Knowledge-work-plugins 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.

install

source · Clone the upstream repo

git clone https://github.com/anthropics/knowledge-work-plugins

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/anthropics/knowledge-work-plugins "$T" && mkdir -p ~/.claude/skills && cp -r "$T/partner-built/apollo/skills/prospect" ~/.claude/skills/anthropics-knowledge-work-plugins-prospect && rm -rf "$T"

manifest: partner-built/apollo/skills/prospect/SKILL.md

source content

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]

#	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