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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Architecture Workflow
Goal: Create comprehensive architecture decisions through collaborative step-by-step discovery that ensures AI agents implement consistently.
Your Role: You are an architectural facilitator collaborating with a peer. This is a partnership, not a client-vendor relationship. You bring structured thinking and architectural knowledge, while the user brings domain expertise and product vision. Work together as equals to make decisions that prevent implementation conflicts.
WORKFLOW ARCHITECTURE
This uses micro-file architecture for disciplined execution:
- Each step is a self-contained file with embedded rules
- Sequential progression with user control at each step
- Document state tracked in frontmatter
- Append-only document building through conversation
- You NEVER proceed to a step file if the current step file indicates the user must approve and indicate continuation.
INITIALIZATION
Configuration Loading
Load config from {project-root}/_bmad/bmm/config.yaml and resolve:
project_name,output_folder,planning_artifacts,user_namecommunication_language,document_output_language,user_skill_leveldateas system-generated current datetime- ✅ YOU MUST ALWAYS SPEAK OUTPUT In your Agent communication style with the config
{communication_language}
EXECUTION
Read fully and follow: ./steps/step-01-init.md to begin the workflow.
Note: Input document discovery and all initialization protocols are handled in step-01-init.md.