
enrich-lead
✓ Official★ 22,300by anthropic · part of anthropics/knowledge-work-plugins
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.
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.
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This is the exact playbook injected into your agent when the skill activates — shown here so you can audit it before installing. You don't need to read it to use the skill.
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." user-invocable: true argument-hint: "[name, company, LinkedIn URL, or email]"
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_nameif name is knowndomainororganization_nameif company is knownlinkedin_urlif LinkedIn is providedemailif email is provided- Set
reveal_personal_emailstotrue
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 |
| 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:
- Save to Apollo — Create this person as a contact via
mcp__claude_ai_Apollo_MCP__apollo_contacts_createwithrun_dedupe: true - Add to a sequence — Ask which sequence, then run the sequence-load flow
- Find colleagues — Search for more people at the same company using
mcp__claude_ai_Apollo_MCP__apollo_mixed_people_api_searchwithq_organization_domains_listset to this company - Find similar people — Search for people with the same title/seniority at other companies
npx skills add https://github.com/anthropics/knowledge-work-plugins --skill enrich-leadRun this in your project — your agent picks the skill up automatically.
No common issues documented yet. If you hit a problem, the repository's GitHub Issues page is the best place to look.