prospect

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🇺🇸

Original

English

🇨🇳

Translation

Chinese

Prospect

潜在客户生成

Go from an ICP description to a ranked, enriched lead list in one shot. The user describes their ideal customer via "$ARGUMENTS".

只需一步即可从ICP描述生成排序后的信息补全版线索列表。用户通过

$ARGUMENTS

描述理想客户。

Examples

示例

/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

/apollo:prospect 美国B轮及以上融资SaaS公司的工程副总裁，员工规模200-1000人

/apollo:prospect 欧洲电商公司的营销负责人

/apollo:prospect 纽约地区员工规模50-500人的金融科技初创公司CTO

/apollo:prospect 员工规模1000人以上制造企业的采购经理

/apollo:prospect 使用Salesforce和Outreach的公司的SDR负责人

Step 1 — Parse the ICP

步骤1 — 解析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.

从

$ARGUMENTS

中的自然语言描述中提取结构化筛选条件：

公司筛选条件：

行业/垂直领域关键词 →
```
q_organization_keyword_tags
```
员工规模范围 →
```
organization_num_employees_ranges
```
公司所在地 →
```
organization_locations
```
指定域名 →
```
q_organization_domains_list
```

人员筛选条件：

职位头衔 →
```
person_titles
```
职级 →
```
person_seniorities
```
人员所在地 →
```
person_locations
```

如果ICP描述模糊，在执行前提出1-2个澄清问题。至少需要明确职位头衔/角色以及行业或公司规模。

Step 2 — Search for Companies

步骤2 — 搜索目标公司

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

使用

mcp__claude_ai_Apollo_MCP__apollo_mixed_companies_search

接口，传入以下公司筛选条件：

```
q_organization_keyword_tags
```
：行业/垂直领域
```
organization_num_employees_ranges
```
：公司规模
```
organization_locations
```
：地域
设置
```
per_page
```
为25

Step 3 — Enrich Top Companies

步骤3 — 补全头部公司信息

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.

使用

mcp__claude_ai_Apollo_MCP__apollo_organizations_bulk_enrich

接口，传入前10条结果中的域名。此操作将获取收入、融资、员工数量和企业属性数据，用于公司排序。

Step 4 — Find Decision Makers

步骤4 — 查找决策者

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

使用

mcp__claude_ai_Apollo_MCP__apollo_mixed_people_api_search

接口，传入：

ICP中的
```
person_titles
```
和
```
person_seniorities
```
限定为已补全信息的公司域名的
```
q_organization_domains_list
```
设置
```
per_page
```
为25

Step 5 — Enrich Top Leads

步骤5 — 补全头部线索信息

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.

点数消耗提醒：在执行前明确告知用户将消耗的点数数量。

使用

mcp__claude_ai_Apollo_MCP__apollo_people_bulk_match

接口，每次最多补全10条线索，传入：

每位人员的
```
first_name
```
、
```
last_name
```
、
```
domain
```
设置
```
reveal_personal_emails
```
为
```
true
```

如果线索数量超过10条，分批次调用接口。

Step 6 — Present the Lead Table

步骤6 — 展示线索表格

Show results in a ranked table:

以排序表格形式展示结果：

Leads matching: [ICP Summary]

匹配线索：[ICP摘要]

#	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.

#	姓名	职位	公司	员工数量	收入	邮箱	电话	ICP匹配度

ICP匹配度评分规则：

高匹配：职位头衔、职级、公司规模、行业全部符合
良好匹配：4项标准中符合3项
部分匹配：4项标准中符合2项

总结：在Y家公司中找到X条线索，共消耗Z点数。

Step 7 — Offer Next Actions

步骤7 — 提供后续操作选项

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

询问用户：

保存至Apollo — 通过
```
mcp__claude_ai_Apollo_MCP__apollo_contacts_create
```
接口批量创建联系人，为每条线索设置
```
run_dedupe: true
```
导入至序列 — 询问用户导入到哪个序列，然后执行对应线索导入流程
深度分析某家公司 — 对列表中的任意公司执行
```
/apollo:company-intel
```
指令
优化搜索条件 — 调整筛选条件后重新执行搜索
导出线索 — 将线索格式化为CSV风格表格，方便复制粘贴