enrich-lead
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npx mdskill add anthropics/knowledge-work-plugins/enrich-leadConvert any identifier into a complete contact dossier instantly.
- Generates full contact cards with emails, phones, titles, and company data.
- Depends on Apollo MCP tools for people matching and personal email reveals.
- Uses name, company, LinkedIn, or email as input to identify the target.
- Delivers enriched profiles directly after consuming one Apollo credit.
SKILL.md
.github/skills/enrich-leadView on GitHub ↗
--- 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_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
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