House rules that earn their place
A learning is one rule, in a sentence, that inline applies to future reviews. Your team can teach one from any pull request, and inline drafts them from what the team keeps dismissing or fixing. Every rule carries the evidence for it, and the ones the evidence stops backing retire.
Rules that earn
their place
Every rule carries its evidence: how often reviews applied it, and whether later pull requests agreed with it. With the lifecycle on automatic, rules that keep being contradicted, or that nothing has needed for 120 days, move to Retired on their own with the reason recorded. Pin the ones that should never move.
How it works
Teach it from
the pull request
Comment @inline learn on any pull request, followed by the rule, and inline applies it to future reviews of that repository. @inline learn global stores it for every repository in the workspace. A rule can narrow to a path glob, a language or both, so it only rides along when a pull request changes a file it matches.
Drafted from what
your team does
When your team keeps dismissing the same kind of finding across several pull requests, inline drafts a rule to stop raising it. When the team keeps fixing one, it drafts a rule to keep raising it. Either lands in Suggested first, with the findings and pull requests behind it, for an admin to accept, edit or reject.
Some decisions stay
with a person
A rule that would silence bug or security findings never activates on its own. A rule a person retired or dismissed never comes back by itself, and a pinned rule never moves. Switch the automatic lifecycle off and every change becomes a person's decision. Each review lists the rules it carried, so you can see a rule at work.
Included
Related
Repository wiki
A wiki generated from each repository and rebuilt on every push, plus a check that quotes the docs a merged pull request made wrong.
Read moreSpecialist reviewers
Up to three specialists read a risky change for security, correctness, concurrency or API breakage, and a verifier tries to refute each finding before it posts.
Read moreEngineering metrics
DORA figures, cycle time split by stage, and how much of the work an AI wrote, counted from the pull requests and deploys inline already sees.
Read more
Put it on one repository
Connect GitHub or GitLab, enable a repo, and the next pull request opened gets a review.