| name | continual-learning |
| description | Nightly refinement of an existing per-repo review-style prompt using this reviewer's own finding outcomes. Read confirmed (resolved-by-commit / thumbs-up) and dismissed (thumbs-down) findings, promote the bug patterns the team actually fixes, demote the false-positive patterns, reconcile against the current prompt, and save the refined version. Use this once outcomes exist; use bootstrap-repo-analysis for a cold-start repo. |
Continual learning
You are refining the existing review-style prompt for the repository named in the
system prompt, using outcomes the reviewer has accrued since the last run. The goal is
to raise recall (catch more real bugs) without hurting precision (stop repeating
dismissed ones).
1. Read outcomes first
Call read_finding_outcomes once. It returns this repo's past findings split into:
confirmed — resolved by a follow-up commit or 👍'd. These are real bug patterns
this team fixes. Promote the recurring ones into the prompt's "hunt for" guidance,
quoting the file/diff_hunk context so the rule stays concrete.dismissed — dismissed or 👎'd. These are false-positive patterns. Add the
recurring ones to the prompt's "do not flag" section so the reviewer stops repeating
them.
Look for repetition, not one-offs. A single dismissed finding is noise; the same class
dismissed several times is a rule.
2. Reconcile against the current prompt
The current custom_prompt is the starting point — you are editing it, not rewriting
from scratch. Read it (it is summarized for you / available via the dashboard record).
Keep what still holds, strengthen rules the outcomes confirm, and remove or soften rules
the outcomes contradict. Optionally do a light gh top-up
(GH_TOKEN=dummy gh ...) to confirm a pattern, but outcomes are the primary signal — do
not re-run a full PR crawl.
Stay aligned with the reviewer-agent themes in the system prompt.
3. Save
Call save_review_style_prompt once with the refined custom_prompt (400–1200 words),
an analysis_summary that names what changed this cycle (e.g. "promoted N-pattern after
3 confirmed fixes; dropped M-pattern after repeated dismissals"), and the
top_reviewers / counts you have. If outcomes were empty and nothing changed, say so in
analysis_summary and re-save the existing prompt unchanged rather than degrading it.
