enrich-lead

GitHub

根据姓名、公司或链接等线索,自动检索并生成包含联系方式及企业详情的完整联系人档案。支持模糊搜索匹配、个人与企业信息增强,并提供保存、加入序列或查找同事等后续操作建议。

partner-built/apollo/skills/enrich-lead/SKILL.md anthropics/knowledge-work-plugins

Trigger Scenarios

提供人员姓名或职位以获取联系方式 输入公司域名或LinkedIn URL进行背景调查 需要批量获取潜在客户的企业情报

Install

npx skills add anthropics/knowledge-work-plugins --skill enrich-lead -g -y
More Options

Non-standard path

npx skills add https://github.com/anthropics/knowledge-work-plugins/tree/main/partner-built/apollo/skills/enrich-lead -g -y

Use without installing

npx skills use anthropics/knowledge-work-plugins@enrich-lead

指定 Agent (Claude Code)

npx skills add anthropics/knowledge-work-plugins --skill enrich-lead -a claude-code -g -y

安装 repo 全部 skill

npx skills add anthropics/knowledge-work-plugins --all -g -y

预览 repo 内 skill

npx skills add anthropics/knowledge-work-plugins --list

SKILL.md

Frontmatter
{
    "name": "enrich-lead",
    "description": "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.",
    "argument-hint": "[name, company, LinkedIn URL, or email]",
    "user-invocable": true
}

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 sarah@stripe.com
  • /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

Field Detail
Email (work) ...
Email (personal) ... (if revealed)
Phone (direct) ...
Phone (mobile) ...
Phone (corporate) ...
Location City, State, Country
LinkedIn URL
Company Domain ...
Company Revenue Range
Company Funding Total raised
Company HQ Location

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

Version History

  • 6f13415 Current 2026-07-05 18:30

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