lead-scorer-free（免费首席得分手）

Lead Scorer

Analyze a domain and return a 0-100 lead score with detailed breakdown. The key feature is customizable scoring profiles — JSON configs that define which signals matter and their weights.

How It Works

1. DNS Analysis — MX records (Google Workspace/M365 = real business), SPF/DMARC
2. Sitemap Parsing — URL count, last modified dates, content volume
3. Website Scraping — Blog detection, tech stack, meta tags, social links, contact info
4. Signal Scoring — Each signal scored against the profile weights
5. Grade Assignment — A (80-100), B (60-79), C (40-59), D (20-39), F (0-19)

Dependencies

pip3 install dnspython

Usage

Single domain (default profile)

python3 scripts/score_lead.py example.com

With custom profile

python3 scripts/score_lead.py example.com --profile clearscope.json

Multiple domains

python3 scripts/score_lead.py domain1.com domain2.com domain3.com

Batch from CSV

python3 scripts/score_lead.py --csv leads.csv --domain-column "Website"

Options

• --profile FILE — Scoring profile JSON (default: default.json, resolved from scripts/profiles/)
• --csv FILE — CSV file with domains
• --domain-column NAME — Column name for domains in CSV (default: domain)
• --scrape-delay SECONDS — Delay between HTTP requests (default: 0.5)
• --output FILE — Write results to file instead of stdout

Output

JSON to stdout with overall score, per-signal breakdown, raw data, and summary:

{
  "domain": "example.com",
  "score": 72,
  "grade": "B",
  "profile": "default",
  "signals": {
    "has_blog": {"score": 20, "max": 20, "evidence": "Blog found at /blog; 234 URLs in sitemap"},
    "business_legitimacy": {"score": 15, "max": 20, "evidence": "MX: Google Workspace; SPF configured"}
  },
  "raw_data": {
    "sitemap_urls": 234,
    "mx_provider": "Google Workspace",
    "tech_stack": ["WordPress", "Cloudflare"]
  },
  "summary": "Strong in: has blog, business legitimacy. Good lead, worth pursuing."
}

Scoring Profiles

Profiles are the key differentiator. They let you define what matters for YOUR use case.

Profile format

{
  "name": "my-profile",
  "description": "What this profile scores for",
  "signals": {
    "signal_name": {
      "weight": 25,
      "description": "What this signal measures",
      "keywords": ["optional", "keyword", "list"]
    }
  }
}

Built-in signals

Custom keyword signals

Any signal with a keywords array will match those terms against the homepage text. This is how you detect competitors, tools, or industry terms:

{
  "name": "crm-seller",
  "signals": {
    "uses_crm": {
      "weight": 30,
      "description": "Already uses a CRM",
      "keywords": ["salesforce", "hubspot", "pipedrive", "zoho crm", "close.io"]
    },
    "has_sales_team": {
      "weight": 25,
      "description": "Mentions sales roles or team",
      "keywords": ["sales team", "account executive", "sdr", "business development"]
    }
  }
}

Shipped profiles

• default.json — Generic scoring for any SaaS/content company
• clearscope.json — Example profile for SEO tool partnership leads

Create your own in scripts/profiles/ or pass any path with --profile.

Rate Limiting

The script is polite by default:

• --scrape-delay 0.5 — 500ms between HTTP requests (default)
• Each domain makes ~5-8 requests (homepage, blog, about, contact, sitemap, DNS)
• For batch mode, there's an additional delay between domains
• Increase delay for large batches: --scrape-delay 2
• All requests use a generic User-Agent string

Recommended delays

Error Handling

If a signal can't be gathered (site down, DNS timeout, etc.), it scores 0 with an explanation in the evidence field. The script never crashes on a single domain failure — it logs the issue to stderr and continues.

Tips

• Start with default profile, review results, then customize
• Weights should sum to 100 for intuitive scoring (not required — auto-normalizes)
• Keywords are powerful — add competitor names, industry terms, technology mentions
• Pipe to jq for quick filtering: python3 scripts/score_lead.py domain.com | jq '.score'
• Batch + sort: Score a CSV, then sort by score to prioritize outreach