Enrich Lead

作者 anthropicsae1513ea94dc無授權條款27K 個星標收錄於 2026年10月8日更新於 2026年10月8日儲存庫今天更新

Instant lead enrichment. Drop a name, company, LinkedIn URL, or email and get the full contact card with email, phone, title, company intel, and next actions.

精選僅含說明Marketing & SalesResearch & Analysis
AI 產生的概覽

依姓名、公司、LinkedIn 連結或電子郵件，透過 Apollo 補全潛在客戶並產生完整聯絡人卡片。

功能
解析姓名、公司、LinkedIn 連結或電子郵件等識別資訊，透過 Apollo 人員比對找出該聯絡人，比對失敗時改用人員搜尋。接著補全其公司資訊以取得企業概況，並輸出格式化的聯絡人卡片，包含電子郵件、電話、所在地、LinkedIn，以及公司營收、募資與總部資訊。最後提供後續操作選項，例如將聯絡人儲存至 Apollo、加入序列，或尋找同事與相似人員。
適用情境
當你手上只有潛在客戶的少量身分資訊，需要整合成一份完整聯絡人檔案時使用。適合銷售或商務開發研究，在接觸前取得聯絡方式與公司背景。
執行需求
需要存取 Apollo MCP 工具（人員比對、人員混合搜尋、組織資訊補全、建立聯絡人）以及連線至 Apollo 的網路。補全會消耗 Apollo 額度，技能要求在呼叫前提醒使用者。僅為指示，未附帶指令碼。

Enrich Lead

Turn any identifier into a full contact dossier. The user provides identifying info via "$ARGUMENTS".

Examples

  • /apollo:enrich-lead Tim Zheng at Apollo
  • /apollo:enrich-lead https://www.linkedin.com/in/timzheng
  • /apollo:enrich-lead [email protected]
  • /apollo:enrich-lead Jane Smith, VP Engineering, Notion
  • /apollo:enrich-lead CEO of Figma

Step 1 — Parse Input

From "$ARGUMENTS", extract every identifier available:

  • First name, last name
  • Company name or domain
  • LinkedIn URL
  • Email address
  • Job title (use as a matching hint)

If the input is ambiguous (e.g. just "CEO of Figma"), first use mcp__claude_ai_Apollo_MCP__apollo_mixed_people_api_search with relevant title and domain filters to identify the person, then proceed to enrichment.

Step 2 — Enrich the Person

Credit warning: Tell the user enrichment consumes 1 Apollo credit before calling.

Use mcp__claude_ai_Apollo_MCP__apollo_people_match with all available identifiers:

  • first_name, last_name if name is known
  • domain or organization_name if company is known
  • linkedin_url if LinkedIn is provided
  • email if email is provided
  • Set reveal_personal_emails to true

If the match fails, try mcp__claude_ai_Apollo_MCP__apollo_mixed_people_api_search with looser filters and present the top 3 candidates. Ask the user to pick one, then re-enrich.

Step 3 — Enrich Their Company

Use mcp__claude_ai_Apollo_MCP__apollo_organizations_enrich with the person's company domain to pull firmographic context.

Step 4 — Present the Contact Card

Format the output exactly like this:

[Full Name] | [Title] [Company Name] · [Industry] · [Employee Count] employees

FieldDetail
Email (work)...
Email (personal)... (if revealed)
Phone (direct)...
Phone (mobile)...
Phone (corporate)...
LocationCity, State, Country
LinkedInURL
Company Domain...
Company RevenueRange
Company FundingTotal raised
Company HQLocation

Step 5 — Offer Next Actions

Ask the user which action to take:

  1. Save to Apollo — Create this person as a contact via mcp__claude_ai_Apollo_MCP__apollo_contacts_create with run_dedupe: true
  2. Add to a sequence — Ask which sequence, then run the sequence-load flow
  3. Find colleagues — Search for more people at the same company using mcp__claude_ai_Apollo_MCP__apollo_mixed_people_api_search with q_organization_domains_list set to this company
  4. Find similar people — Search for people with the same title/seniority at other companies

來源與署名

來源：anthropics/knowledge-work-plugins位於partner-built/apollo/skills/enrich-lead提交ae1513e

授權條款： 無授權條款

內容歸原作者所有。SourceWeft 從公開儲存庫中收錄這些內容。

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