Automated AI-powered lead generation and prospecting. Find ideal customers, enrich data, personalize outreach, and book meetings without manual effort. Use when generating B2B leads at scale.
Instrucciones de origen · Vista previa de solo lectura
name
ai-lead-generation
description
Automated AI-powered lead generation and prospecting. Find ideal customers, enrich data, personalize outreach, and book meetings without manual effort. Use when generating B2B leads at scale.
AI lead generation is a $500-5K/month service you can sell to B2B companies. Each booked meeting is worth $50-500 in service revenue. At 3-15% conversion from outreach to meeting, with 500 prospects/month at $0.50-5/lead cost, you generate $2.5K-25K pipeline value per month.
When Not to Use
Simple or one-off tasks — if the task is straightforward, direct execution is faster than structured methodology.
Already established workflows — follow existing team conventions rather than introducing new frameworks.
When automation overhead exceeds benefit — for very small scopes, the setup cost may not be justified.
Dependencies
Python 3.8+ or Node.js 18+
Access to relevant APIs/services for your specific use case
Basic understanding of the domain concepts
Commands
# Refer to the skill's usage section for specific commands# Adapt these to your workflow
Revenue Streams
Lead Gen Service ($2K-10K/client/month) — Run full pipeline for clients: prospect, enrich, personalize, and book meetings.
Lead Lists ($500-2K/list) — Sell pre-enriched prospect lists with verified contacts and intent data.
Done-With-You ($5K-20K/project) — Set up their outbound systems: CRM, enrichment, sequences, and warm-up.
First Action in 60 Minutes
#!/usr/bin/env bash# 60-minute lead gen setup: pick niche, install tools, generate 100 leadsmkdir -p ~/leadgen/{prospects,enriched,outreach}
echo"1. Define ICP (use ~/leadgen/icp.md template)"echo"2. Source 500 prospects via Apollo/LinkedIn Sales Nav"echo"3. Enrich with Clearbit/Clay ($100-500/mo)"echo"4. Warm up sending domain (Instantly/Smartlead $39-49/mo)"echo"5. Launch 5-touch sequence"
The AI Lead Gen Pipeline
Stage 1: Prospecting (Automated)
1. Define ICP (Ideal Customer Profile)
2. Find companies matching criteria
3. Identify decision makers
4. Gather contact info
Output: List of 500-5000 prospects
Stage 2: Enrichment (AI)
1. Add company data (size, tech, funding)
2. Add personal data (role, background)
3. Add intent signals (job changes, news)
4. Score by fit + intent
Output: Enriched lead list
Stage 3: Personalization (AI)
1. Analyze prospect's content
2. Find common ground
3. Generate personalized message
4. A/B test variations
Output: Customized outreach
Subject: Quick question about [Company]'s [Challenge]
Hi [Name],
I noticed [specific observation about their company/content].
Most [companies like theirs] struggle with [pain point].
We've helped [similar company] achieve [result].
Quick 10-minute call this week?
Best,
[Your name]
LinkedIn V1
[Name], curious about your thoughts on [topic].
Saw your post about [their content] - [insight].
We help [target companies] do [result].
Would love to hear your perspective.
Link to calendar: [calendly link]
Thanks,
[Your name]
Multi-Channel Sequence
Day 1: Email + LinkedIn request
Day 3: LinkedIn message
Day 5: Email follow-up
Day 7: Break (if no response)
Day 14: Final email + phone call
Day 21: Remove from sequence
AI Personalization
Use AI To:
Analyze prospect's recent posts
Find common connections
Identify recent company news
Generate custom hooks
Write tailored openers
Prompt Example
Analyze this prospect:
- Name: [name]
- Company: [company]
- Role: [role]
- Recent post: [post content]
Write 3 personalized openers
that reference their work.
Keep under 50 words each.
Cold Email Warm-up
Day 1-3: 5 emails
Day 1: Personal
Day 2: Personal
Day 3: Personal
Day 4-14: Add volume
Day 4: 10 emails
Day 7: 20 emails
Day 14: 50 emails
Maintain
Daily: 20-50 emails
Reply to engagement
Mark as important
Metrics & Benchmarks
Lead Gen Metrics
Metric
Benchmark
Target
Open rate
20-30%
35%+
Reply rate
3-8%
10%+
Meeting rate
1-3%
5%+
Cost per meeting
$20-50
<$30
Conversion Pipeline
Stage
Benchmark
Leads to Open
30%
Open to Reply
8%
Reply to Meeting
40%
Meeting to Close
25%
ROI Calculation
Revenue: 10 meetings x $2K deal = $20K
Cost: 1000 leads x $1 = $1,000
ROI: 1900%
Integration with 1ai-skills
Combine ai-lead-generation with related skills:
Sales Pipeline
AI Lead Gen -> Outbound -> Qualify -> Demo -> Close
Skill Synergies
Skill
Use Case
voice-ai-agent
Handle inbound calls
sales
Close deals
ai-consulting
Convert to projects
marketing
Nurture leads
Best Practices
Do's
Personalize at scale
Test different angles
Follow up consistently
Track everything
A/B test subject lines
Clean data regularly
Don'ts
Don't spam
Don't ignore unsubscribe
Don't sound salesy
Don't skip warm-up
Don't neglect deliverability
Technical Implementation
Required Tools
Web Scraping: curl, jq, BeautifulSoup (Python), Puppeteer (JS)
CRM: HubSpot API, Pipedrive, or Airtable as lightweight CRM
Email: SendGrid API, Mailgun, or AWS SES
AI/LLM: Claude API for personalization, GPT for batch processing
Storage: SQLite or PostgreSQL, pandas for analysis
Daily Pipeline (Cron)
#!/bin/bash# Run daily via cron: 0 9 * * 1-5# 1. Scan for new signals
python3 scan_signals.py --sources linkedin,crunchbase,builtwith
# 2. Score new leads
python3 score_leads.py --new-only --icp icp_v2.json
# 3. Generate outreach for A/B grade leads
python3 generate_outreach.py --min-grade B --sequence cold
# 4. Send scheduled outreach (respects rate limits)
python3 send_outreach.py --today --respect-quiet-hours
# 5. Generate daily report
python3 pipeline_report.py --period daily | mail -s "Daily Lead Gen Report" you@email.com
Error Handling
Error
Cause
Recovery
API rate limit (429)
Too many requests
Implement exponential backoff, spread requests across time
Invalid email (bounce)
Bad email from scraping
Verify with Hunter.io email verification before sending
Low open rates (<5%)
Poor subjects or spam filters
A/B test subjects, check SPF/DKIM/DMARC, warm up domain
CRM sync failure
API timeout or auth expired
Retry with backoff, refresh OAuth tokens, log failures
Scraping blocked
IP blocked
Rotate user agents, use proxy pool, respect robots.txt
Score drift
ICP changed
Re-score all leads when ICP changes, version the criteria
ICP Definition Schema
{"version":"v2","industry":["SaaS","FinTech","E-commerce"],"company_size":{"min":10,"max":500},"revenue":{"min":1000000},"roles":["CTO","VP Engineering","Head of Product"],"geography":["US","UK","EU"],"signals":{"job_posting":15,"recent_funding":20,"tech_migration":10,"social_activity":5}}
Pipeline Management SQL
# Weekly pipeline report
sqlite3 leads.db <<'SQL'
SELECT
grade,
COUNT(*) as total,
SUM(CASE WHEN stage='contacted' THEN 1 ELSE 0 END) as contacted,
SUM(CASE WHEN stage='engaged' THEN 1 ELSE 0 END) as engaged,
SUM(CASE WHEN stage='qualified' THEN 1 ELSE 0 END) as qualified,
SUM(CASE WHEN stage='proposal' THEN 1 ELSE 0 END) as proposal,
ROUND(AVG(score), 1) as avg_score
FROM leads
WHERE created_at > datetime('now', '-7 days')
GROUP BY grade
ORDER BY grade;
SQL
Anti-Rationalization Table
Excuse
Truth
"I need a perfect list first"
Start with 100 bad leads, iterate
"I'll automate later"
Manual first, automate what works
"Outbound doesn't work"
3-15% reply rate is real with personalization
Output Format
On completion: "[N] prospects sourced, [N] enriched, [N] sequence launched, $[N] pipeline value generated"
Red Flags
Lead scoring does not filter out unqualified prospects wasting sales time
Agent sources leads from low-quality or spam-heavy channels
Watch for shortcuts and skipped steps
Verification
After completing this skill, confirm:
Lead scoring filters out unqualified prospects
Lead sources are high-quality with verified contact data
