causify-ai
Add script to synchronize GH branch protection and merge property
We want to create a Python script that allows us to synchronize settings from an inventory file to a GitHub repo
We have explored how to do it via GitHub App and GitHub Actions, such as
- https://github.com/apps/settings
- https://github.com/marketplace/actions/repo-settings-sync
We want to create something similar, but using Python scripts, similar to how we synchronize our labels
- https://github.com/causify-ai/helpers/blob/master/dev_scripts_helpers/github/dockerized_sync_gh_issue_labels.py
- https://github.com/causify-ai/helpers/blob/master/dev_scripts_helpers/github/sync_gh_issue_labels.py
We can use either of the following clients. Feel free to evaluate which one is easier to use and meets the requirement.
- GH CLI (https://cli.github.com/manual/)
- PyGithub (https://github.com/PyGithub/PyGithub)
Implementation
- Steps:
- Create a YAML file that stores the settings
- Create a Python script that synchronizes the settings to GitHub repo
- Wrap that script in a dockerized executable
- Add unit tests for important functionality
- We can start with branch protection and merge property setting first
- We can use the script to synchronize the labels mentioned above as sample
- The resulting script should be placed in
dev_scripts_helpers/github/
Feel free to do it in phases in multiple PRs
FYI @gpsaggese @Shaunak01 @sonniki
Assigning to @sandeepthalapanane as he has been working on the scripts to synchronize the GH issue labels
Feel free to post your plan or clarification questions here
We should also:
- have some light testing using mocks. Something simple, just to make sure the script is not broken
- add some documentation on how it works and how to use it
Small steps as usual is best.
Assigned to @sandeepthalapanane on the bounty gsheet too
Feel free to post your plan or clarification questions here
The initial plan is to create a script similar to dockerized_sync_gh_issue_labels.py, which will have the same arguments, including options for a dry run, backup, and a sample YAML file for settings. I will do a quick run locally and verify the implementation.
Once this script is completed, we can proceed to create the Dockerized executable. After that, the test process will be quite similar to what I have done previously, including mocking. Since PyGithub is easy to mock, we can use this library in our implementation.
