feat: Calculer automatiquement les moyennes après chaque saisie de notes
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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.
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# Workflow Classification Reference
Classify the skill type based on user requirements. This table is for internal use — DO NOT show to user.
## 3-Type Taxonomy
| Type | Description | Structure | When to Use |
| -------------------- | ---------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------- | ---------------------------------------------------------------------------- |
| **Simple Utility** | Input/output building block. Headless, composable, often has scripts. | Single SKILL.md + scripts/ | Composable building block with clear input/output, single-purpose |
| **Simple Workflow** | Multi-step process contained in a single SKILL.md. Minimal or no prompt files. | SKILL.md + optional references/ | Multi-step process that fits in one file, no progressive disclosure needed |
| **Complex Workflow** | Multi-stage with progressive disclosure, numbered prompt files at root, config integration. May support headless mode. | SKILL.md (routing) + prompt stages at root + references/ | Multiple stages, long-running process, progressive disclosure, routing logic |
## Decision Tree
```
1. Is it a composable building block with clear input/output?
└─ YES → Simple Utility
└─ NO ↓
2. Can it fit in a single SKILL.md without progressive disclosure?
└─ YES → Simple Workflow
└─ NO ↓
3. Does it need multiple stages, long-running process, or progressive disclosure?
└─ YES → Complex Workflow
```
## Classification Signals
### Simple Utility Signals
- Clear input → processing → output pattern
- No user interaction needed during execution
- Other skills/workflows call it
- Deterministic or near-deterministic behavior
- Could be a script but needs LLM judgment
- Examples: JSON validator, schema checker, format converter
### Simple Workflow Signals
- 3-8 numbered steps
- User interaction at specific points
- Uses standard tools (gh, git, npm, etc.)
- Produces a single output artifact
- No need to track state across compactions
- Examples: PR creator, deployment checklist, code review
### Complex Workflow Signals
- Multiple distinct phases/stages
- Long-running (likely to hit context compaction)
- Progressive disclosure needed (too much for one file)
- Routing logic in SKILL.md dispatches to stage prompts
- Produces multiple artifacts across stages
- May support headless/autonomous mode
- Examples: agent builder, module builder, project scaffolder
## Module Context (Orthogonal)
Module context is asked for ALL types:
- **Module-based:** Part of a BMad module. Uses `bmad-{modulecode}-{skillname}` naming. Config loading includes a fallback pattern — if config is missing, the skill informs the user that the module setup skill is available and continues with sensible defaults.
- **Standalone:** Independent skill. Uses `bmad-{skillname}` naming. Config loading is best-effort — load if available, use defaults if not, no mention of a setup skill.