Prospect

1---2name: prospect3description: "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."4user-invocable: true5argument-hint: "[describe your ideal customer]"6---7 8# Prospect9 10Go from an ICP description to a ranked, enriched lead list in one shot. The user describes their ideal customer via "$ARGUMENTS".11 12## Examples13 14- `/apollo:prospect VP of Engineering at Series B+ SaaS companies in the US, 200-1000 employees`15- `/apollo:prospect heads of marketing at e-commerce companies in Europe`16- `/apollo:prospect CTOs at fintech startups, 50-500 employees, New York`17- `/apollo:prospect procurement managers at manufacturing companies with 1000+ employees`18- `/apollo:prospect SDR leaders at companies using Salesforce and Outreach`19 20## Step 1 — Parse the ICP21 22Extract structured filters from the natural language description in "$ARGUMENTS":23 24**Company filters:**25- Industry/vertical keywords → `q_organization_keyword_tags`26- Employee count ranges → `organization_num_employees_ranges`27- Company locations → `organization_locations`28- Specific domains → `q_organization_domains_list`29 30**Person filters:**31- Job titles → `person_titles`32- Seniority levels → `person_seniorities`33- Person locations → `person_locations`34 35If the ICP is vague, ask 1-2 clarifying questions before proceeding. At minimum, you need a title/role and an industry or company size.36 37## Step 2 — Search for Companies38 39Use `mcp__claude_ai_Apollo_MCP__apollo_mixed_companies_search` with the company filters:40- `q_organization_keyword_tags` for industry/vertical41- `organization_num_employees_ranges` for size42- `organization_locations` for geography43- Set `per_page` to 2544 45## Step 3 — Enrich Top Companies46 47Use `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.48 49## Step 4 — Find Decision Makers50 51Use `mcp__claude_ai_Apollo_MCP__apollo_mixed_people_api_search` with:52- `person_titles` and `person_seniorities` from the ICP53- `q_organization_domains_list` scoped to the enriched company domains54- `per_page` set to 2555 56## Step 5 — Enrich Top Leads57 58> **Credit warning**: Tell the user exactly how many credits will be consumed before proceeding.59 60Use `mcp__claude_ai_Apollo_MCP__apollo_people_bulk_match` to enrich up to 10 leads per call with:61- `first_name`, `last_name`, `domain` for each person62- `reveal_personal_emails` set to `true`63 64If more than 10 leads, batch into multiple calls.65 66## Step 6 — Present the Lead Table67 68Show results in a ranked table:69 70### Leads matching: [ICP Summary]71 72| # | Name | Title | Company | Employees | Revenue | Email | Phone | ICP Fit |73|---|---|---|---|---|---|---|---|---|74 75**ICP Fit** scoring:76- **Strong** — title, seniority, company size, and industry all match77- **Good** — 3 of 4 criteria match78- **Partial** — 2 of 4 criteria match79 80**Summary**: Found X leads across Y companies. Z credits consumed.81 82## Step 7 — Offer Next Actions83 84Ask the user:85 861. **Save all to Apollo** — Bulk-create contacts via `mcp__claude_ai_Apollo_MCP__apollo_contacts_create` with `run_dedupe: true` for each lead872. **Load into a sequence** — Ask which sequence and run the sequence-load flow for these contacts883. **Deep-dive a company** — Run `/apollo:company-intel` on any company from the list894. **Refine the search** — Adjust filters and re-run905. **Export** — Format leads as a CSV-style table for easy copy-paste