package starred_ml

Starred_ml - Github Awesome list based on starred proejct

Are you a compulsive Github stargazer? Starred_ml is here for you! It will access Github https://api.github.com/user/starred API and fetch all starred repositories and dump a mardown that you can use as README.md in a repository.

Running

opam update
opam install starred_ml


TOKEN={{your github personal token}} starred_ml > README.md 

See Templating section for details on the output markdown.

Will output something liket this:

Awesome Stars
===

> A curated list of Github stars! Generated by [starred_ml](https://github.com/paulosuzart/starred_ml)


Languages
---
   - [C](#C)
   - [C#](#C#)
   - [C++](#C++)
   - [Haskell](#Haskell)
   - [Java](#Java)
   - [Jupyter Notebook](#Jupyter Notebook)
   - [Lua](#Lua)
 ...

Contents
---

## C
   - [PikaPython](https://api.github.com/repos/pikasTech/PikaPython): An ultra-lightweight Python interpreter that runs with only 4KB of RAM, zero dependencies. It is ready to use out of the box without any configuration required and easy to extend with C. Similar project: MicroPython, JerryScript.
## C#
   - [kiota](https://api.github.com/repos/microsoft/kiota): OpenAPI based HTTP Client code generator
...

Generating your awesome README.md

You can run starred_ml locally, but it makes more sense that you atomatically generate a README.md in dedicated github repository. For that you can use my Awesome as a template https://github.com/paulosuzart/awesome.

You need two things:

1. Select Allow GitHub Actions to create and approve pull requests under (YOUR REPO)/Settings/Actions/General.

2. A PAT Personal Access Token with starred read-only access that you must set to your awesome repository under (YOUR REPO)/Settings/Secrets and variables/Actions, then in the Repository Secrets you can add your generated token with the name TOKEN.

Templating

Starred_ml uses jingoo as a template engine. Create a default.jingoo file if you want to customize. Please refer to the provided template here and customize as you see fit.

The availabe variables are:

• lang_count - The total count of languages found (Including Not set)

• languages - a list of languages detected across all starred repos

• by_language - a list of objects representing starred repositories with the following keys:

  • language - the language of in question

  • starred - a list of objects with the following keys:

    • name - name of the repository

    • html_url - Github url

    • description - repository description

Similar projects

While developing Starred_ml I found at least one alternative:

• smaguowei/starred written in python. I then proceeded to use a similar output