Call sites take the fallback text and log the error instead of treating a
canned string as success. phraseSource drops the text entirely — its callers
hold the passage and read it back better than "вот что я нашла: <passage>".
The talk scorer's before-and-after model probe (the #395 workaround) goes;
the run now fails only when every case errored, which is the honest
"nothing was measured" condition. TalkFixture gets its own schema version so
the two fixtures can be versioned apart.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Nine reply cases and a fourth column in the talk report. The reply path is a
separate object from the phraser in the daemon, so Pair joins a Talker and a
Confirmer for a run that covers everything Maven says.
Cases carry intent/key/value because the replier is phrased from the decision the
router resolved, not from the raw utterance. Three of them are baits the other
paths cannot produce: a masculine verb about himself that she must not copy onto
herself, a polite plural input that must still come back на ты, and an unresolved
note that invites a question a confirmation is not allowed to ask.
Not scored against a model here — this box has no llama-server, and the baseline
test is opt-in on MAVEN_LLM_URL.
The phrasing fixture was 15 nudge cases, so every prompt change we
measured only told us about nudges. But the shared context block sits in
front of five prompts, and three of them — chat, note query, general
knowledge — had no scorer at all. Those are the long free-form replies,
where a persona break is most likely and where nothing could see one.
27 cases, nine per path. Nine rather than five because the nudge fixture
already cannot resolve a change smaller than about three cases, and a
per-path score off five would be worse.
Reuses the persona checks instead of copying them. Length, mood and
"no questions" are left out on purpose: these paths return no mood, and
a follow-up question is a feature in chat, not a fault.
The run refuses to score unless the model answers before and after it.
PhraseChat and PhraseQuery swallow model errors and return a canned
string, so without that guard a dead server produces a full report with
zero errors and a bad score — which reads as bad phrasing rather than as
nothing measured. Vikunja #397 is the real fix.
claude
kami