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.
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.
Review the instructions the agent will receive before you install.
Go from an ICP description to a ranked, enriched lead list in one shot. The user describes their ideal customer via "$ARGUMENTS".
/apollo:prospect VP of Engineering at Series B+ SaaS companies in the US, 200-1000 employees/apollo:prospect heads of marketing at e-commerce companies in Europe/apollo:prospect CTOs at fintech startups, 50-500 employees, New York/apollo:prospect procurement managers at manufacturing companies with 1000+ employees/apollo:prospect SDR leaders at companies using Salesforce and OutreachExtract structured filters from the natural language description in "$ARGUMENTS":
Company filters:
q_organization_keyword_tagsorganization_num_employees_rangesorganization_locationsq_organization_domains_listPerson filters:
person_titlesperson_senioritiesperson_locationsIf the ICP is vague, ask 1-2 clarifying questions before proceeding. At minimum, you need a title/role and an industry or company size.
Use mcp__claude_ai_Apollo_MCP__apollo_mixed_companies_search with the company filters:
q_organization_keyword_tags for industry/verticalorganization_num_employees_ranges for sizeorganization_locations for geographyper_page to 25Use 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.
Use mcp__claude_ai_Apollo_MCP__apollo_mixed_people_api_search with:
person_titles and person_seniorities from the ICPq_organization_domains_list scoped to the enriched company domainsper_page set to 25Credit 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 personreveal_personal_emails set to trueIf more than 10 leads, batch into multiple calls.
Show results in a ranked table:
| # | Name | Title | Company | Employees | Revenue | Phone | ICP Fit |
|---|
ICP Fit scoring:
Summary: Found X leads across Y companies. Z credits consumed.
Ask the user:
mcp__claude_ai_Apollo_MCP__apollo_contacts_create with run_dedupe: true for each lead/apollo:company-intel on any company from the listNo reviews yet. Tell others what you thought.
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Build a single brief on an existing customer using available sales records, emails, documents, call notes, and internal chat. Understand the current situation, key contacts, recent discussions, and gaps that may need attention.
Build or refresh a strategic customer account plan from sales records, emails, and meeting transcripts. Organize goals, stakeholders, growth opportunities, risks, and dated actions into a document, then sync approved highlights to the customer relationship management system.
Research sales accounts using Common Room data to summarize company details, buying signals, ownership, and key contacts. Answer specific questions directly or prepare a broader briefing with evidence-based next steps, clearly identifying missing information.
Research prospective companies and contacts, check existing customer records, and assess fit against your ideal customer profile. Receive a sourced brief covering recent developments, likely priorities, outreach talking points, and a suggested next step.
Score and group accounts by customer fit and engagement, such as recent contact and open sales opportunities. Identify where to focus sales effort, choose next actions, and spot promising accounts with neglected outreach or missing information.