After last week’s Google Dork demo I got a bunch of LinkedIn messages asking, ‘Can you teach the Dork framework in detail so we don’t have to copy-paste?’
So today I’m walking you through the five-step formula that actually works.
I know still some of you might be wondering: why learn the syntax when we’ve got AI?
The truth: AI is useful, but it’s not perfect. It can miss important details, make up content, pull unreliable sources, or give dorks that don’t return useful results. If you don’t understand how Google dorks work, you won’t know how to test, debug, or improve those queries. Learn the basics, then use AI to speed things up — not to replace your judgment.
That’s why you should learn the fundamentals, then use AI to speed things up. I hope that makes sense.
Okay — five pieces to build every solid dork: site target, exact role, exclusions, skill/context (with precision tools), and contact or file filters.
Step 1 — Site target. Tell Google where to look with site:. For developers and engineers start with site:github.com or site:stackoverflow.com.” or reddit.com. Reddit is also one of the hidden gems to find top talents. I’m sticking here with site:github.com. Here Google will narrow down its search to focus exclusively on Github site.
Step 2 — Role. Use quotes for exact titles so Google doesn’t guess. Like "Machine Learning Engineer".”
Step 3 — Exclude junk. Add -job -careers -apply so you don’t pull job listings.
Step 4 — Skill and precision. Add core skills like "Python" — or use intitle: / inurl: / allintext: for stricter matches. And when you want high precision, use AROUND(n) to force terms to appear near each other.
AROUND in action: site:reddit.com "data scientist" AROUND(20) "Python" intext:email — that forces the role and skill to sit close together for tighter hits.
→ Google will find pages where “data scientist" and “Python” appear within 20 words distance.
Important Note: AROUND operator is extremely restrictive and unreliable on GitHub. GitHub pages aren’t structured like resumes — the titles might not appear within a few words of each other. Using AROUND can accidentally eliminate good profiles.
Plus,
I’m also avoiding filetype:pdf because GitHub rarely stores resumes as PDFs. Most profiles use Markdown or simple text files. If you force PDF, you lose almost all candidates.
Step 5 — Contact and file filters. Use intext:email or intext:contact for contact info, and filetype:pdf or filetype:docx to pull hosted resumes or CVs.
Put it together — a practical GitHub dork you can run: site:github.com "Machine Learning Engineer" -job -careers "Python" intext:email. Run it and click a profile — you should see contact clues or a linked personal site.
OR
site:github.com "data scientist" "Python" intext:email (intext:"United States" OR intext:USA OR intext:"United States of America")
If you want city-level targeting (example: SF / NYC / Seattle):
site:github.com inurl:readme "Python" intext:@gmail.com -job
That's the perfect technical question to lead into your framework video! Sourcing contact details via a README is a master-level strategy because the developer is intentionally providing the information.
P.S. Dorking is an option generator, not a magic wand. Start simple, then layer on filters. Test each addition. If results drop to zero, you've gone too narrow—dial it back."
You must still review the results using your judgment—that’s why you’re irreplaceable!
Now you know how to build Google dorks from scratch. Go find your talent.
Happy sourcing!