- name
- lead-scorer
- description
- |
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
- DNS Analysis — MX records (Google Workspace/M365 = real business), SPF/DMARC
- Sitemap Parsing — URL count, last modified dates, content volume
- Website Scraping — Blog detection, tech stack, meta tags, social links, contact info
- Signal Scoring — Each signal scored against the profile weights
- Grade Assignment — A (80-100), B (60-79), C (40-59), D (20-39), F (0-19)
Dependencies
pip3 install dnspythonUsage
Single domain (default profile)
python3 scripts/score_lead.py example.comWith custom profile
python3 scripts/score_lead.py example.com --profile clearscope.jsonMultiple domains
python3 scripts/score_lead.py domain1.com domain2.com domain3.comBatch from CSV
python3 scripts/score_lead.py --csv leads.csv --domain-column "Website"Options
--profile FILE— Scoring profile JSON (default:default.json, resolved fromscripts/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
| Signal | What it checks |
|---|---|
has_blog | Blog/content section existence + sitemap volume |
business_legitimacy | MX provider, SPF/DMARC, about page, meta tags |
content_velocity | Sitemap dates — recency and frequency of updates |
tech_stack | CMS, analytics, chat tools detected in page source |
audience_size | Social media links (Twitter, LinkedIn, YouTube, Facebook) |
contact_findability | Contact page, emails on site, LinkedIn link |
seo_tools | Keyword matching in homepage text (requires keywords array) |
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 companyclearscope.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
| Batch size | Delay | Est. time |
|---|---|---|
| 1-10 | 0.5s (default) | ~30s-2min |
| 10-50 | 1.0s | ~5-15min |
| 50+ | 2.0s | ~30min+ |
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