Antispam: plugin docs, the learning acceptance test, and a bootstrap that refuses bad corpora
docs/plugins.md described an SDK that no longer exists. It now covers messages.raw/sample, actions.move_to_special, the envelope's account_id / mailbox_special_use / account_state, the message.changed transition payload, the message.actions.menu slot, Slot.title and .wm-menu-item — with the reasons, not just the signatures. M3's acceptance is that a plugin can be written from this file alone, and it had drifted. Acceptance 4 (§C.8): a test that teaches 20 spam + 20 ham to a real engine and checks the classifier generalises to held-out mail, keeps ham under the banner threshold, and does not leak between users. It skips when no engine is reachable. It does NOT assert the literal move_threshold — a synthetic fixture cannot reach 12.0 once the SMTP-time symbols are zeroed, which is the whole point of the tuning. Recorded as OQ-8 rather than quietly asserting something weaker. Bayes ignores training until min_learns, stock 200. For a personal mailbox that means every correction appears to be ignored for months, so it is now 20. The bootstrap would have made this filter worse. Accounts do not sync their provider's spam folder by default, so the real corpus here is 3 junk beside 1,294 aged inbox — teaching both as found trains "everything is ham". It now refuses below 10 spam samples (leaving the account unmarked so a later run can do it properly) and otherwise caps ham at 3x the spam count. Run against the live account, it correctly declined: "only 3 spam samples (need 10); enable spam-folder sync to build a corpus" Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_018SqJYt7jD7oUEkoY68sBZo
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@@ -4,3 +4,10 @@
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per_user = true;
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autolearn = false; # only explicit/passive teaching from the app (C2)
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backend = "redis";
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# Rspamd ignores Bayes until it has seen this many messages (stock default 200).
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# For a personal mailbox that is a long silence: the filter would learn from every
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# correction the user made and appear to ignore all of them. 20 is enough to be
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# useful without being erratic, and the provider-Junk bootstrap (§C.5) clears it
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# on the first account that has a Junk folder worth reading.
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min_learns = 20;
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