feat(ai): implementar DORA 2025 AI Capability 6 - AI-accessible Data - TASK-025

Implementacion completa de AI-accessible Internal Data:

Data Catalog Engine:
- DataCatalog class con 4 datasets catalogados:
  * dora_metrics (time_series, real_time)
  * deployment_cycles (aggregated, real_time)
  * performance_metrics (time_series, 5_minutes)
  * quality_metrics (aggregated, daily)

- DataQueryEngine con query capabilities:
  * query_dora_metrics() - flexible filtering
  * query_deployment_cycles() - deployment analysis
  * get_aggregated_stats() - comprehensive stats

API Endpoints (AI-friendly):
- GET /api/dora/data-catalog/
  * Complete catalog with schemas
  * Self-describing APIs
  * Example queries included

- GET /api/dora/data-catalog/dora-metrics/
  * Query params: days, phase_name, feature_id
  * Metadata-rich responses
  * JSON format optimized for AI

- GET /api/dora/data-catalog/deployment-cycles/
  * Query params: days, failed_only
  * Aggregated cycle information

- GET /api/dora/data-catalog/aggregated-stats/
  * Query params: days
  * Complete DORA statistics
  * By phase, by decision breakdowns

Features:
- Self-describing schemas
- Metadata-rich responses (query, metadata, data)
- Flexible querying (time, phase, feature filters)
- AI-friendly JSON format
- Rate limiting applied (100/min, 1000/hour)
- Example usage for AI agents
- Comprehensive documentation

Archivos:
- dora_metrics/data_catalog.py (nuevo)
- dora_metrics/views.py (actualizado)
- dora_metrics/urls.py (actualizado)
- docs/ai_capabilities/TASK-025-dora-ai-capability-6.md

Story Points: 8 SP
Estado: COMPLETADO
DORA 2025 AI Capability: 6/7 (86%)

Author: claude