feat: Calculer automatiquement les moyennes après chaque saisie de notes

Les enseignants ont besoin de moyennes à jour immédiatement après la
publication ou modification des notes, sans attendre un batch nocturne.

Le système recalcule via Domain Events synchrones : statistiques
d'évaluation (min/max/moyenne/médiane), moyennes matières pondérées
(normalisation /20), et moyenne générale par élève. Les résultats sont
stockés dans des tables dénormalisées avec cache Redis (TTL 5 min).

Trois endpoints API exposent les données avec contrôle d'accès par rôle.
Une commande console permet le backfill des données historiques au
déploiement.

.agents/skills/bmad-code-review/steps/step-03-triage.md

@@ -0,0 +1,49 @@
+---
+---
+
+# Step 3: Triage
+
+## RULES
+
+- YOU MUST ALWAYS SPEAK OUTPUT in your Agent communication style with the config `{communication_language}`
+- Be precise. When uncertain between categories, prefer the more conservative classification.
+
+## INSTRUCTIONS
+
+1. **Normalize** findings into a common format. Expected input formats:
+   - Adversarial (Blind Hunter): markdown list of descriptions
+   - Edge Case Hunter: JSON array with `location`, `trigger_condition`, `guard_snippet`, `potential_consequence` fields
+   - Acceptance Auditor: markdown list with title, AC/constraint reference, and evidence
+
+   If a layer's output does not match its expected format, attempt best-effort parsing. Note any parsing issues for the user.
+
+   Convert all to a unified list where each finding has:
+   - `id` -- sequential integer
+   - `source` -- `blind`, `edge`, `auditor`, or merged sources (e.g., `blind+edge`)
+   - `title` -- one-line summary
+   - `detail` -- full description
+   - `location` -- file and line reference (if available)
+
+2. **Deduplicate.** If two or more findings describe the same issue, merge them into one:
+   - Use the most specific finding as the base (prefer edge-case JSON with location over adversarial prose).
+   - Append any unique detail, reasoning, or location references from the other finding(s) into the surviving `detail` field.
+   - Set `source` to the merged sources (e.g., `blind+edge`).
+
+3. **Classify** each finding into exactly one bucket:
+   - **decision_needed** -- There is an ambiguous choice that requires human input. The code cannot be correctly patched without knowing the user's intent. Only possible if `{review_mode}` = `"full"`.
+   - **patch** -- Code issue that is fixable without human input. The correct fix is unambiguous.
+   - **defer** -- Pre-existing issue not caused by the current change. Real but not actionable now.
+   - **dismiss** -- Noise, false positive, or handled elsewhere.
+
+   If `{review_mode}` = `"no-spec"` and a finding would otherwise be `decision_needed`, reclassify it as `patch` (if the fix is unambiguous) or `defer` (if not).
+
+4. **Drop** all `dismiss` findings. Record the dismiss count for the summary.
+
+5. If `{failed_layers}` is non-empty, report which layers failed before announcing results. If zero findings remain after dropping dismissed AND `{failed_layers}` is non-empty, warn the user that the review may be incomplete rather than announcing a clean review.
+
+6. If zero findings remain after triage (all rejected or none raised): state "✅ Clean review — all layers passed." (Step 3 already warned if any review layers failed via `{failed_layers}`.)
+
+
+## NEXT
+
+Read fully and follow `./step-04-present.md`