[dustfinger]

Yeah, that matches what I have seen: if the upstream metadata isn’t reliable, automation can amplify the mess.

I tried to avoid relying solely on contributors to accurately label or tag things correctly. The script is tag-driven only for release boundaries (version tags), while categorization is derived from PR title & body with optional GitHub metadata. The script is idempotent and preserves edits/omissions so you can correct the few bad ones post-generation.

If you are curious, I am happy to share my script and would be genuinely interested whether it reduces the manual cleanup for your workflow. Also, if you run it with `--ai --github` and a PR body is sparse, it fetches a truncated PR diff and uses that as extra context for the LLM summary.