Linky is a another LinkedIn scraper. Inspired by vysecurity and his LinkedInt project.
Currently, this method of extracting data from LinkedIn is limited to 1000 users at a time. So, Linky's HTML output has a small table at the bottom of the page which calculates the top 5 most common occupations that occur. This way, if the company has a weird naming scheme for devs, then Linky should be able to spot it and report it back. With these new found data points, the --keywords flag can be used to attempt to filter the output.
I will open to issues for the new features I'm working on implementing:
pip3 -r install requirements.txt
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<<<Yet another LinkedIn scraper>>>
usage: linky.py [-h] -c [-i] [-k] [-d] [-o] [-f]
Yet another LinkedIn scraper.
optional arguments:
-h, --help show this help message and exit
-c , --cookie Cookie to authenticate to LinkedIn with [li_at]
-i , --company-id Company ID number
-k , --keyword Keyword for searches
-d , --domain Company domain name
-o , --output File to output to: Writes CSV, JSON and HTML.
-f , --format Format for email addresses
When working with cookies, Linky accepts both file and string:
--cookie cookie.txt
OR
--cookie alongcookiestring
python3 --cookie cookie.txt --company-id 1441 --domain google.com --output google_employees --format 'firstname.surname'
python3 --cookie cookie.txt --company-id 1441 --domain google.com --output google_employees --format 'firstname.surname' --keyword developer
Currently, there is no support for middle names but its on the to-do list. Here are the current naming schemes:
firstname.surname
f.surname
firstnamesurname
fsurname
surname.firstname
s.firstname
surnamefirstname
sfirstname
They can all be referenced in --format, E.G:
f.surname: --format f.surname