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Every executed issue is a labelled example — this phrasing, this sizing, this acceptance
detail produced this outcome — and the loop currently does not close. There is a post-merge
feedback path into the drafter, but nothing systematically feeds the quality score back.
Scope
Feed per-issue quality scores and their component signals back as drafting signal:
which phrasings, sizings, and acceptance-detail patterns correlate with clean first-pass
merges
Persist the corpus of (issue text → outcome) pairs in a form usable for evaluation and
future training, with the same held-out-split discipline used elsewhere in the repo so
improvements are measured rather than asserted
Drafter changes are evaluated against held-out historical outcomes rather than judgment,
and the trend is visible.
Note
This is the compounding piece — decomposition quality improves from real execution rather
than synthetic data, and the corpus grows with every merged PR.
Problem
Every executed issue is a labelled example — this phrasing, this sizing, this acceptance
detail produced this outcome — and the loop currently does not close. There is a post-merge
feedback path into the drafter, but nothing systematically feeds the quality score back.
Scope
which phrasings, sizings, and acceptance-detail patterns correlate with clean first-pass
merges
future training, with the same held-out-split discipline used elsewhere in the repo so
improvements are measured rather than asserted
method, so a regression is visible
Acceptance
Drafter changes are evaluated against held-out historical outcomes rather than judgment,
and the trend is visible.
Note
This is the compounding piece — decomposition quality improves from real execution rather
than synthetic data, and the corpus grows with every merged PR.