Lead Gen Pipeline
AI-powered lead generation pipeline. Score leads intelligently, generate personalized follow-ups, and manage your sales pipeline.
Quick Start
CODEBLOCK0
Lead Scoring
The AI scorer evaluates leads on multiple dimensions:
| Factor | Weight | Description |
|---|
| Fit | 30% | Does the lead match your ICP? (title, company size, industry) |
| Intent |
30% | Behavioral signals (page visits, downloads, email engagement) |
|
Engagement | 20% | How actively are they interacting? (recency, frequency) |
|
Source Quality | 20% | Where did they come from? (referral > webinar > cold) |
Score Ranges
- - 80-100: 🔥 Hot — reach out immediately, high buying intent
- 60-79: 🟡 Warm — nurture with targeted content, book a call
- 40-59: 🟠 Cool — add to drip sequence, monitor engagement
- 0-39: 🔵 Cold — low priority, long-term nurture only
CODEBLOCK1
Follow-Up Generation
Generate personalized follow-up messages for any pipeline stage:
CODEBLOCK2
Supported Tones
- - professional — formal business communication
- casual — friendly, conversational
- urgent — time-sensitive, action-oriented
- friendly — warm, relationship-focused
- consultative — expert advice framing
Supported Channels
- - email — full email with subject line
- sms — short, punchy (< 160 chars)
- whatsapp — conversational, emoji-friendly
- linkedin — professional networking tone
Pipeline Stages
- - initial — first contact / cold outreach
- warm — engaged but no meeting yet
- booked — meeting/demo scheduled
- post-demo — after initial call/demo
- proposal — proposal sent
- closing — negotiation / final decision
- revival — re-engaging cold/lost lead
Cold Outreach Templates
The AIDA Framework
- 1. Attention — Hook with relevant pain point
- Interest — Show you understand their world
- Desire — Paint the outcome
- Action — Clear, low-friction CTA
Outreach Sequences
Day 1: Initial value-first email
Day 3: Follow-up with case study / social proof
Day 7: Different angle (video, voice note, meme)
Day 14: Break-up email ("Should I close your file?")
Generate any of these:
CODEBLOCK3
CRM Integration Patterns
With GHL (GoHighLevel)
CODEBLOCK4
With Any CRM
The scripts output JSON — pipe into any CRM API wrapper. Lead scores include reasoning that can be stored as CRM notes.
Response Handling
When a lead replies, re-score with updated context:
CODEBLOCK5
Then generate contextual response:
CODEBLOCK6
Credits
Built by
M. Abidi |
agxntsix.ai
YouTube |
GitHub
Part of the
AgxntSix Skill Suite for OpenClaw agents.
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潜在客户生成管道
AI驱动的潜在客户生成管道。智能评分潜在客户，生成个性化跟进，并管理您的销售管道。
快速开始
bash
export OPENROUTERAPIKEY=your-key
评分潜在客户
python3 {baseDir}/scripts/lead_scorer.py {name:Jane Smith,company:Acme Corp,title:VP Marketing,source:webinar,actions:[downloaded whitepaper,visited pricing page 3x,opened 5 emails]}
生成跟进
python3 {baseDir}/scripts/followup_generator.py {name:Jane Smith,company:Acme Corp,context:Attended our AI webinar, downloaded whitepaper,stage:warm,tone:professional}
潜在客户评分
AI评分器从多个维度评估潜在客户：
| 因素 | 权重 | 描述 |
|---|
| 匹配度 | 30% | 潜在客户是否符合您的ICP？（职位、公司规模、行业） |
| 意图 |
30% | 行为信号（页面访问、下载、邮件互动） |
|
参与度 | 20% | 他们互动的活跃程度？（近期性、频率） |
|
来源质量 | 20% | 他们来自哪里？（推荐 > 网络研讨会 > 冷接触） |
评分范围
- - 80-100: 🔥 热——立即联系，购买意向高
- 60-79: 🟡 温——通过定向内容培育，预约通话
- 40-59: 🟠 凉——加入滴灌序列，监控互动
- 0-39: 🔵 冷——低优先级，仅长期培育
bash
使用自定义ICP评分
python3 {baseDir}/scripts/lead_scorer.py {name:...,company:...,icp:{industries:[SaaS,fintech],minEmployees:50,titles:[VP,Director,C-suite]}}
跟进生成
为任何管道阶段生成个性化跟进消息：
bash
演示后的专业跟进
python3 {baseDir}/scripts/followup_generator.py {
name: Jane Smith,
company: Acme Corp,
context: Had a 30-min demo, interested in enterprise plan, concerned about onboarding time,
stage: post-demo,
tone: professional,
channel: email
}
随意的短信问候
python3 {baseDir}/scripts/followup_generator.py {
name: Mike,
context: Met at conference, exchanged cards, talked about AI automation,
stage: initial,
tone: casual,
channel: sms
}
紧急成交消息
python3 {baseDir}/scripts/followup_generator.py {
name: Sarah Johnson,
company: TechFlow,
context: Proposal sent 5 days ago, no response, deal worth $25k, quarter ending,
stage: closing,
tone: urgent,
channel: email
}
支持的语气
- - 专业 — 正式商务沟通
- 随意 — 友好、对话式
- 紧急 — 时间敏感、行动导向
- 友好 — 温暖、注重关系
- 咨询式 — 专家建议框架
支持的渠道
- - email — 带主题行的完整邮件
- sms — 简短有力（< 160字符）
- whatsapp — 对话式、支持表情符号
- linkedin — 专业社交语气
管道阶段
- - 初始 — 首次联系/冷接触
- 温 — 已互动但尚未会面
- 已预约 — 已安排会议/演示
- 演示后 — 初次通话/演示后
- 提案 — 已发送提案
- 成交 — 谈判/最终决策
- 复活 — 重新接触冷淡/流失的潜在客户
冷接触模板
AIDA框架
- 1. 注意 — 用相关痛点吸引注意
- 兴趣 — 展示您了解他们的世界
- 渴望 — 描绘成果
- 行动 — 清晰、低门槛的行动号召
外联序列
第1天： 首次价值优先邮件
第3天： 附带案例研究/社会证明的跟进
第7天： 不同角度（视频、语音留言、表情包）
第14天： 分手邮件（我该关闭您的档案吗？）
生成其中任何一个：
bash
python3 {baseDir}/scripts/followupgenerator.py {name:...,stage:initial,sequencestep:1}
python3 {baseDir}/scripts/followupgenerator.py {name:...,stage:initial,sequencestep:4}
CRM集成模式
与GHL（GoHighLevel）
bash
1. 评分传入的潜在客户
SCORE=$(python3 {baseDir}/scripts/lead
scorer.py {name:...,source:facebookad})
2. 在GHL中创建联系人并添加评分标签
python3 ../ghl-crm/{baseDir}/scripts/ghl_api.py contacts create {firstName:...,tags:[score-85,hot-lead]}
3. 添加到适当的管道阶段
python3 ../ghl-crm/{baseDir}/scripts/ghl_api.py opportunities create {pipelineId:...,stageId:hot-stage-id,contactId:...}
4. 生成并发送跟进
MSG=$(python3 {baseDir}/scripts/followup_generator.py {name:...,stage:warm,channel:sms})
python3 ../ghl-crm/{baseDir}/scripts/ghl_api.py conversations send-sms
$MSG
与任何CRM
脚本输出JSON——可导入任何CRM API包装器。潜在客户评分包含可存储为CRM备注的推理过程。
回复处理
当潜在客户回复时，使用更新后的上下文重新评分：
bash
python3 {baseDir}/scripts/lead_scorer.py {name:Jane,company:Acme,actions:[replied to email,asked about pricing,requested demo]}
然后生成上下文相关回复：
bash
python3 {baseDir}/scripts/followup_generator.py {name:Jane,context:She asked about pricing and wants a demo,stage:warm,tone:professional}
致谢
由 M. Abidi 构建 | agxntsix.ai
YouTube | GitHub
属于OpenClaw代理的 AgxntSix技能套件 的一部分。
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