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A reviewer who keeps flagging the same non-issue is a reviewer your team learns to ignore. The fix is not a config file nobody updates; it is the ability to tell the reviewer, in the moment, what your codebase actually does — and have it remember. Sigilix learns from conversation. When a finding is wrong for a reason specific to your repo, you reply in plain language. Sigilix records the rule, applies it judiciously in future reviews, and — crucially — tells you when a later review was shaped by something it learned. Learnings are per-repo and per-customer; what one team teaches stays with that team.

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Teach a rule in three ways

1

Reply to a finding

The most natural path. A finding fires, you disagree, and you say why: “we use integer cents here, this isn’t a float-rounding bug.” Sigilix turns that reply into a rule scoped to this repo.
2

@sigilix remember …

State a rule directly, without waiting for a finding to react to:
@sigilix remember we use integer cents for all money values
3

@sigilix forget …

Retire a rule that no longer applies:
@sigilix forget the integer-cents rule
When Sigilix captures a new rule, it confirms with a “Learned something new” footer on its reply, so you know the lesson landed and did not vanish into the thread.

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Applied judiciously, and attributed inline

A learned rule is not a hard mute. Sigilix reasons about whether the rule applies to the specific code in front of it, rather than blanket-suppressing a whole category. When a later review is shaped by a learning, the finding (or its absence) is attributed inline — you will see a note that it was applied because of a learned rule. The attribution matters: it keeps the system honest and lets you see exactly which lesson changed the outcome.

Judicious application

A rule narrows judgment where it is relevant; it does not silence an entire severity or category. Safety-critical and security findings remain protected.

Inline attribution

When a learning changes a review, Sigilix says so on the spot — “applied because of a learned rule” — so the influence of memory is visible, not hidden.

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Where Learnings sit in the pipeline

Conversational Learnings is the memory gate of the believability pipeline. After a candidate finding has cleared evidence, provenance, refutation, and received its proof-tier receipt, it is weighed against what Sigilix has learned about this repo. A finding that contradicts a learned rule can be down-weighted or suppressed before it ever reaches the PR. Learnings is also a layer of earned context: every rule you teach becomes reusable understanding that later reviews — and the CLI and Deep-Research Chat — can draw on.
Learnings is the headline mechanism of review memory. A secondary, statistical signal also exists — Sigilix tracks accept-and-dismiss patterns across findings — but the plain-language rules you teach are the primary, explicit way to shape how Sigilix reviews your code.

Commands

The full command reference, including @sigilix remember and @sigilix forget.

Earned Context

How learned rules join the reusable context layer the whole product draws on.