Mathias STRASSER
b7dc27f2a5
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.
3.1 KiB
Quality Dimensions — Quick Reference
Seven dimensions to keep in mind when building agent skills. The quality scanners check these automatically during quality analysis — this is a mental checklist for the build phase.
1. Outcome-Driven Design
Describe what each capability achieves, not how to do it step by step. The agent's persona context (identity, communication style, principles) informs HOW — capability prompts just need the WHAT.
- The test: Would removing this instruction cause the agent to produce a worse outcome? If the agent would do it anyway given its persona and the desired outcome, the instruction is noise.
- Pruning: If a capability prompt teaches the LLM something it already knows — or repeats guidance already in the agent's identity/style — cut it.
- When procedure IS value: Exact script invocations, specific file paths, API calls, security-critical operations. These need low freedom.
2. Informed Autonomy
The executing agent needs enough context to make judgment calls when situations don't match the script. The Overview section establishes this: domain framing, theory of mind, design rationale.
- Simple agents with 1-2 capabilities need minimal context
- Agents with memory, autonomous mode, or complex capabilities need domain understanding, user perspective, and rationale for non-obvious choices
- When in doubt, explain why — an agent that understands the mission improvises better than one following blind steps
3. Intelligence Placement
Scripts handle plumbing (fetch, transform, validate). Prompts handle judgment (interpret, classify, decide).
Test: If a script contains an if that decides what content means, intelligence has leaked.
Reverse test: If a prompt validates structure, counts items, parses known formats, compares against schemas, or checks file existence — determinism has leaked into the LLM. That work belongs in a script.
4. Progressive Disclosure
SKILL.md stays focused. Detail goes where it belongs.
- Capability instructions →
./references/ - Reference data, schemas, large tables →
./references/ - Templates, starter files →
./assets/ - Memory discipline →
./references/memory-system.md - Multi-capability SKILL.md under ~250 lines: fine as-is
- Single-purpose up to ~500 lines: acceptable if focused
5. Description Format
Two parts: [5-8 word summary]. [Use when user says 'X' or 'Y'.]
Default to conservative triggering. See ./references/standard-fields.md for full format.
6. Path Construction
Only use {project-root} for _bmad paths. Config variables used directly — they already contain {project-root}.
See ./references/standard-fields.md for correct/incorrect patterns.
7. Token Efficiency
Remove genuine waste (repetition, defensive padding, meta-explanation). Preserve context that enables judgment (persona voice, domain framing, theory of mind, design rationale). These are different things — never trade effectiveness for efficiency. A capability that works correctly but uses extra tokens is always better than one that's lean but fails edge cases.