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Collect a dossier on a person by username from a huge number of sites

Project description

Maigret

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The Commissioner Jules Maigret is a fictional French police detective, created by Georges Simenon. His investigation method is based on understanding the personality of different people and their interactions.

About

Purpose of Maigret - collect a dossier on a person by username only, checking for accounts on a huge number of sites.

This is a sherlock fork with cool features under heavy development. Don't forget to regularly update source code from repo.

Currently supported more than 2000 sites (full list), by default search is launched against 500 popular sites in descending order of popularity.

Main features

  • Profile pages parsing, extracting personal info, links to other profiles, etc.
  • Recursive search by new usernames found
  • Search by tags (site categories, countries)
  • Censorship and captcha detection
  • Very few false positives

Installation

NOTE: Python 3.6 or higher and pip is required.

Python 3.8 is recommended.

# install from pypi
$ pip3 install maigret

# or clone and install manually
$ git clone https://github.com/soxoj/maigret && cd maigret
$ pip3 install .

Using examples

maigret user

# make HTML and PDF reports
maigret user --html --pdf

# search on sites marked with tags photo & dating
maigret user --tags photo,dating


# search for three usernames on all available sites
maigret user1 user2 user3 -a

Run maigret --help to get arguments description. Also options are documented in the Maigret Wiki.

With Docker:

docker build -t maigret .

docker run maigret user

Demo with page parsing and recursive username search

PDF report, HTML report

maigret alexaimephotographycars

animation of recursive search

Full output

License

MIT © Maigret
MIT © Sherlock Project
Original Creator of Sherlock Project - Siddharth Dushantha

Project details


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Source Distribution

maigret-0.1.11.tar.gz (108.3 kB view hashes)

Uploaded Source

Built Distribution

maigret-0.1.11-py3-none-any.whl (114.5 kB view hashes)

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