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-product-brief/prompts/contextual-discovery.md

@@ -0,0 +1,57 @@
+**Language:** Use `{communication_language}` for all output.
+**Output Language:** Use `{document_output_language}` for documents.
+**Output Location:** `{planning_artifacts}`
+
+# Stage 2: Contextual Discovery
+
+**Goal:** Armed with the user's stated intent, intelligently gather and synthesize all available context — documents, project knowledge, and web research — so later stages work from a rich, relevant foundation.
+
+## Subagent Fan-Out
+
+Now that you know what the brief is about, fan out subagents in parallel to gather context. Each subagent receives the product intent summary so it knows what's relevant.
+
+**Launch in parallel:**
+
+1. **Artifact Analyzer** (`../agents/artifact-analyzer.md`) — Scans `{planning_artifacts}` and `{project_knowledge}` for relevant documents. Also scans any specific paths the user provided. Returns structured synthesis of what it found.
+
+2. **Web Researcher** (`../agents/web-researcher.md`) — Searches for competitive landscape, market context, trends, and relevant industry data. Returns structured findings scoped to the product domain.
+
+### Graceful Degradation
+
+If subagents are unavailable or fail:
+- Read only the most relevant 1-2 documents in the main context and summarize (don't full-read everything — limit context impact in degraded mode)
+- Do a few targeted web searches inline
+- Never block the workflow because a subagent feature is unavailable
+
+## Synthesis
+
+Once subagent results return (or inline scanning completes):
+
+1. **Merge findings** with what the user already told you
+2. **Identify gaps** — what do you still need to know to write a solid brief?
+3. **Note surprises** — anything from research that contradicts or enriches the user's assumptions?
+
+## Mode-Specific Behavior
+
+**Guided mode:**
+- Present a concise summary of what you found: "Here's what I learned from your documents and web research..."
+- Highlight anything surprising or worth discussing
+- Share the gaps you've identified
+- Ask: "Anything else you'd like to add, or shall we move on to filling in the details?"
+- Route to `guided-elicitation.md`
+
+**Yolo mode:**
+- Absorb all findings silently
+- Skip directly to `draft-and-review.md` — you have enough to draft
+- The user will refine later
+
+**Headless mode:**
+- Absorb all findings
+- Skip directly to `draft-and-review.md`
+- No interaction
+
+## Stage Complete
+
+This stage is complete when subagent results (or inline scanning fallback) have returned and findings are merged with user context. Route per mode:
+- **Guided** → `guided-elicitation.md`
+- **Yolo / Headless** → `draft-and-review.md`