prospect

Name: prospect
Availability: InStock
Author: sun2443

Generate ranked, enriched decision-maker leads from natural-language ICP descriptions.

Updated Apr 8, 2026

Search Tags:#lead generation #sales outreach #enrichment #apollo #icp #decision-makers #phone emails

Ready to run

npx skills add https://github.com/sun2443/designer-skills --skill prospect-sun2443

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: prospect
Source: https://github.com/sun2443/designer-skills/tree/main/partner-built/apollo/skills/prospect
Command: npx skills add https://github.com/sun2443/designer-skills --skill prospect-sun2443

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Prospecting breaks down when you have an ICP in plain English but need a prioritized list of decision-makers with reliable contact details ready for outreach.

Core Features & Use Cases

ICP-to-filters parsing: Converts your natural-language ideal customer description into structured company and person filters (titles, seniority, locations, domains, employee ranges).
Company discovery and enrichment: Searches for matching companies, then enriches the top companies with firmographic data like revenue, funding, and headcount to support better ranking.
Decision-maker identification and lead enrichment: Finds likely decision-makers at enriched domains and enriches top leads with personal email and phone number, then scores ICP fit.
Use case: You can generate a ranked outreach-ready lead list from a description like “VP of Engineering at Series B+ SaaS companies in the US, 200-1000 employees,” then export or load results into Apollo.

Quick Start

Run /apollo:prospect and describe your ideal customer in plain English to get a ranked table of enriched decision-maker leads with emails and phone numbers.

Frequently Asked Questions about prospect

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I generate enriched leads from a natural language ICP description?▼

To generate enriched leads from a natural language ICP, this Skill parses your plain English description into structured company and person filters, searches for matching companies, and locates decision-makers to produce a ranked lead list. It outputs up to 10 matched leads with personal emails and phone numbers per call.

Can I use Apollo to find phone numbers and personal emails for decision-makers?▼

Yes, you can find phone numbers and personal emails for decision-makers by running the prospect command. It identifies likely decision-makers at enriched company domains and performs bulk person matching to reveal contact details, scoring each lead's ICP fit for your sales outreach.

What is the best way to rank sales prospects by firmographic data?▼

The best way to rank sales prospects by firmographic data is to enrich top company domains with metrics like revenue, funding, and headcount. This Skill uses that enriched firmographic data alongside your parsed ICP constraints to calculate a scored ICP-fit table for prioritized outreach.

Does lead generation with this approach require MCP calls for company search?▼

Yes, lead generation with this approach requires MCP calls for company search, bulk company enrichment, people search, and bulk person matching. These MCP connections are necessary to execute the end-to-end workflow of parsing filters, discovering companies, and revealing contact details.

How many enriched decision-maker leads can I match per call?▼

You can match up to 10 enriched decision-maker leads per call. The Skill limits bulk person matching to 10 leads per execution to locate decision-makers, reveal personal emails and phone numbers, and present a scored ICP-fit table efficiently.