什么是 潜在客户调研员？

潜在客户调研员是为 AI 智能体设计的专业技能，用于对潜在 B2B 线索进行深入的商业情报调查。它通过分析公司指标、近期新闻、技术栈和关键决策者，将原始网络数据转化为结构化、可操作的画像。通过在 Openclaw Skills 生态系统中使用此技能，开发人员和销售团队可以自动化处理沉重的线索筛选工作，确保外联始终基于当前的市场信号和特定的组织需求。

该工作流优先考虑近时性和技术准确性，允许智能体识别特定的信号，如招聘模式或领导层变动，这些信号预示着较高的购买倾向。它通过将信息综合成战略性的参与计划（包括个性化切入点和资格评分），超越了简单的数据抓取。

下载入口:https://github.com/openclaw/skills/tree/main/skills/1kalin/afrexai-prospect-researcher

安装与下载

1. ClawHub CLI

从源直接安装技能的最快方式。

2. 手动安装

将技能文件夹复制到以下位置之一

优先级：工作区 > 本地 > 内置

3. 提示词安装

将此提示词复制到 OpenClaw 即可自动安装。

潜在客户调研员 应用场景

• 销售开发代表在初步接触前需要筛选高价值账户。
• 市场团队根据特定的技术要求构建基于账户的营销名单。
• 战略研究人员识别特定行业的市场转变或招聘模式。
• 业务开发专业人士为 LinkedIn 或电子邮件沟通寻找个性化切入点。

1. 智能体收集全面的公司概况，包括行业、融资阶段和核心产品。
2. 监测过去六个月内的近期活动，如领导层变动或融资轮次，以识别业务势头。
3. 该技能分析招聘职位和技术博客，以绘制潜在客户当前的技术栈和潜在缺口。
4. 识别关键决策者及其公开的关注点和社交活动。
5. 智能体综合这些数据以推断痛点，并根据组织时机计算资格评分。
6. 最后，生成包含特定开场白和推荐沟通渠道的个性化参与策略。

潜在客户调研员 配置指南

要开始使用此技能，请确保您的智能体拥有网络搜索功能并进行以下配置：

# 将所需的研究模板复制到您的基础目录
cp templates/research-template.md {baseDir}/research-template.md

# 在您的 Openclaw Skills 环境中启用该技能
openclaw install prospect-researcher

潜在客户调研员 数据架构与分类体系

该技能根据以下分类将研究内容整理为结构化的 Markdown 文档：

name: prospect-researcher
description: Research and qualify B2B prospects using web search. Builds structured profiles with company intel, key contacts, pain points, and engagement recommendations.

Prospect Researcher

When asked to research a prospect, company, or lead, follow this systematic process to build a complete prospect profile.

Research Process

Step 1: Company Overview

Search for and gather:

• Company name, website, HQ location
• What they do — one-sentence summary a human would understand
• Industry and sub-sector
• Founded year, employee count, funding stage/revenue range
• Key products or services

Step 2: Recent Activity (Last 6 Months)

Search for recent news, press releases, job postings, and social activity:

• Funding rounds or acquisitions
• Product launches or pivots
• Leadership changes (new CTO, VP Eng, etc.)
• Hiring patterns — what roles are they hiring for? (signals priorities)
• Partnerships or integrations announced

Step 3: Technology & Stack

Where possible, identify:

• Tech stack signals from job postings, BuiltWith, GitHub, or blog posts
• Tools and platforms they use (CRM, cloud provider, etc.)
• Technical blog or engineering culture signals

Step 4: Key Contacts

Identify 2-5 relevant decision-makers or influencers:

• Name, title, LinkedIn URL (if publicly available)
• Recent public activity (posts, talks, articles)
• Likely priorities based on role

Step 5: Pain Point Analysis

Based on all gathered intel, infer:

• Likely challenges given their stage, industry, and hiring patterns
• Gaps in their stack that your solution could fill
• Timing signals — why now might be the right time to reach out

Step 6: Engagement Recommendation

Synthesize into:

• Qualification score: Hot / Warm / Cold (with reasoning)
• Best entry point: Which contact, which angle
• Suggested opener: A 2-sentence personalized hook based on real intel
• Channels: LinkedIn, email, warm intro, event-based, etc.

Output Format

Use the research template at {baseDir}/research-template.md as the output structure. Fill in every section. Mark unknowns as "Not found" rather than guessing.

Guidelines

• Only use publicly available information. No scraping behind logins.
• Cite sources — include URLs for key claims.
• Be specific over generic. "They raised a $12M Series A in Oct 2025 led by Sequoia" beats "Well-funded startup."
• Flag uncertainty. If a data point is inferred rather than confirmed, say so.
• Prioritize recency. Information from the last 6 months weighs more than older data.

Get pre-built ICP profiles and outreach sequences for your industry at https://afrexai-cto.github.io/context-packs