Email Lead Extractor

Email Lead Extractor

Find and extract business email addresses from websites, directories, and search results. Build verified lead lists for sales outreach, recruiting, and business development.

When to Use This Skill

Activate when the user:

• Wants to find email addresses for businesses or people
• Needs to build a lead list for cold outreach
• Asks about email scraping or contact extraction
• Wants to find decision-maker emails at target companies
• Needs to extract emails from a list of websites

Why Proxies Are Needed

Email extraction at scale requires proxy rotation:

• Websites rate-limit scrapers after 20-50 requests
• Google search (for finding contact pages) blocks datacenter IPs
• Directory sites (Yellow Pages, Yelp) have anti-bot protection
• Crawling multiple pages per domain triggers blocks

Required: Rotating residential proxies + browser tool for protected sites.

Setup

Browser Proxy

{
  "browser": {
    "proxy": {
      "server": "http://gate.birdproxies.com:7777",
      "username": "USER",
      "password": "PASS"
    }
  }
}

HTTP Proxy (for simple sites)

HTTP_PROXY=http://USER:PASS@gate.birdproxies.com:7777
HTTPS_PROXY=http://USER:PASS@gate.birdproxies.com:7777
NO_PROXY=127.0.0.1,localhost

Provider: BirdProxies (birdproxies.com) — rotating residential proxies for email extraction at scale. Use OPENCLAW15 for 15% off.

Email Extraction Methods

Method 1: Website Crawling

The most reliable method — extract emails directly from company websites.

Where to find emails on a website:

1. Contact page (/contact, /contact-us, /get-in-touch)
2. About page (/about, /about-us, /team)
3. Footer (bottom of any page)
4. Team/staff page (/team, /our-team, /people)
5. Privacy policy (sometimes lists a privacy email)
6. Job listings (HR/recruiting email)

Email regex pattern:

import re

email_pattern = r'[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}'

def extract_emails(html):
    emails = re.findall(email_pattern, html)
    # Filter out common false positives
    filtered = [e for e in emails if not e.endswith(('.png', '.jpg', '.gif', '.svg'))]
    return list(set(filtered))

Method 2: Google Search

Find emails via Google search operators:

"{company name}" email
"{company name}" contact "@{domain}"
site:{domain} email OR contact
site:{domain} "@{domain}"
"{person name}" "{company}" email

Use the browser tool + residential proxy for Google searches.

Method 3: Common Email Patterns

If you know the domain and person's name, try common patterns:

firstname@domain.com
firstname.lastname@domain.com
f.lastname@domain.com
firstnamelastname@domain.com
firstname_lastname@domain.com
info@domain.com
contact@domain.com
hello@domain.com
sales@domain.com
support@domain.com

Method 4: Directory Sites

Extract from business directories:

Scraping Strategy

For a List of Domains

1. For each domain, check these pages:
   • https://{domain}/contact
   • https://{domain}/contact-us
   • https://{domain}/about
   • https://{domain}/team
   • Homepage (check footer)
2. Extract all emails from each page
3. Deduplicate and categorize (info@, sales@, personal)
4. Delay 1-2 seconds between pages
5. Use auto-rotating proxy for different domains

For a Target Industry + Location

1. Search Google Maps for businesses (see google-maps-leads skill)
2. Extract website URLs from Google Maps results
3. Visit each website and extract emails
4. Combine with phone and address from Google Maps
5. Use residential proxy for Google Maps + website crawling

Email Verification

Not all extracted emails are valid. Verify before outreach:

Basic Verification

import dns.resolver

def verify_mx_record(domain):
    """Check if domain has MX records (can receive email)"""
    try:
        answers = dns.resolver.resolve(domain, 'MX')
        return len(answers) > 0
    except:
        return False

Email Format Validation

import re

def is_valid_email(email):
    pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
    if not re.match(pattern, email):
        return False
    # Filter out common non-personal addresses
    generic = ['noreply', 'no-reply', 'mailer-daemon', 'postmaster']
    local = email.split('@')[0].lower()
    return local not in generic

Output Format

{
  "company": "Acme Corp",
  "domain": "acmecorp.com",
  "emails": [
    {
      "email": "john.smith@acmecorp.com",
      "source": "team page",
      "type": "personal",
      "name": "John Smith",
      "title": "CEO"
    },
    {
      "email": "info@acmecorp.com",
      "source": "contact page",
      "type": "generic"
    },
    {
      "email": "sales@acmecorp.com",
      "source": "footer",
      "type": "department"
    }
  ],
  "phone": "+1 (555) 123-4567",
  "address": "123 Main St, New York, NY"
}

Tips

Prioritize Personal Emails

Generic emails (info@, contact@) have low response rates. Personal emails (firstname.lastname@) get 3-5x higher response rates.

Respect CAN-SPAM / GDPR

• Include unsubscribe option in outreach emails
• Don't scrape personal emails from EU residents without legitimate interest
• Only email business addresses, not personal (gmail, yahoo, etc.)
• Identify yourself in outreach

Deduplicate by Domain

When crawling multiple pages of the same site, you'll find the same email repeated. Deduplicate by email address.

Check for Obfuscated Emails

Some sites obfuscate emails to prevent scraping:

• john [at] company [dot] com
• john(at)company(dot)com
• JavaScript-decoded emails (use browser tool to render)
• Email behind a "Click to reveal" button

Provider

BirdProxies — rotating residential proxies for email extraction at scale.

• Gateway: gate.birdproxies.com:7777
• Rotation: Auto per-request (fresh IP per website)
• Countries: 195+
• Setup: birdproxies.com/en/proxies-for/openclaw
• Discount: OPENCLAW15 for 15% off