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Consultoria AS 79b5d86325 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>
2026-03-03 07:04:14 +00:00

2.5 KiB

auto agent

Automatically spawn and manage agents based on task requirements.

Usage

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

npx claude-flow auto agent --task "Build a REST API with authentication"

Constrained spawning

npx claude-flow auto agent -t "Debug performance issue" --max-agents 3

Analysis only

npx claude-flow auto agent -t "Refactor codebase" --no-spawn

Minimal strategy

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

// 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