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Prospect

AnthropicOfficialCreated by Apollo.ioDistributed by Anthropic

  • Skill
  • Workflow
  • Business and productivity
  • MIT
  • No issues found
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From the author

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.
— the description by Apollo.io

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

Prospect streamlines the lead generation process by translating plain-English ideal customer profile (ICP) descriptions into structured search queries. It automates the discovery of companies and decision-makers, enriches the data with firmographic and contact details, and presents a ranked list based on ICP fit. The skill includes built-in credit management and offers follow-up actions such as saving contacts to a CRM or adding them to outreach sequences.

What it does

  • Parses natural language ICP descriptions into structured search filters.
  • Searches for companies and decision-makers matching specific criteria.
  • Enriches lead data with professional contact information and firmographics.
  • Ranks leads based on their alignment with the defined ICP.
  • Provides a structured table of results including contact details and fit scores.
  • Offers direct integration for saving leads or initiating outreach sequences.

When to use it

  • When you need to quickly build a targeted list of prospects for a new outreach campaign.
  • When you want to automate the manual process of searching and enriching lead data.
  • When you have a clear idea of your ideal customer but need to identify specific decision-makers.
  • When you need to maintain consistent lead qualification standards across your sales team.

Advantages

  • Reduces manual research time by automating the entire lead discovery pipeline.
  • Ensures consistent lead qualification through standardized ICP fit scoring.
  • Provides actionable contact information including emails and phone numbers.
  • Integrates directly with CRM and sequence management workflows.
  • Allows for rapid iteration and refinement of search criteria.

Usage examples

“VP of Engineering at Series B+ SaaS companies in the US, 200-1000 employees”
The agent parses the criteria, searches for matching US-based SaaS companies, identifies VPs of Engineering, enriches the contact data, and returns a ranked table of leads.
“CTOs at fintech startups, 50-500 employees, New York”
The agent filters for fintech companies in New York with 50-500 employees, finds CTOs, enriches their profiles, and presents a table with contact details and ICP fit scores.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/anthropics/prospect/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip prospect.zip -d .agents/skills

Documentation by Apollo.io

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]
#NameTitleCompanyEmployeesRevenueEmailPhoneICP 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

Files

SKILL.md

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]
#NameTitleCompanyEmployeesRevenueEmailPhoneICP 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

Requirements and compatibility

Written for: Claude Code, Claude apps

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Claude Code frontmatter, Needs an MCP server

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
Apollo.io
Publisher
Anthropic (Official)
Distributed by
Anthropic
Source
anthropics/knowledge-work-plugins · partner-built/apollo/skills/prospect · commit e050c2e
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from partner-built/apollo/LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Apollo.io or Anthropic.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download prospect.zip