From apollo
Generates ranked tables of enriched decision-maker leads (emails/phones) from ICP descriptions using Apollo company/people searches and bulk enrichment.
npx claudepluginhub anthropics/knowledge-work-plugins --plugin apolloThis skill uses the workspace's default tool permissions.
Go from an ICP description to a ranked, enriched lead list in one shot. The user describes their ideal customer via "$ARGUMENTS".
Researches B2B leads and decision makers by role, company, location; enriches organizations by domain and people by email using Apollo.io API.
Finds, enriches, and qualifies companies and people matching ICP criteria from playbooks, segments, and personas. Outputs prospect lists with fit reasoning and sales approaches.
Builds targeted company and contact lists using Common Room Prospector for net-new prospects or existing accounts with signals. Clarifies object types and refines iteratively.
Share bugs, ideas, or general feedback.
Go from an ICP description to a ranked, enriched lead list in one shot. The user describes their ideal customer via "$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 list