mcpField
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enrich-lead

enrich-lead

github.com/anthropicsMarketing & growth33.6k2.9k installsUpdated 1d ago
Repository

About enrich-lead

enrich-lead is an Apollo contact and company enrichment skill for sales and prospecting workflows. It matches people from identifiers such as names, emails, LinkedIn URLs, or company details, then prepares a contact card and follow-up options.

Key features

  1. 01Extracts contact details from varied identifiers
  2. 02Matches and enriches person profiles
  3. 03Enriches company information
  4. 04Produces a contact card
  5. 05Offers save and search follow-up actions

Use cases

  1. 01Look up a contact from an email or LinkedIn URL
  2. 02Build a sales lead from a name and company
  3. 03Review background information about a contact’s company

MCP support

The skill uses Apollo MCP tools to search for and match people, enrich person and company details, and create contacts.

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/anthropics/knowledge-work-plugins --skill enrich-lead

After installing, type/enrich-leadin Claude Code to confirm it loaded.

Details

From the project README

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

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