github2obsidian

extract basic info from my starred github repos (readme, number of stars, and - most importantly - topics/tags), create markdown for use in obsidian, the github topics/tags are included in obsidian tag-syntax, allowing to facility tag- and link-tools of obsidian (graph-view, tag-extensions....)

python script github2obsidian.py

what does the script do?

• Fetches starred repositories from GitHub via API.
• Extracts relevant details about each repository,
  • such as the name,
  • description,
  • topics,
  • README content, and
  • star count.
• Generates a Markdown file for each repository with topics included as obsidian/markdown tags (e.g. topic "mapbox" become #mapbox in the obsidian markdown)

pre-requisites

• python 3 installed
• python module request (install via pip install requests)
• github account
• github personal access token for your github account (https://github.com/settings/tokens)

script usage

• download github2obsidian.py
• replace 'YOUR_GITHUB_USERNAME' and 'YOUR_GITHUB_TOKEN' with your data
• set 'OUTPUT_DIR' variable to the dir where you want the Markdown files saved (The script will create this directory if it doesn't exist. )
• Run the script with Python by navigating to the directory containing your script and using: python github2obsidian.py
• After running the script, you'll find a Markdown file for each starred repository in the specified output directory. These files can then be moved or synced to your Obsidian vault directory for integration into your Zettelkasten.

next steps

This script serves as a starting point. I would love to add more features or refine the existing ones. It's unlikely to happen ;-) If I had the time, energy, expertise, these would be things to consider: — customize the Markdown formatting

• perform Tag Clustering (grouping items based on shared tags to identify similarities or common themes)
• make use of Topic Modelling, i.e. going beyound explicit tags, making use of Topic Modelling projects which make use of Latent Dirichlet Allocation (LDA) and the like, to analyze the words in the created markdowns, in order to identify topics that pervade the collection
• try knowledge graph tools play with tools like Graphviz or Gephi, pyhton NetworkX, to get a basic understanding how knowledge-graphs are created, appreachiating even more what the obisidan developers have already made look to effortless and easy ;-)