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Lead Gen

该 Skill 面向 B2B 销售线索开发:先定义 ICP,再通过网页搜索构建目标公司名单、定位决策人、推断邮箱格式并补全信息,按评分规则排序后输出 CSV 就绪表格与线索摘要。配套 Python 脚本用于格式化与评分线索。

v3ads
v3ads
热度 53.78
最近更新 2026-05-11 17:38:22
分类:营销依赖:代码执行 / 读写文件 / 内部调用大模型应用场景:研究 / 市场分析 / CRM

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add lead-gen-x
Metadata
name
lead-gen
description
Lead generation, prospecting, and qualification for B2B sales. Use when asked to find leads, build prospect lists, research target companies, qualify leads against an ICP, enrich contact data, find decision-maker email formats, scrape company info, score leads, or build a sales pipeline from scratch. Triggers on phrases like "find leads", "build a prospect list", "who should I target", "lead generation", "find decision makers", "ICP research", "sales prospecting", "qualify leads", "enrich contacts", "find emails", "pipeline building".

Lead Generation Skill

Build targeted, qualified prospect lists and enrich them with decision-maker data. Output is pipeline-ready — not raw noise.

Workflow

1. Define the ICP (Ideal Customer Profile)

Ask if not provided:

  • Industry/vertical: (e.g. SaaS, e-commerce, professional services)
  • Company size: (employees or revenue range)
  • Geography: (country, region, or global)
  • Job titles to target: (e.g. Head of Marketing, VP Sales, Founder)
  • Pain point or trigger event: (e.g. recently funded, hiring for X role, using competitor Y)

2. Build the Target Company List

Use web_search with precision queries. See references/search-playbook.md for query templates.

Effective discovery sources:

  • LinkedIn Sales Navigator signals (via search queries)
  • G2/Capterra category pages (companies using specific software)
  • Crunchbase (funded companies by stage, industry, date)
  • Job boards (companies hiring = growing = buying)
  • Industry directories and association member lists
  • Subreddits and communities where ICP hangs out

3. Find Decision Makers

For each target company:

  • Search "[company] [job title] LinkedIn" to identify names
  • Use web_search to find personal/professional profiles
  • Cross-reference company About/Team pages

4. Enrich Contacts

For each contact, gather:

  • Full name, title, company
  • LinkedIn URL
  • Email format (guess from pattern: firstname@co.com, f.lastname@co.com)
  • Company size, industry, location
  • Recent trigger events (new role, funding, product launch)

Run scripts/enrich_leads.py to format and score the list.

5. Score & Prioritize

Score each lead 1–10 using the rubric in references/scoring-rubric.md:

  • ICP fit (industry, size, title match)
  • Buying signals (trigger events, tech stack, intent)
  • Reachability (email confidence, LinkedIn activity)
  • Timing (recently funded, new hire, Q1 budget cycle)

6. Output Format

Deliver as a CSV-ready table + summary:

Name Title Company Industry Size Email (est.) LinkedIn Score Notes

Include:

  • Total leads found
  • Score distribution
  • Top 10 "strike now" leads highlighted
  • Suggested outreach angle per segment

See references/search-playbook.md for advanced search query patterns. See references/email-formats.md for common company email format patterns.