Continual Learning Agent Skill

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.