • Look for: "AMA" posts, technical discussions, community contributions
  2. Content Analysis:
    • Use 
      search_reddit_posts
      with username if known: "author:[username]"
    • Analyze for:
      • Subreddit preferences (which communities they're active in)
      • Technical depth of contributions
      • Helping behavior vs self-promotion ratio
      • Community reputation indicators
  3. Topic Expertise:
    • Use 
      search_reddit_posts
      for specific topics: "[topic] [username or company]"
    • 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 
      search_instagram_posts
      with hashtags: "#[name] #[company]"
    • Use 
      get_instagram_user
      if handle known
  2. Content Style:
    • Use 
      get_instagram_user_posts
      (count: 20-30)
    • Analyze for: personal brand vs professional content
    • Note: visual style, posting frequency, engagement rate
D. Web Intelligence & Media Presence:
  1. Professional Presence:
    • duckduckgo_search
      : "[Name] [Company] speaker conference"
    • duckduckgo_search
      : "[Name] interview podcast"
    • duckduckgo_search
      : "[Name] article blog post"
  2. Expertise & Thought Leadership:
    • duckduckgo_search
      : "[Name] expertise [primary topic from posts]"
    • Check for: publications, talks, media mentions
    • duckduckgo_search
      : "[Name] [key topic] site:medium.com OR site:dev.to OR site:substack.com"
  3. Company-Specific Context:
    • duckduckgo_search
      : "[Name] [Company] announcement"
    • Look for: press releases, product launches, executive quotes
  4. GitHub/Tech Presence (if technical role):
    • duckduckgo_search
      : "[Name] site:github.com"
    • Look for: open source contributions, personal projects
E. Parse Key Pages:
  • Use 
    parse_webpage
    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
A. Twitter/X分析（若找到账号或可识别）：
  1. 查找Twitter账号：
    • 检查LinkedIn个人资料简介中的@用户名
    • 若未找到，使用
      search_twitter_users
      工具搜索姓名："[名] [姓] [企业]"
    • 通过简介、个人资料描述验证匹配度
  2. 账号分析：
    • 使用
      get_twitter_user
      工具并传入用户名
    • 提取信息：粉丝数、关注数、推文总数、简介、所在地
    • 记录：认证状态、账号创建日期
  3. 内容分析：
    • 使用
      get_twitter_user_posts
      工具（数量：50-100条近期推文）
    • 分析维度：
      • 技术能力信号（代码片段、技术讨论）
      • 行业观点与热门话题
      • 个人兴趣与爱好
      • 与其他意见领袖的互动
      • 转发vs原创内容比例
    • 计算：每日推文数量、平均互动率
  4. 主题发现：
    • 使用
      search_twitter_posts
      工具搜索该用户的核心兴趣："[话题] from:@用户名"
    • 识别重复出现的主题与专业领域
    • 记录争议性或立场鲜明的观点
B. Reddit动态分析（若可发现用户名）：
  1. 查找Reddit足迹：
    • 搜索其他平台的用户名
    • 使用
      search_reddit_posts
      工具搜索姓名/企业提及
    • 重点关注：AMA帖子、技术讨论、社区贡献
  2. 内容分析：
    • 若已知用户名，使用
      search_reddit_posts
      工具搜索："author:[用户名]"
    • 分析维度：
      • 子版块偏好（活跃的社区）
      • 贡献内容的技术深度
      • 帮助他人vs自我推广的比例
      • 社区声誉指标
  3. 主题专业度：
    • 使用
      search_reddit_posts
      工具搜索特定话题："[话题] [用户名或企业]"
    • 识别其被视为专家/提供帮助的领域
    • 记录其发起的热门帖子或讨论
C. Instagram足迹（可选，适用于B2C场景或个人品牌重点）：
  1. 账号发现：
    • 检查LinkedIn或Twitter中是否有提及
    • 使用
      search_instagram_posts
      工具搜索话题标签："#[姓名] #[企业]"
    • 若已知账号，使用
      get_instagram_user
      工具
  2. 内容风格：
    • 使用
      get_instagram_user_posts
      工具（数量：20-30条）
    • 分析维度：个人品牌vs专业内容占比
    • 记录：视觉风格、发布频率、互动率
D. 网络情报与媒体足迹：
  1. 专业足迹：
    • duckduckgo_search
      搜索："[姓名] [企业] 演讲 会议"
    • duckduckgo_search
      搜索："[姓名] 采访 播客"
    • duckduckgo_search
      搜索："[姓名] 文章 博客"
  2. 专业度与思想领导力：
    • duckduckgo_search
      搜索："[姓名] 专业领域 [帖子中的核心话题]"
    • 检查：出版物、演讲、媒体提及
    • duckduckgo_search
      搜索："[姓名] [核心话题] site:medium.com OR site:dev.to OR site:substack.com"
  3. 企业特定背景：
    • duckduckgo_search
      搜索："[姓名] [企业] 公告"
    • 查找：新闻稿、产品发布、高管引用
  4. GitHub/技术足迹（若为技术岗位）：
    • duckduckgo_search
      搜索："[姓名] site:github.com"
    • 查找：开源贡献、个人项目
E. 关键页面解析：
  • 使用
    parse_webpage
    工具处理高价值来源：
    • 个人博客/网站（若在任何个人资料中提及）
    • 近期采访或播客出镜内容
    • 会议演讲者资料
    • 企业"关于我们-团队"页面
    • 重要的Medium/Substack文章
    • 热门Reddit AMA或讨论
  • 提取信息：简介、专业领域、引用内容、兴趣、独特观点
平台优先级策略：
  1. 必分析平台： LinkedIn（强制）+ 网络搜索
  2. 高优先级： Twitter/X（若找到）——通常对科技受众最具参考价值
  3. 中优先级： Reddit（若活跃）——体现技术深度与社区互动
  4. 低优先级： Instagram——仅适用于B2C场景或个人品牌突出的情况
  5. 场景依赖： GitHub——对技术岗位至关重要，对业务岗位参考价值较低
跨平台分析：
  • 对比各平台的语气（专业的LinkedIn vs 随意的Twitter）
  • 识别平台专属内容主题
  • 记录各平台的互动等级
  • 综合分析一致兴趣点与平台专属行为

Phase 5: Cross-Platform Strategic Analysis & Report Generation

第五阶段：跨平台战略分析与报告生成

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
人脉拓展策略：
  1. 沟通话题（按相关性排序，综合所有平台内容）：
    • 来自LinkedIn帖子/评论的前3-5个话题
    • 来自Twitter/X的热门观点或鲜明立场
    • 来自Reddit的技术讨论
    • 跨平台参与的行业趋势
    • 共同兴趣或人脉（若有）
    • 需提及的企业近期成就
  2. 互动方式：
    • 最佳渠道：LinkedIn评论、Twitter回复、Reddit评论、私信、邮件
    • 渠道偏好：记录其最活跃/最易响应的平台
    • 时间选择：基于各平台的发布模式（例如："Twitter晚间最活跃，LinkedIn周二上午最活跃"）
    • 破冰方式：引用与AnySite相关的特定帖子/评论/推文
    • 平台专属语气：LinkedIn需专业，Twitter可随意，Reddit要技术向
  3. 跨平台个性综合：
    • 专业形象（LinkedIn）vs 个人形象（Twitter/Reddit）
    • 技术深度指标（Reddit讨论、GitHub动态）
    • 各平台的沟通风格差异
    • 一致兴趣点（跨平台提及的话题）
对AnySite的战略价值评估：
从多维度分析匹配度：
A. 直接业务价值：
  • 潜在客户：其企业是否符合AnySite的理想客户画像？
    • B2B SaaS、AI企业、数据密集型业务
    • 规模指标：10-500名员工，成长阶段
    • 痛点：提及数据提取、API集成、Agent开发相关内容
  • 决策层级：高管层、副总裁、总监、经理
  • 预算权限指标
B. 合作潜力：
  • 技术协同（互补工具/平台）
  • 渠道合作机会
  • 集成可能性
  • 联合营销潜力
C. 网络与影响力：
  • 网络规模与质量（1万+人脉=超级连接器）
  • 行业影响力（意见领袖、频繁演讲者）
  • 投资人脉（其网络中的风投、天使投资人）
  • 引荐潜力
D. 人才与顾问价值：
  • 与顾问/导师岗位的专业匹配度
  • 未来扩张的潜在招聘对象
  • 填补空缺的领域知识
优先级矩阵：
  • 一级（高价值线索）：决策者+理想客户画像匹配+高互动性
  • 二级（中价值线索）：中层管理者+理想客户画像匹配 或 意见领袖+相关网络
  • 三级（长期培育）：未来潜在价值，需建立关系
  • 四级（低优先级）：无明确匹配度，仅维持基础连接

Output Format

输出格式

Generate comprehensive markdown report with sections:
markdown
undefined
生成包含以下章节的全面Markdown报告：
markdown
undefined

Person Intelligence Report: [Name]

人物智能报告: [姓名]

Generated: [Date] Analysis Depth: [Quick/Standard/Deep] Confidence Score: [0-100%] based on data availability
生成日期: [日期] 分析深度: [快速/标准/深度] 置信度评分: [0-100%] 基于数据可用性

Executive Summary

执行摘要

[2-3 sentences: who they are, what they do, why they matter to AnySite]
[2-3句话：人物身份、核心工作、对AnySite的价值]

Professional Profile

专业资料

  • 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]
  • 当前职位: [职位名称] @ [企业名称] (自 [日期] 起)
  • 所在地: [城市, 国家]
  • 从业经验: [X年 行业/岗位经验]
  • 教育背景: [学位, 院校]
  • 人脉规模: [LinkedIn人脉数量]
  • LinkedIn资料: [链接]
  • Twitter/X: [@账号或"未找到"] (若找到则标注粉丝数)
  • Reddit: [u/用户名或"未找到/未搜索"]
  • GitHub: [用户名或"未找到"] (若为技术岗位)
  • 个人网站: [若找到则标注链接]

Key Background

核心背景

[2-3 paragraphs covering:]
  • Career trajectory and notable positions
  • Expertise and specializations
  • Notable achievements or credentials
[2-3段落涵盖:]
  • 职业发展轨迹与重要职位
  • 专业领域与专长
  • 重要成就或资质

Multi-Platform Activity Analysis

跨平台动态分析

LinkedIn Activity (Last 90 Days)

LinkedIn动态（最近90天）

Content Themes

内容主题

  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)
  1. [主题1] (占帖子的40%)
    • 核心话题: [列表]
    • 示例帖子: "[引用或摘要]"
  2. [主题2] (占帖子的30%)
    • 核心话题: [列表]
  3. [主题3] (占帖子的20%)

Engagement Patterns

互动模式

  • Posting Frequency: [X posts/month]
  • Engagement Rate: [Average likes, comments per post]
  • Response Style: [Description]
  • Active Topics: [Topics they comment on most]
  • 发布频率: [X条/月]
  • 互动率: [平均点赞、评论数]
  • 响应风格: [描述]
  • 活跃话题: [其评论最多的话题]

Twitter/X Activity (if found)

Twitter/X动态（若找到）

Profile Stats

账号统计

  • Followers: [count]
  • Following: [count]
  • Tweets: [total count]
  • Account Age: [created date]
  • 粉丝数: [数量]
  • 关注数: [数量]
  • 推文总数: [数量]
  • 账号创建时间: [日期]

Content Analysis (Recent 50-100 tweets)

内容分析（最近50-100条推文）

  • 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]
  • 发布频率: [每天/每周推文数量]
  • 内容构成: [%原创推文 vs 转发 vs 回复]
  • 核心话题: [前3-5个主题列表]
  • 互动等级: [平均点赞、转发数]
  • 重要观点: [任何鲜明立场或 viral 推文]
  • 技术深度: [代码片段、技术讨论的层级]

Community Engagement

社区互动

  • Engages with: [types of accounts: VCs, founders, engineers, etc.]
  • Tone: [professional/casual/humorous/technical]
  • 互动对象: [账号类型：风投、创始人、工程师等]
  • 语气: [专业/随意/幽默/技术向]

Reddit Activity (if found)

Reddit动态（若找到）

Subreddit Preferences

子版块偏好

  • Most Active In: [list top 3-5 subreddits]
  • Karma: [post/comment karma if visible]
  • 最活跃社区: [前3-5个子版块列表]
  • ** karma值:** [若可见则标注帖子/评论 karma]

Contribution Style

贡献风格

  • 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]
  • 互动类型: [%提问 vs 解答 vs 讨论]
  • 技术深度: [技术回复的详细程度]
  • 社区声誉: [乐于助人、专家、普通参与者]
  • 重要贡献: [任何热门帖子或有用解答]

Cross-Platform Synthesis

跨平台综合

Personality Comparison

个性对比

  • LinkedIn Persona: [professional characteristics]
  • Twitter Persona: [casual/personal characteristics]
  • Reddit Persona: [technical/community characteristics]
  • Consistency: [topics/interests mentioned across platforms]
  • LinkedIn形象: [专业特征]
  • Twitter形象: [随意/个人特征]
  • Reddit形象: [技术/社区特征]
  • 一致性: [跨平台提及的话题/兴趣]

Platform Preferences

平台偏好

  • 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]
  • 最活跃平台: [互动量最高的平台]
  • 最佳互动渠道: [获得最多响应的平台]
  • 内容类型: [LinkedIn的专业洞察、Twitter的热门观点、Reddit的深度技术内容]

Communication Style

沟通风格

[Synthesized description: formal/casual, technical depth, storytelling approach, cross-platform consistency or variation]
[综合描述：正式/随意、技术深度、叙事方式、跨平台一致性或差异]

Company Intelligence: [Company Name]

企业情报: [企业名称]

Company Overview

企业概述

  • 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]
  • 行业: [领域]
  • 规模: [员工数量]
  • 阶段: [初创/成长期/成熟企业]
  • 使命: [简要描述]
  • Twitter: [@账号或"未找到"] (若找到则标注粉丝数)
  • Reddit足迹: [活跃/被提及/未找到]

Strategic Context

战略背景

  • Recent News: [Key developments from last 6 months]
  • Growth Indicators: [Hiring, funding, expansion signals]
  • Market Position: [Brief competitive context]
  • Technology Focus: [If relevant]
  • 近期新闻: [最近6个月的核心动态]
  • 增长信号: [招聘、融资、扩张迹象]
  • 市场地位: [简要竞争背景]
  • 技术重点: [若相关]

Company LinkedIn Content Analysis

企业LinkedIn内容分析

[Themes from company LinkedIn posts, strategic priorities]
[企业LinkedIn帖子的主题、战略优先级]

Company Social Media Presence

企业社交媒体足迹

Twitter/X Activity (if found)

Twitter/X动态（若找到）

  • 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]
  • 账号统计: [粉丝数、关注数、推文数]
  • 内容构成: [产品公告、文化、技术内容、互动]
  • 近期亮点: [最近30天的核心推文]
  • 发布频率: [每周推文数量]
  • 互动等级: [平均点赞、转发数]
  • 重要公告: [招聘、融资、发布]

Reddit Community Sentiment (if mentioned)

Reddit社区情绪（若被提及）

  • 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]
  • 核心子版块: [讨论该企业的社区]
  • 讨论量: [找到的提及数量]
  • 情绪分析: [正面/混合/负面 - 附示例]
  • 常见话题:
    • 好评: [用户认可的点]
    • 投诉: [提及的痛点]
    • 问题: [用户常问的内容]
  • 重要讨论: [指向关键讨论的链接]

Social Intelligence Synthesis

社交情报综合

  • 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]
  • 品牌感知: [社交平台 vs LinkedIn上的企业形象差异]
  • 客户洞察: [Twitter/Reddit的真实反馈 vs 官方宣传]
  • 增长信号: [跨平台提及的招聘活动、扩张计划]
  • 文化指标: [实际践行的企业价值观 vs 宣传的价值观]
  • 竞争背景: [社交平台上与竞品的对比]

External Intelligence

外部情报

Web Presence

网络足迹

  • 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]
  • 演讲/会议: [若有则列出]
  • 出版物/采访: [若有则列出]
  • 博客/文章: [Medium、Substack、Dev.to、个人博客]
  • 媒体提及: [重要新闻报道]
  • GitHub项目: [开源贡献、个人项目（若为技术岗位）]

Technical Footprint (if applicable)

技术足迹（若适用）

  • GitHub Activity: [contribution level, popular repos]
  • Stack Overflow: [reputation, areas of expertise]
  • Technical Writing: [blog posts, tutorials, documentation]
  • GitHub动态: [贡献等级、热门仓库]
  • Stack Overflow: [声誉、专业领域]
  • 技术写作: [博客文章、教程、文档]

Additional Context

补充背景

[Insights from parsed webpages, quotes, expertise areas, unique perspectives]
[来自解析网页的洞察、引用内容、专业领域、独特观点]

Connection Strategy

人脉拓展策略

Recommended Conversation Topics

推荐沟通话题

  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]
  1. [话题1] - [原因：来自某平台的特定帖子/推文/评论]
  2. [话题2] - [原因：企业背景或跨平台主题]
  3. [话题3] - [原因：跨平台的共同兴趣/行业趋势]
  4. [话题4] - [原因：来自Reddit/GitHub的技术兴趣]
  5. [话题5] - [原因：来自Twitter的个人兴趣]

Platform-Specific Engagement

平台专属互动方式

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]
LinkedIn:
  • 时间选择: [基于动态的最佳日期/时间]
  • 方式: [专业风格，评论特定帖子]
  • 破冰示例: "[引用其LinkedIn内容的示例]"
Twitter/X（若活跃）:
  • 时间选择: [最佳日期/时间]
  • 方式: [随意回复推文，引用推文并附加价值]
  • 破冰示例: "[引用其推文或讨论的示例]"
Reddit（若活跃）:
  • 时间选择: [其最活跃的时间]
  • 方式: [在其常去的子版块发布有帮助的评论]
  • 破冰示例: [在相关子版块提出技术问题或分享见解]
直接触达:
  • 最佳渠道: [邮件/LinkedIn私信/Twitter私信 - 按可能性排序]
  • 时间选择: [综合所有平台得出的最佳日期/时间]
  • 价值主张: [基于其兴趣定位AnySite的相关性]

Potential Pain Points

潜在痛点

[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]
[从其岗位、企业、跨平台帖子中推断的痛点 - AnySite可提供帮助的方向]
  • [痛点1及来自某平台的证据]
  • [痛点2及来自某平台的证据]
  • [痛点3及来自某平台的证据]

Strategic Value for AnySite

对AnySite的战略价值

Primary Classification

核心分类

[Tier 1/2/3/4]: [Customer/Partner/Influencer/Advisor/Talent]
[一级/二级/三级/四级]: [客户/合作伙伴/意见领袖/顾问/人才]

Value Dimensions

价值维度

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]
客户潜力: [高/中/低]
  • 理想客户画像匹配: [是/否 - 理由]
  • 决策权限: [层级]
  • 购买信号: [列出任何指标]
合作潜力: [高/中/低]
  • [若有则列出具体机会]
网络价值: [高/中/低]
  • [影响力等级、人脉价值]
顾问/人才价值: [高/中/低]
  • [具体专业价值]

Action Priority

行动优先级

Priority Level: [Critical/High/Medium/Low] Recommended Timeline: [Contact within: X days/weeks]
优先级: [关键/高/中/低] 推荐时间线: [联系时间：X天/周内]

Next Steps

下一步行动

  1. [Specific action item with reasoning]
  2. [Follow-up action]
  3. [Long-term nurture plan if applicable]
  1. [具体行动项及理由]
  2. [跟进行动]
  3. [若适用则列出长期培育计划]

Analysis Metadata

分析元数据

  • 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 tools used]
  • 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]
undefined
  • 分析平台:
    • LinkedIn: [✓ 资料、帖子、评论、互动]
    • Twitter/X: [✓ 找到并分析 / ✗ 未找到 / - 未搜索]
    • Reddit: [✓ 找到动态 / ✗ 无动态 / - 未搜索]
    • GitHub: [✓ 找到项目 / ✗ 未找到 / - 不适用]
    • 网络: [✓ 找到文章/采访]
  • 数据来源: [列出使用的具体工具]
  • 数据新鲜度:
    • LinkedIn帖子: [分析的日期范围]
    • Twitter: [若分析则标注日期范围]
    • Reddit: [若分析则标注日期范围]
  • 总数据量: [约数：X条帖子、Y条推文、Z条评论]
  • 置信度因素:
    • 资料完整度: [高/中/低]
    • 动态数据: [高/中/低 - 按平台]
    • 外部验证: [高/中/低]
    • 跨平台一致性: [高/中/低]
  • 局限性: [任何数据缺口、无法访问的平台或约束条件]
undefined

Error Handling & Edge Cases

错误处理与边缘情况

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
Rate Limiting / API Errors:
  • Continue with available data from other sources
  • 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
数据不足:
  • 若帖子/评论数量极少：重点转向企业分析与基于岗位的推断
  • 若个人资料信息稀疏：加大网络搜索的使用力度
  • 若企业规模小/不知名：重点关注人物的专业度与人脉
多个资料匹配:
  • 进行深度分析前务必获得用户确认
  • 清晰展示差异化因素
速率限制 / API错误:
  • 使用其他来源的可用数据继续分析
  • 在报告中注明局限性
  • 建议手动验证步骤
隐私考量:
  • 仅分析公开可获取的信息
  • 不猜测私人/个人事务
  • 聚焦专业背景

Customization Parameters

自定义参数

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.
用户可请求调整分析深度：
快速分析（10-15分钟）:
  • LinkedIn: 资料 + 最近10条帖子 + 企业基础信息
  • 企业: 仅LinkedIn企业资料
  • Twitter/X: 仅检查人物账号（若找到）
  • 网络: 2-3次定向搜索
  • Reddit/GitHub: 跳过，除非特别要求
  • 输出: 仅核心信息
标准分析（20-30分钟）- 默认:
  • LinkedIn: 完整资料 + 20-50条帖子 + 评论/互动 + 企业分析
  • 企业: LinkedIn + Twitter账号 + Reddit提及搜索（新增）
  • Twitter/X: 人物资料 + 50条近期推文（若找到）
  • Reddit: 搜索人物用户名 + 动态（若找到）
  • 网络: 5-7次战略搜索 + 解析2-3个关键页面
  • GitHub: 快速检查是否存在（若为技术岗位）
  • 输出: 如上所述的完整工作流结果
深度分析（45-60分钟）:
  • LinkedIn: 扩展分析（100+条帖子）、所有互动类型、详细企业调研
  • 企业: LinkedIn + Twitter（30条帖子） + Reddit（全面提及） + 情绪分析（新增）
  • Twitter/X: 人物100+条推文、线程分析、互动模式（若找到）
  • Reddit: 人物完整评论历史、子版块分析（若找到）
  • 网络: 10-15次搜索、解析5-10个网页、深度技术足迹
  • GitHub: 详细仓库分析、贡献模式（若为技术岗位）
  • Instagram: 资料与内容分析（若相关）
  • 输出: 包含深度洞察的全面跨平台综合报告
平台专属聚焦: 用户也可请求聚焦特定平台：
  • "聚焦Twitter足迹" → 深度分析人物与企业的Twitter数据，标准分析LinkedIn
  • "仅技术资料" → 聚焦LinkedIn + GitHub + Reddit + Stack Overflow（人物）
  • "仅业务资料" → 聚焦LinkedIn + 网络足迹 + 媒体，跳过Reddit/GitHub
  • "企业深度调研" → 全面跨平台分析企业社交媒体（新增）
默认使用标准分析，除非用户指定。