Prospect apollo

Skill w95/awesome-claude-corporate-skills/05-sales/prospect-apollo

166 production-ready Claude AI skills organized by corporate role — executive leadership, finance, HR, marketing, sales, legal, operations, engineering, product, data, customer success, procurement & document processing

Install

npx -y skills add w95/awesome-claude-corporate-skills --skill prospect-apollo

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What its author says it does

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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.

SKILL.md

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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