Prospect
- 261 installs
- 18 repo stars
- Updated July 20, 2026
- apolloio/apollo-mcp-plugin
This is a copy of prospect by anthropics - installs and ranking accrue to the original listing.
apollo-prospect is a Claude skill that turns a plain-English ICP description into a ranked table of enriched decision-maker leads with emails and phone numbers via the Apollo MCP.
About
This skill goes from an ideal-customer-profile description in plain English to a ranked, enriched lead list in one flow. A developer or rep describes their ICP and it searches companies, enriches the top ones, finds decision makers, bulk-enriches leads with emails and phones, and presents a ranked table with ICP-fit scoring, all through the Apollo MCP. It warns on credit cost before enrichment.
- Turns a plain-English ICP into a ranked table of enriched decision-maker leads
- Runs the full company-search to people-search to bulk-enrich pipeline via the Apollo MCP
- Scores each lead's ICP fit and warns on credit consumption before enriching
Prospect by the numbers
- 261 all-time installs (skills.sh)
- Data as of Jul 28, 2026 (Skillselion catalog sync)
prospect capabilities & compatibility
Requires an Apollo.io account; company and lead enrichment consume Apollo credits, disclosed before each enrich step.
- Capabilities
- prospecting · lead enrichment · lead generation
- Use cases
- marketing · email · research
- Runs
- Local or remote
- Pricing
- Bring your own API key
What prospect says it does
Go from an ICP description to a ranked, enriched lead list in one shot.
**Credit warning**: Tell the user exactly how many credits will be consumed before proceeding.
npx skills add https://github.com/apolloio/apollo-mcp-plugin --skill prospectAdd your badge
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| Installs | 261 |
|---|---|
| repo stars | ★ 18 |
| Last updated | July 20, 2026 |
| Repository | apolloio/apollo-mcp-plugin ↗ |
What it does
Turn a plain-English ICP into a ranked, enriched Apollo lead list with emails and phones.
Who is it for?
Generating a ranked, enriched lead list from an ICP description in one shot.
Skip if: Enriching a single known identifier (use the enrich-lead skill).
When should I use this skill?
The user describes an ideal customer profile and wants a ranked list of enriched leads.
What you get
A ranked table of enriched leads with emails, phones, and ICP-fit scores plus a credit summary.
- Ranked table of enriched decision-maker leads with emails and phones
By the numbers
- 7-step pipeline
- top 10 companies enriched
- up to 10 leads bulk-matched per call
Files
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_tagsfor industry/verticalorganization_num_employees_rangesfor sizeorganization_locationsfor geography- Set
per_pageto 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_titlesandperson_senioritiesfrom the ICPq_organization_domains_listscoped to the enriched company domainsper_pageset 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,domainfor each personreveal_personal_emailsset totrue
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 | 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
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What input does it need?
A plain-English ICP; at minimum a title or role plus an industry or company size. If vague, it asks 1-2 clarifying questions first.
How is lead fit scored?
Each lead gets an ICP Fit of Strong, Good, or Partial based on how many of title, seniority, company size, and industry match.