Prospect

# 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:

1. **Save all to Apollo** — Bulk-create contacts via `mcp__claude_ai_Apollo_MCP__apollo_contacts_create` with `run_dedupe: true` for each lead 2. **Load into a sequence** — Ask which sequence and run the sequence-load flow for these contacts 3. **Deep-dive a company** — Run `/apollo:company-intel` on any company from the list 4. **Refine the search** — Adjust filters and re-run 5. **Export** — Format leads as a CSV-style table for easy copy-paste