This article is a working draft; I will update it as I go.

If you have yet to read the first part of this series, you can find it here.

We built a tool to help us analyze GitHub issues. Below is the result in action or a page.

Regarding the output:

  • Titles are not passed through GPT, and they are raw user entries. Normalizing the title is a good next step.
  • The body is more evident than the title most of the time.
  • Red boxes are issues that need to be classified correctly.

Sampling over 31 issues “user-support” (10%) and doing this classification manually, assisted by the AI tool, we make this classification:

documentation: 1062,1575,1675,1709,1770,1816,1845,1924
new-feature: 1322,1071,1519,1764,1333
wallet: 1071,1519
api: 1322
user-support: 1379,1623,1672,1770,1774,1796,1837,1850,1915,1930,1932
packaging: 1379
frontend: 1437
mime-content: 1468,1438
naming: 1518,1709

to-be-closed: 1709,1779,1816,1925

We see that the classification could be better. The model is not able to classify issues accurately. We have only ~10 out of ~30 issues classified correctly over a sample of 10% in the « user-support » label. It does help to read quickly over an issue; direct integration of this kind of tool is to be expected soon.

The prompt of the model needs to be reworked to get the model to classify issues more accurately.


  • Closure of old issues of non-contributor if there is no activity up to a determined amount of time.
    • A gitbot could help with this.
  • Labelling seems to be useful, at least for filtering.
  • Issues templating with different models with an example of what to include could fast-track issue triaging in the future.