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prospect

Describe your ideal customer in plain language to find matching companies and decision makers. The skill adds company details and contact information, then presents a ranked lead table showing how closely each prospect matches your criteria.

中文简介正在准备中。当前显示英语简介。

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Full ICP-to-leads pipeline. Describe your ideal customer in plain English and get a ranked table of enriched decision-maker leads with emails and phone numbers.

查看安装方法

适用场景

  • Find sales prospects by industry and company size
  • Identify decision makers with specific job titles
  • Prioritize leads by fit with your ideal customer
  • Prepare lead data for Apollo or CSV-style export

包含的文件(1)

  • SKILL.md3.5 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Prospect

Go from an ICP description to a ranked, enriched lead list in one shot. The user describes their ideal customer via "$ARGUMENTS".

Examples

  • /apollo:prospect VP of Engineering at Series B+ SaaS companies in the US, 200-1000 employees
  • /apollo:prospect heads of marketing at e-commerce companies in Europe
  • /apollo:prospect CTOs at fintech startups, 50-500 employees, New York
  • /apollo:prospect procurement managers at manufacturing companies with 1000+ employees
  • /apollo:prospect SDR leaders at companies using Salesforce and Outreach

Step 1 — Parse the ICP

Extract structured filters from the natural language description in "$ARGUMENTS":

Company filters:

  • Industry/vertical keywords → q_organization_keyword_tags
  • Employee count ranges → organization_num_employees_ranges
  • Company locations → organization_locations
  • Specific domains → q_organization_domains_list

Person filters:

  • Job titles → person_titles
  • Seniority levels → person_seniorities
  • Person locations → person_locations

If the ICP is vague, ask 1-2 clarifying questions before proceeding. At minimum, you need a title/role and an industry or company size.

Step 2 — Search for Companies

Use mcp__claude_ai_Apollo_MCP__apollo_mixed_companies_search with the company filters:

  • q_organization_keyword_tags for industry/vertical
  • organization_num_employees_ranges for size
  • organization_locations for geography
  • Set per_page to 25

Step 3 — Enrich Top Companies

Use mcp__claude_ai_Apollo_MCP__apollo_organizations_bulk_enrich with the domains from the top 10 results. This reveals revenue, funding, headcount, and firmographic data to help rank companies.

Step 4 — Find Decision Makers

Use mcp__claude_ai_Apollo_MCP__apollo_mixed_people_api_search with:

  • person_titles and person_seniorities from the ICP
  • q_organization_domains_list scoped to the enriched company domains
  • per_page set to 25

Step 5 — Enrich Top Leads

Credit warning: Tell the user exactly how many credits will be consumed before proceeding.

Use mcp__claude_ai_Apollo_MCP__apollo_people_bulk_match to enrich up to 10 leads per call with:

  • first_name, last_name, domain for each person
  • reveal_personal_emails set to true

If more than 10 leads, batch into multiple calls.

Step 6 — Present the Lead Table

Show results in a ranked table:

Leads matching: [ICP Summary]

#NameTitleCompanyEmployeesRevenueEmailPhoneICP Fit

ICP Fit scoring:

  • Strong — title, seniority, company size, and industry all match
  • Good — 3 of 4 criteria match
  • Partial — 2 of 4 criteria match

Summary: Found X leads across Y companies. Z credits consumed.

Step 7 — Offer Next Actions

Ask the user:

  1. Save all to Apollo — Bulk-create contacts via mcp__claude_ai_Apollo_MCP__apollo_contacts_create with run_dedupe: true for each lead
  2. Load into a sequence — Ask which sequence and run the sequence-load flow for these contacts
  3. Deep-dive a company — Run /apollo:company-intel on any company from the list
  4. Refine the search — Adjust filters and re-run
  5. Export — Format leads as a CSV-style table for easy copy-paste

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