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Yet Another LInkedIn Scraper...
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.gitignore working keywords Aug 31, 2019 updated Nov 19, 2019
LICENSE release 0.1 Aug 2, 2019 fixed encoding Nov 20, 2019


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Yet another LinkedIn Scraper...

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.


pip3 -r install requirements.txt

Help Page

usage: [-h] [-c] [-i] [-k] [-d] [-o] [-f] [-v] [-a] [-t]
                [--valid-emails-only] [--verbose] [--debug]
                [--list-email-schemes | --version]

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
  -v , --validate       Validate email addresses: O365/Hunter API
  -a , --api            API Key for Hunter API
  -t , --threads        Amount of threads to use [default 5]
  --valid-emails-only   When you literally only want a txt of valid emails.
  --verbose             Verbosity of the output
  --debug               Enable debugging, will spam.
  --list-email-schemes  List available email schemes
  --version             Print current version

Example: python3 --cookie cookie.txt --company-id 1441 --domain --output google_employees --format 'firstname.surname'


Get Employees

python3 --cookie cookie.txt --company-id 1441 --domain --output google_employees --format 'firstname.surname'

Get Employees with keyword

python3 --cookie cookie.txt --company-id 1441 --domain --output google_employees --format 'firstname.surname' --keyword developer

Supported email formats

Run --list-email-schemes to see all current formats:


They can all be referenced in --format, E.G:

f.surname: --format f.surname

Job Role Count

By default, Linky will count the occurence of job roles and write it out to html. But, it will also do so with a standard json file. The structure is as seen below:

  "Software Developer": 24,
  "Systems Developer": 14,
  "Senior Software Developer": 11,
  "Project Manager": 10,
  "System Developer": 9,
  "Cyber Security Consultant": 7,
  "Project Developer": 7,
  "Programme Manager": 6,
  "Software Architect": 6,
  "Development Manager": 6

Efficient usage

  1. Run once the gain the initial data:

python3 --cookie cookie.txt --company-id 1441 --domain --output google_employees --format 'firstname.surname'

  1. Find the job role occurence

    cat job_role_count.json|jq

  2. With the roles identified, use the keyword feature:

python3 --cookie cookie.txt --company-id 1441 --domain --output google_employees --format 'firstname.surname' --keyword developer

Only print a list of validated email addresses

The --valid-emails-only flag will perform the same level of enumeration. But, it will only output validated emails to a txt file. This also assumes o365 validation.

python3 --cookie cookie.txt --company-id 1441 --domain --output google_employees --format 'firstname.surname' --keyword developer --valid-emails-only

From this command, a txt file will be created with nothing but emails that were found to be valid via o365.

This is basically the TL;DR version of Linky.

Happy Stalking.

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