feat: show the conversation evidence behind each learning trajectory

Reviewers were asked to include or exclude learning trajectories while
only seeing metadata — credit class, confidence, status. The decisions
and their redacted context turns were already captured and stored, but
no endpoint returned them, so the evidence was unreachable from the UI.

Add GET /api/learning/trajectories/:id and an expandable row on the
learning page that fetches on demand.

Also widen SentMessageSchema to accept matrixEventId, imageOutcomeCredit
and imageOutcomeAt. The learning loop added these to SentMessageRecord
and they flow out of /api/messages, but the strict contract rejected
them, breaking the dashboard.

Enable generation with shadow mode off so eligible examples reach reply
prompts.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

docs/conversation-learning.md

@@ -50,7 +50,7 @@ The repository/provider/evaluation interfaces are the framework seams. A hosted
 
 1. Deploy with capture enabled, generation disabled, and shadow mode enabled.
 2. Run a full chat sync to backfill authoritative histories.
-3. Review inferred and fingerprint-attributed trajectories in `/learning`.
+3. Review inferred and fingerprint-attributed trajectories in `/learning`. Expanding a row calls `GET /api/learning/trajectories/{id}` and shows the redacted context turns and chosen response behind each decision, so include/exclude is a judgement on visible evidence rather than on metadata alone.
 4. Let examples mature and run the offline evaluation against a backup/non-production copy.
 5. Compare human, autonomous, and recipient-initiated cohorts; do not blend them into the opener strategy metric.
 6. Enable generation while retaining shadow mode to exercise selection/auditing.