Prospect

This skill helps users generate a ranked and enriched lead list from an ideal customer profile (ICP) description.

Install

npx promptshop add prospect

Details

What This Skill Does

This skill helps users generate a ranked and enriched lead list from an ideal customer profile (I

CP) description. It's designed for sales and marketing teams looking to quickly identify and prioritize potential customers based on specific criteria. The skill automates the process of finding relevant companies and decision-makers.

When to Use

Identify VP of Engineering leads at Series B+ SaaS firms.
Find heads of marketing at e-commerce companies in Europe.
Generate a list of C.
TOs at fintech startups in New York.
Locate procurement managers at large manufacturing companies.
Discover S.

DR leaders at companies using Salesforce and Outreach.

Key Features

Parses natural language I

CP descriptions into structured filters.
Searches for companies based on industry, size, and location.
Enriches company data to reveal revenue, funding, and headcount.
Finds decision-makers based on job titles and seniority levels.
Enriches lead data to provide comprehensive contact information.

Manual Installation

Manual installation

View Full Skill Content

The complete markdown content that gets installed

Prospect

Go from an I

CP description to a ranked, enriched lead list in one shot. The user describes their ideal customer via "$A

RGUMENTS".

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 C

TOs at fintech startups, 50-500 employees, New York /apollo:prospect procurement managers at manufacturing companies with 1000+ employees /apollo:prospect S

DR leaders at companies using Salesforce and Outreach

Step 1 — Parse the I

Extract structured filters from the natural language description in "$A

RGUMENTS":

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 I

CP 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 I

CP 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: [I

CP Summary]

CP Fit | |---|---|---|---|---|---|---|---|---|

CP 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 C

SV-style table for easy copy-paste

Prospect

Go from an I

CP description to a ranked, enriched lead list in one shot. The user describes their ideal customer via "$A

RGUMENTS".

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 C

TOs at fintech startups, 50-500 employees, New York /apollo:prospect procurement managers at manufacturing companies with 1000+ employees /apollo:prospect S

DR leaders at companies using Salesforce and Outreach

Step 2 — Search for Companies

Use mcp__claude_ai_

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 I

CP 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: [I

CP Summary]

CP Fit | |---|---|---|---|---|---|---|---|---|

CP 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.