- FASE-0: Diagnostic audit of 500+ files, 33 findings cataloged (7P0/8P1/12P2/6P3) - FASE-1: Resolved 7 P0 critical conflicts (ports, paths, dedup OQI-010/ADR-002, orphan schemas) - FASE-2: Resolved 8 P1 issues (traces, README/CLAUDE.md, DEPENDENCY-GRAPH v2.0, DDL drift, stack versions, DoR/DoD) - FASE-3: Resolved 12 P2 issues (archived tasks indexed, RNFs created, OQI-010 US/RF/ET, AGENTS v2.0) - FASE-4: Purged 3 obsolete docs to _archive/, fixed MODELO-NEGOCIO.md broken ref - FASE-5: Cross-layer validation (DDL→OQI 66%, OQI→BE 72%, BE→FE 78%, Inventories 95%) - FASE-6: INFORME-FINAL, SA-INDEX (18 subagents), METADATA COMPLETED 27/33 findings resolved (82%), 6 P3 deferred to backlog. 18 new files created, 40+ modified, 4 archived. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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| id | title | type | status | priority | epic | story_points | created_date |
|---|---|---|---|---|---|---|---|
| US-LTI-003 | Interpretar Senales ML en Lenguaje Natural | User Story | Backlog | Media | OQI-010 | 5 | 2026-02-06 |
US-LTI-003: Interpretar Senales ML en Lenguaje Natural
Como
Un usuario del modulo educativo
Quiero
Que el copiloto me explique las senales ML en terminos simples
Para
Entender por que el modelo sugiere una operacion y aprender trading
Criterios de Aceptacion
- El LLM traduce confidence scores a niveles de confianza legibles
- Explica factores que generaron la senal (indicadores, patrones)
- Ofrece contexto educativo cuando se detecta usuario principiante
- Incluye disclaimers de riesgo apropiados