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]

Name

Title

Company

Employees

Revenue

Email

Phone

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