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>
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Consultoria AS
2026-03-03 07:04:14 +00:00
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# auto agent
Automatically spawn and manage agents based on task requirements.
## Usage
```bash
npx claude-flow auto agent [options]
```
## Options
- `--task, -t <description>` - Task description for agent analysis
- `--max-agents, -m <number>` - Maximum agents to spawn (default: auto)
- `--min-agents <number>` - Minimum agents required (default: 1)
- `--strategy, -s <type>` - Selection strategy: optimal, minimal, balanced
- `--no-spawn` - Analyze only, don't spawn agents
## Examples
### Basic auto-spawning
```bash
npx claude-flow auto agent --task "Build a REST API with authentication"
```
### Constrained spawning
```bash
npx claude-flow auto agent -t "Debug performance issue" --max-agents 3
```
### Analysis only
```bash
npx claude-flow auto agent -t "Refactor codebase" --no-spawn
```
### Minimal strategy
```bash
npx claude-flow auto agent -t "Fix bug in login" -s minimal
```
## How It Works
1. **Task Analysis**
- Parses task description
- Identifies required skills
- Estimates complexity
- Determines parallelization opportunities
2. **Agent Selection**
- Matches skills to agent types
- Considers task dependencies
- Optimizes for efficiency
- Respects constraints
3. **Topology Selection**
- Chooses optimal swarm structure
- Configures communication patterns
- Sets up coordination rules
- Enables monitoring
4. **Automatic Spawning**
- Creates selected agents
- Assigns specific roles
- Distributes subtasks
- Initiates coordination
## Agent Types Selected
- **Architect**: System design, architecture decisions
- **Coder**: Implementation, code generation
- **Tester**: Test creation, quality assurance
- **Analyst**: Performance, optimization
- **Researcher**: Documentation, best practices
- **Coordinator**: Task management, progress tracking
## Strategies
### Optimal
- Maximum efficiency
- May spawn more agents
- Best for complex tasks
- Highest resource usage
### Minimal
- Minimum viable agents
- Conservative approach
- Good for simple tasks
- Lowest resource usage
### Balanced
- Middle ground
- Adaptive to complexity
- Default strategy
- Good performance/resource ratio
## Integration with Claude Code
```javascript
// In Claude Code after auto-spawning
mcp__claude-flow__auto_agent {
task: "Build authentication system",
strategy: "balanced",
maxAgents: 6
}
```
## See Also
- `agent spawn` - Manual agent creation
- `swarm init` - Initialize swarm manually
- `smart spawn` - Intelligent agent spawning
- `workflow select` - Choose predefined workflows