feat: CRM Clinicas SaaS - MVP completo

- Auth: Login/Register con creacion de clinica
- Dashboard: KPIs reales, graficas recharts
- Pacientes: CRUD completo con busqueda
- Agenda: FullCalendar, drag-and-drop, vista recepcion
- Expediente: Notas SOAP, signos vitales, CIE-10
- Facturacion: Facturas con IVA, campos CFDI SAT
- Inventario: Productos, stock, movimientos, alertas
- Configuracion: Clinica, equipo, catalogo servicios
- Supabase self-hosted: 18 tablas con RLS multi-tenant
- Docker + Nginx para produccion

Co-Authored-By: claude-flow <ruv@ruv.net>

.claude/skills/v3-memory-unification/SKILL.md

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+---
+name: "V3 Memory Unification"
+description: "Unify 6+ memory systems into AgentDB with HNSW indexing for 150x-12,500x search improvements. Implements ADR-006 (Unified Memory Service) and ADR-009 (Hybrid Memory Backend)."
+---
+
+# V3 Memory Unification
+
+## What This Skill Does
+
+Consolidates disparate memory systems into unified AgentDB backend with HNSW vector search, achieving 150x-12,500x search performance improvements while maintaining backward compatibility.
+
+## Quick Start
+
+```bash
+# Initialize memory unification
+Task("Memory architecture", "Design AgentDB unification strategy", "v3-memory-specialist")
+
+# AgentDB integration
+Task("AgentDB setup", "Configure HNSW indexing and vector search", "v3-memory-specialist")
+
+# Data migration
+Task("Memory migration", "Migrate SQLite/Markdown to AgentDB", "v3-memory-specialist")
+```
+
+## Systems to Unify
+
+### Legacy Systems → AgentDB
+```
+┌─────────────────────────────────────────┐
+│  • MemoryManager (basic operations)     │
+│  • DistributedMemorySystem (clustering) │
+│  • SwarmMemory (agent-specific)         │
+│  • AdvancedMemoryManager (features)     │
+│  • SQLiteBackend (structured)           │
+│  • MarkdownBackend (file-based)         │
+│  • HybridBackend (combination)          │
+└─────────────────────────────────────────┘
+                    ↓
+┌─────────────────────────────────────────┐
+│       🚀 AgentDB with HNSW             │
+│  • 150x-12,500x faster search          │
+│  • Unified query interface             │
+│  • Cross-agent memory sharing          │
+│  • SONA learning integration           │
+└─────────────────────────────────────────┘
+```
+
+## Implementation Architecture
+
+### Unified Memory Service
+```typescript
+class UnifiedMemoryService implements IMemoryBackend {
+  constructor(
+    private agentdb: AgentDBAdapter,
+    private indexer: HNSWIndexer,
+    private migrator: DataMigrator
+  ) {}
+
+  async store(entry: MemoryEntry): Promise<void> {
+    await this.agentdb.store(entry);
+    await this.indexer.index(entry);
+  }
+
+  async query(query: MemoryQuery): Promise<MemoryEntry[]> {
+    if (query.semantic) {
+      return this.indexer.search(query); // 150x-12,500x faster
+    }
+    return this.agentdb.query(query);
+  }
+}
+```
+
+### HNSW Vector Search
+```typescript
+class HNSWIndexer {
+  constructor(dimensions: number = 1536) {
+    this.index = new HNSWIndex({
+      dimensions,
+      efConstruction: 200,
+      M: 16,
+      speedupTarget: '150x-12500x'
+    });
+  }
+
+  async search(query: MemoryQuery): Promise<MemoryEntry[]> {
+    const embedding = await this.embedContent(query.content);
+    const results = this.index.search(embedding, query.limit || 10);
+    return this.retrieveEntries(results);
+  }
+}
+```
+
+## Migration Strategy
+
+### Phase 1: Foundation
+```typescript
+// AgentDB adapter setup
+const agentdb = new AgentDBAdapter({
+  dimensions: 1536,
+  indexType: 'HNSW',
+  speedupTarget: '150x-12500x'
+});
+```
+
+### Phase 2: Data Migration
+```typescript
+// SQLite → AgentDB
+const migrateFromSQLite = async () => {
+  const entries = await sqlite.getAll();
+  for (const entry of entries) {
+    const embedding = await generateEmbedding(entry.content);
+    await agentdb.store({ ...entry, embedding });
+  }
+};
+
+// Markdown → AgentDB
+const migrateFromMarkdown = async () => {
+  const files = await glob('**/*.md');
+  for (const file of files) {
+    const content = await fs.readFile(file, 'utf-8');
+    await agentdb.store({
+      id: generateId(),
+      content,
+      embedding: await generateEmbedding(content),
+      metadata: { originalFile: file }
+    });
+  }
+};
+```
+
+## SONA Integration
+
+### Learning Pattern Storage
+```typescript
+class SONAMemoryIntegration {
+  async storePattern(pattern: LearningPattern): Promise<void> {
+    await this.memory.store({
+      id: pattern.id,
+      content: pattern.data,
+      metadata: {
+        sonaMode: pattern.mode,
+        reward: pattern.reward,
+        adaptationTime: pattern.adaptationTime
+      },
+      embedding: await this.generateEmbedding(pattern.data)
+    });
+  }
+
+  async retrieveSimilarPatterns(query: string): Promise<LearningPattern[]> {
+    return this.memory.query({
+      type: 'semantic',
+      content: query,
+      filters: { type: 'learning_pattern' }
+    });
+  }
+}
+```
+
+## Performance Targets
+
+- **Search Speed**: 150x-12,500x improvement via HNSW
+- **Memory Usage**: 50-75% reduction through optimization
+- **Query Latency**: <100ms for 1M+ entries
+- **Cross-Agent Sharing**: Real-time memory synchronization
+- **SONA Integration**: <0.05ms adaptation time
+
+## Success Metrics
+
+- [ ] All 7 legacy memory systems migrated to AgentDB
+- [ ] 150x-12,500x search performance validated
+- [ ] 50-75% memory usage reduction achieved
+- [ ] Backward compatibility maintained
+- [ ] SONA learning patterns integrated
+- [ ] Cross-agent memory sharing operational