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. Apollo owns enrichment + sequences; Lark owns where the list lives and how the team acts on it.

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. Push to Lark CRM (Base) — Land the ranked list in a Lark Base as system-of-record (P5) so the team can filter/own/track it. Dedup with lark_base_search first: it REQUIRES search_fields (the Bitable API mandates the field(s) to match, e.g. email or name) and does NOT support jq — narrow with select_fields / limit instead. Unsure of field names? Discover them via lark_api GET /open-apis/bitable/v1/apps/{base}/tables/{table}/fields. Then batch the rows via lark_base_record_upsert (base_token, table_id, one fields map per lead: name, title, company, employees, revenue, email, phone, ICP Fit) with dry_run: true; show the planned batch, then commit. No leads Base yet → scaffold with the base-deploy skill, don't hand-roll schema.
6. Post a digest card to the team — When the user wants the squad to see the batch, surface it as an interactive card (P4) instead of plain text: lark_im_card_send with a spec (header = ICP summary, one item row per top lead with an "Own" button). Validate with print_json: true, then dry_run: true, then send. For the card YAML grammar, delegate to the lark-im skill. Resolve any internal recipient with lark_contact_search first (P1).