How to Automate LinkedIn Lead List Building

I set up this system to build targeted lead lists from linkedin using automated search and extraction. This automate linkedin lead list building scraping approach turns manual lead work into automated pipeline growth.

Lead generation is the lifeblood of the business. Automating it means your pipeline fills while you focus on closing.

What You Need Before Starting

• Python 3.8+ with requests
• Anthropic API key
• CRM API (HubSpot, GoHighLevel, or similar)
• Database for lead storage

Step 1: Build the Capture Layer

from flask import Flask, request
import sqlite3
from datetime import datetime

app = Flask(__name__)
db = sqlite3.connect("leads.db")
db.execute('''CREATE TABLE IF NOT EXISTS leads (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    email TEXT UNIQUE, name TEXT, source TEXT,
    score INTEGER DEFAULT 0, created_at TEXT, status TEXT DEFAULT 'new'
)''')

@app.route("/api/lead", methods=["POST"])
def capture():
    data = request.json
    db.execute("INSERT OR IGNORE INTO leads (email, name, source, created_at) VALUES (?,?,?,?)",
        (data["email"], data.get("name", ""), data.get("source", "direct"), datetime.now().isoformat()))
    db.commit()
    return {"status": "captured"}

Step 2: Qualify with AI

import anthropic
from dotenv import load_dotenv

load_dotenv()
client = anthropic.Anthropic()

def qualify_lead(lead):
    message = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=256,
        system="Score this lead 1-100. Consider source, engagement, fit. Classify hot/warm/cold.",
        messages=[{"role": "user", "content": f"Lead: {lead}. Score and classify."}]
    )
    return message.content[0].text

Step 3: Route to Right Sequence

def route_lead(lead, score):
    if score >= 80:
        trigger_hot_sequence(lead)
        notify_sales(lead)
    elif score >= 50:
        trigger_nurture(lead)
    else:
        add_to_long_term(lead)

Step 4: Sync to CRM

import requests

def push_to_crm(lead, api_key):
    return requests.post(
        "https://api.hubapi.com/crm/v3/objects/contacts",
        headers={"Authorization": f"Bearer {api_key}"},
        json={"properties": {"email": lead["email"], "firstname": lead["name"], "lead_score": str(lead["score"])}}
    ).json()

What to Build Next

Add multi-touch attribution to know which channels produce highest-quality leads, not just the most volume.

Related Reading

• Lead Scoring with AI - framework for deciding what to automate first
• From Overwhelmed to Automated - framework for deciding what to automate first
• Building Moats with AI - practical guide to building AI business systems