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.
3.4 KiB
3.4 KiB
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.