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CrossLinked is a LinkedIn enumeration tool that uses search engine scraping to collect valid employee names from an organization. This technique provides accurate results without the use of API keys, credentials, or accessing LinkedIn directly!

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Install the last stable release from PyPi:

pip3 install crosslinked

Or, install the most recent code from GitHub:

git clone
cd crosslinked
python3 install


CrossLinked assumes the organization's account naming convention has already been identified. This is required for execution and should be added to the CMD args based on your expected output. See the Naming Format and Example Usage sections below:

Naming Format

{first.{last}           = john.smith
CMP\{first}{l}          = CMP\johns
{f}{last}   =

🦖 Still Stuck? Metadata is always a good place to check for hidden information such as account naming convention. see PyMeta for more.

Advanced Formatting

:boom: New Feature :boom:

To be compatible with alternate naming conventions CrossLinked allows users to control the index position of the name extracted from search text. Should the name not be long enough, or errors encountered with the search string, CrossLinked will revert back to its default format.

Note: the search string array starts at 0. Negative numbers can also be used to count backwards from the last value.

# Default output
crosslinked -f '{first}.{last}' Company
John David Smith =

# Use the second-to-last name as "last"
crosslinked -f '{0:first}.{-2:last}' Company
John David Smith    =
Jane Doe            =

# Use the second item in the array as "last"
crosslinked -f '{first}.{1:last}' Company
John David Smith    =
Jane Doe            =

By default, CrossLinked will use google and bing search engines to identify employees of the target organization. After execution, two files (names.txt & names.csv) will appear in the current directory, unless modified in the CMD args.

  • names.txt - List of unique user accounts in the specified format.
  • names.csv - Raw search data. See the Parse section below for more.

Example Usage

python3 -f '{first}.{last}' company_name
python3 -f 'domain\{f}{last}' -t 15 -j 2 company_name

⚠️ For best results, use the company name as it appears on LinkedIn "Target Company" not the domain name.



Account naming convention changed after execution and now your hitting CAPTCHA requests? No Problem!

CrossLinked includes a names.csv output file, which stores all scraping data including: name, job title, and url. This can be ingested and parsed to reformat user accounts as needed.

Example Usage

python3 -f '{f}{last}' names.csv


Additional Options

Proxy Rotation

The latest version of CrossLinked provides proxy support to rotate source addresses. Users can input a single proxy with --proxy or use multiple via --proxy-file proxies.txt.

> cat proxies.txt

> python3 --proxy-file proxies.txt -f '{first}.{last}' -t 10 "Company"

⚠️ HTTP/S proxies can be added by IP:Port notation. However, socks proxies will require a socks4:// or socks5:// prefix.*

Command-Line Arguments

positional arguments:
  company_name        Target company name

optional arguments:
  -h, --help          show help message and exit
  -t TIMEOUT          Max timeout per search (Default=15)
  -j JITTER           Jitter between requests (Default=1)

Search arguments:
  --search ENGINE     Search Engine (Default='google,bing')

Output arguments:
  -f NFORMAT          Format names, ex: 'domain\{f}{last}', '{first}.{last}'
  -o OUTFILE          Change name of output file (omit_extension)

Proxy arguments:
  --proxy PROXY       Proxy requests (IP:Port)
  --proxy-file PROXY  Load proxies from file for rotation


Contribute to the project by:

  • Like and share the tool!
  • Create an issue to report any problems or, better yet, initiate a PR.
  • Reach out with any potential features or improvements @m8sec.


Did you know?

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