Apollo

Skill 90 of 200

Prospect

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.

1 minute · 324 words · 9 sections

Install

$ npx skills add anthropics/knowledge-work-plugins --skill prospect

$ npx skills add anthropics/knowledge-work-plugins

» /plugin marketplace add anthropics/knowledge-work-plugins

The first command installs just this skill, by the name in its SKILL.md; the second installs the whole repository.

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:

Save all to Apollo — Bulk-create contacts via mcp__claude_ai_Apollo_MCP__apollo_contacts_create with run_dedupe: true for each lead
Load into a sequence — Ask which sequence and run the sequence-load flow for these contacts
Deep-dive a company — Run /apollo:company-intel on any company from the list
Refine the search — Adjust filters and re-run
Export — Format leads as a CSV-style table for easy copy-paste

Colophon for this issue

Colophon

How this issue was made

Source

github.com/anthropics/knowledge-work-plugins, branch main, last pushed 23 September 2026.

Licence

Apache-2.0 — the text of every skill is reproduced unmodified, frontmatter included, under the upstream licence.

Discovery

200 skills found by walking the repository tree for SKILL.md, not by matching a directory convention. 27 distinct layouts observed: bio-research/skills/*/SKILL.md, cowork-plugin-management/skills/*/SKILL.md, customer-support/skills/*/SKILL.md, data/skills/*/SKILL.md, design/skills/*/SKILL.md, engineering/skills/*/SKILL.md, enterprise-search/skills/*/SKILL.md, finance/skills/*/SKILL.md, human-resources/skills/*/SKILL.md, legal/skills/*/SKILL.md, marketing/skills/*/SKILL.md, operations/skills/*/SKILL.md, partner-built/apollo/skills/*/SKILL.md, partner-built/brand-voice/skills/*/SKILL.md, partner-built/common-room/skills/*/SKILL.md, partner-built/slack/skills/*/SKILL.md, partner-built/zoom-plugin/skills/*/SKILL.md, partner-built/zoom-plugin/skills/contact-center/*/SKILL.md, partner-built/zoom-plugin/skills/meeting-sdk/*/SKILL.md, partner-built/zoom-plugin/skills/meeting-sdk/web/*/SKILL.md, partner-built/zoom-plugin/skills/video-sdk/*/SKILL.md, partner-built/zoom-plugin/skills/virtual-agent/*/SKILL.md, partner-built/zoom-plugin/skills/zoom-mcp/*/SKILL.md, pdf-viewer/skills/*/SKILL.md, product-management/skills/*/SKILL.md, productivity/skills/*/SKILL.md, sales/skills/*/SKILL.md.

Issue colours

Resolved from the awesome-design-md registry — hue 39°, chroma 0.113. Two accent tones are generated per issue and each is proven against its own ground before it ships: a single accent that passes AA on both light and dark paper is arithmetically impossible.

Typefaces

Display: Copernicus is not available to us; set in Newsreader.

We never load a typeface at request time from a third-party origin, so a brand face we do not already self-host is substituted rather than fetched.

Heading repairs

1 repair applied to this skill so the document has one h1 and no skipped levels:

Removed “Prospect”, a leading h1 that duplicated the skill title.

Spec compliance

53 editorial notes across 41 of 200 skills. They are printed in the margin of each skill rather than as errors here.

and 31 more.

Images

Images inside a skill come from the upstream repository. Where the author gave no alternative text we mark the image decorative rather than inventing a description — a plausible caption we made up is worse than none for the reader who depends on it.

Marketplace

A plugin manifest is published at .claude-plugin/marketplace.json by Anthropic, declaring 120 plugins. It is read for editorial metadata only — never as the skill index, which is always the repository tree.

Signal

Install counts come from skills.sh. They measure downloads, not quality, and an unranked repository is not an unread one.

Agent surfaces

The whole issue is available as one markdown document at /anthropics/knowledge-work-plugins.md, and each skill at its own .md URL.

Publication

Set by Skills Docs from the source repository. Body text is Literata at the reader’s chosen size and measure; code is Geist Mono. Nothing on this page was written by us except this paragraph.

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:

Save all to Apollo — Bulk-create contacts via mcp__claude_ai_Apollo_MCP__apollo_contacts_create with run_dedupe: true for each lead
Load into a sequence — Ask which sequence and run the sequence-load flow for these contacts
Deep-dive a company — Run /apollo:company-intel on any company from the list
Refine the search — Adjust filters and re-run
Export — Format leads as a CSV-style table for easy copy-paste