A lead-finding workflow that turns an ideal customer profile, or ICP, into a ranked list of potential decision-makers. The list can include their work email addresses and phone numbers.

Use it to find sales prospects by role, industry, company size, location, seniority, or technology use.

It removes the need to manually search for companies and the right people using separate filters and tools.

prospect — Skill for Claude Code and Codex by apolloio: A lead-finding workflow that turns an ideal customer profile…

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods

npx agentmods add skills/apolloio/apollo-mcp-plugin/prospect

Any agent

npx skills add apolloio/apollo-mcp-plugin --skill prospect

Clone the repo

git clone --depth 1 https://github.com/apolloio/apollo-mcp-plugin

Made for: Claude Code, Codex.

Or install apollo-mcp-plugin, the plugin that ships this one along with the rest of its 4 skills, 2 MCP servers.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/apolloio/apollo-mcp-plugin/prospect.svg)](https://agentmods.dev/skills/apolloio/apollo-mcp-plugin/prospect)

Your own site

<a href="https://agentmods.dev/skills/apolloio/apollo-mcp-plugin/prospect"><img src="https://agentmods.dev/badge/skills/apolloio/apollo-mcp-plugin/prospect.svg" alt="Measured on agentmods" height="20"></a>

Per session 33 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 897 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. Scan, not verified.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5	$0.00033	$0.00897
Opus 5	$0.00016	$0.00449
Sonnet 5	$0.00007	$0.00179
Haiku 4.5	$0.00003	$0.00090

Measured 4d ago against content hash 772a3a115a92, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

prospect scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/prospect/SKILL.md · 91 lines

How it starts

The opening of the file, as written. The whole thing — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

Read the full file on GitHub · 91 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

4d ago First seen · 91 lines · 33 tokens per session scan A 772a3a115a92

Subscribe to this mod's changes

prospect is a skill published in the GitHub repository apolloio/apollo-mcp-plugin (20 stars, last pushed 1mo ago), licensed MIT. It adds 33 tokens to every session and 897 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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