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This project will automatically contribute to Github in a way that enhances the user's contribution graph.
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README.md
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README.md

Github Contribution Graph Maximizer

The purpose of this project is to manipulate the user's contribution graph in a way that illustrates consistent effort being pushed to Github throughout the year. Often, strong contributors are prone to squashing their commits or contributing to projects outside Github. As a result, they may have what looks like a poor contribution graphs and in turn this leads to unfair prejudices by peers and a lack of self-worth after reflecting on one's lack of contributions. This project intends to address the deficiencies of summary representations of one’s efforts such as the Github contribution graph.

Before

Before

After

After

Prerequisites

  • Python 3
  • Pip
  • Git

Quick Start

These commands will install github_maximizer and its prerequisites. Then when you run the script it will create the repository specified by -n and contribute a year's worth of commits to the Github account specified by the config file.

git clone https://github.com/geedo0/github_maximizer.git
cd github_maximizer
pip install -r requirements.txt
./github_maximizer.py -n 'stateless_info_client' -c sample_config.ini

Cron Mode

After generating a year's worth of contributions, it may be worthwhile to run the script as a daily cron job with '-d 1'. That way, the script will randomly generate a series of commits and push them up for you each day. The result is an unbroken chain of contributions for your profile.

# crontab
# m h  dom mon dow   command
0 0 * * * * /scripts/github_maximizer/github_maximizer.py -n stateless_info_client -c /scripts/github_maximizer/sample_config.ini -d 1

Notes

  • This uses a Poisson distribution for determining how many commits to make on any given day. In general, it will look like you commit more often on weekdays than on weekends.
  • Your password is stored in plain text and is used without much concern for security by this script. Make sure you read the code and understand your risks.
  • The commits generated are obviously fake, so if you care about that mark the repository as private and set Github to count private contributions.
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