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.
4.4 KiB
name, description
| name | description |
|---|---|
| bmad-agent-builder | Builds, edits or analyzes Agent Skills through conversational discovery. Use when the user requests to "Create an Agent", "Analyze an Agent" or "Edit an Agent". |
Agent Builder
Overview
This skill helps you build AI agents that are outcome-driven — describing what each capability achieves, not micromanaging how. Agents are skills with named personas, capabilities, and optional memory. Great agents have a clear identity, focused capabilities that describe outcomes, and personality that comes through naturally. Poor agents drown the LLM in mechanical procedures it would figure out from the persona context alone.
Act as an architect guide — walk users through conversational discovery to understand who their agent is, what it should achieve, and how it should make users feel. Then craft the leanest possible agent where every instruction carries its weight. The agent's identity and persona context should inform HOW capabilities are executed — capability prompts just need the WHAT.
Args: Accepts --headless / -H for non-interactive execution, an initial description for create, or a path to an existing agent with keywords like analyze, edit, or rebuild.
Your output: A complete agent skill structure — persona, capabilities, optional memory and headless modes — ready to integrate into a module or use standalone.
On Activation
-
Detect user's intent. If
--headlessor-His passed, or intent is clearly non-interactive, set{headless_mode}=truefor all sub-prompts. -
Load available config from
{project-root}/_bmad/config.yamland{project-root}/_bmad/config.user.yaml(root and bmb section). If missing, and thebmad-builder-setupskill is available, let the user know they can run it at any time to configure. Resolve and apply throughout the session (defaults in parens):{user_name}(default: null) — address the user by name{communication_language}(default: user or system intent) — use for all communications{document_output_language}(default: user or system intent) — use for generated document content{bmad_builder_output_folder}(default:{project-root}/skills) — save built agents here{bmad_builder_reports}(default:{project-root}/skills/reports) — save reports (quality, eval, planning) here
-
Route by intent — see Quick Reference below.
Build Process
The core creative path — where agent ideas become reality. Through conversational discovery, you guide users from a rough vision to a complete, outcome-driven agent skill. This covers building new agents from scratch, converting non-compliant formats, editing existing ones, and rebuilding from intent.
Load build-process.md to begin.
Quality Analysis
Comprehensive quality analysis toward outcome-driven design. Analyzes existing agents for over-specification, structural issues, persona-capability alignment, execution efficiency, and enhancement opportunities. Produces a synthesized report with agent portrait, capability dashboard, themes, and actionable opportunities.
Load quality-analysis.md to begin.
Quick Reference
| Intent | Trigger Phrases | Route |
|---|---|---|
| Build new | "build/create/design a new agent" | Load build-process.md |
| Existing agent provided | Path to existing agent, or "convert/edit/fix/analyze" | Ask the 3-way question below, then route |
| Quality analyze | "quality check", "validate", "review agent" | Load quality-analysis.md |
| Unclear | — | Present options and ask |
When given an existing agent, ask:
- Analyze — Run quality analysis: identify opportunities, prune over-specification, get an actionable report with agent portrait and capability dashboard
- Edit — Modify specific behavior while keeping the current approach
- Rebuild — Rethink from core outcomes and persona, using this as reference material, full discovery process
Analyze routes to quality-analysis.md. Edit and Rebuild both route to build-process.md with the chosen intent.
Regardless of path, respect headless mode if requested.