>_ built by guillaume & pierre andre · since 2020 join the community · PAT — sourcing training ↗ · Anara ↗
>_freesourcingtools
EN FR

>_names to linkedin

Turn a list of names into ready-to-run Google X-Ray queries that surface each person's public LinkedIn profile, with optional company and location hints.

G by guillaume
>_ INPUT

Generate one search URL per name — free, no key. Searching by name works best on LinkedIn (real names are indexed).

>_ OUTPUT
Paste names, then pick a mode above.
>_ HOW IT WORKS
STEP 01

Paste a list of names

One name per line. Add an optional company hint and location hint to narrow each query.

STEP 02

Get one X-Ray query per name

The output is a list of `site:linkedin.com/in/ "Name" ["Company"] ["Location"]` queries, one per row.

STEP 03

Click to open each in Google

Open them in tabs and skim. The first result is usually right when the name + company combo is specific.

tipTwo-step approach — first run a name-only query, then refine with company once you've confirmed the profile is the right person. Saves time vs. over-narrowing upfront.

You’ve got a list of names — from a conference attendee sheet, a company org chart, a CSV someone dumped on you — and you need the LinkedIn profile behind each one. Pasting them into LinkedIn one at a time is slow, and LinkedIn search throttles you fast. This tool turns the whole list into ready-to-run Google X-Ray queries in one pass.

Paste one name per line. The output is a row per name, each carrying a query of the shape site:linkedin.com/in/ "Name", with an “Open ↗” button that fires it in Google. Add an optional company or location hint and it tightens every query at once. For example, pasting Andrej Karpathy with the company hint OpenAI builds site:linkedin.com/in/ "Andrej Karpathy" "OpenAI" — and the right profile is usually the first result when the name and company are specific.

Two honest notes. First, this surfaces only what Google has publicly indexed; profiles set to private won’t appear, and very common names (“John Smith”) need the company or location hint to disambiguate. Second, garbage in, garbage out — messy inputs like Smith, John (CEO) won’t match cleanly. There’s a button to run your list through the FST Name Cleaner GPT first to normalize them to John Smith before you generate. Open the queries in tabs, skim, and confirm the person before you trust the match.