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>
This commit is contained in:
Consultoria AS
2026-03-03 07:04:14 +00:00
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# Automation Commands
Commands for automation operations in Claude Flow.
## Available Commands
- [auto-agent](./auto-agent.md)
- [smart-spawn](./smart-spawn.md)
- [workflow-select](./workflow-select.md)

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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

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# Self-Healing Workflows
## Purpose
Automatically detect and recover from errors without interrupting your flow.
## Self-Healing Features
### 1. Error Detection
Monitors for:
- Failed commands
- Syntax errors
- Missing dependencies
- Broken tests
### 2. Automatic Recovery
**Missing Dependencies:**
```
Error: Cannot find module 'express'
→ Automatically runs: npm install express
→ Retries original command
```
**Syntax Errors:**
```
Error: Unexpected token
→ Analyzes error location
→ Suggests fix through analyzer agent
→ Applies fix with confirmation
```
**Test Failures:**
```
Test failed: "user authentication"
→ Spawns debugger agent
→ Analyzes failure cause
→ Implements fix
→ Re-runs tests
```
### 3. Learning from Failures
Each recovery improves future prevention:
- Patterns saved to knowledge base
- Similar errors prevented proactively
- Recovery strategies optimized
**Pattern Storage:**
```javascript
// Store error patterns
mcp__claude-flow__memory_usage({
"action": "store",
"key": "error-pattern-" + Date.now(),
"value": JSON.stringify(errorData),
"namespace": "error-patterns",
"ttl": 2592000 // 30 days
})
// Analyze patterns
mcp__claude-flow__neural_patterns({
"action": "analyze",
"operation": "error-recovery",
"outcome": "success"
})
```
## Self-Healing Integration
### MCP Tool Coordination
```javascript
// Initialize self-healing swarm
mcp__claude-flow__swarm_init({
"topology": "star",
"maxAgents": 4,
"strategy": "adaptive"
})
// Spawn recovery agents
mcp__claude-flow__agent_spawn({
"type": "monitor",
"name": "Error Monitor",
"capabilities": ["error-detection", "recovery"]
})
// Orchestrate recovery
mcp__claude-flow__task_orchestrate({
"task": "recover from error",
"strategy": "sequential",
"priority": "critical"
})
```
### Fallback Hook Configuration
```json
{
"PostToolUse": [{
"matcher": "^Bash$",
"command": "npx claude-flow hook post-bash --exit-code '${tool.result.exitCode}' --auto-recover"
}]
}
```
## Benefits
- 🛡️ Resilient workflows
- 🔄 Automatic recovery
- 📚 Learns from errors
- ⏱️ Saves debugging time

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# Cross-Session Memory
## Purpose
Maintain context and learnings across Claude Code sessions for continuous improvement.
## Memory Features
### 1. Automatic State Persistence
At session end, automatically saves:
- Active agents and specializations
- Task history and patterns
- Performance metrics
- Neural network weights
- Knowledge base updates
### 2. Session Restoration
```javascript
// Using MCP tools for memory operations
mcp__claude-flow__memory_usage({
"action": "retrieve",
"key": "session-state",
"namespace": "sessions"
})
// Restore swarm state
mcp__claude-flow__context_restore({
"snapshotId": "sess-123"
})
```
**Fallback with npx:**
```bash
npx claude-flow hook session-restore --session-id "sess-123"
```
### 3. Memory Types
**Project Memory:**
- File relationships
- Common edit patterns
- Testing approaches
- Build configurations
**Agent Memory:**
- Specialization levels
- Task success rates
- Optimization strategies
- Error patterns
**Performance Memory:**
- Bottleneck history
- Optimization results
- Token usage patterns
- Efficiency trends
### 4. Privacy & Control
```javascript
// List memory contents
mcp__claude-flow__memory_usage({
"action": "list",
"namespace": "sessions"
})
// Delete specific memory
mcp__claude-flow__memory_usage({
"action": "delete",
"key": "session-123",
"namespace": "sessions"
})
// Backup memory
mcp__claude-flow__memory_backup({
"path": "./backups/memory-backup.json"
})
```
**Manual control:**
```bash
# View stored memory
ls .claude-flow/memory/
# Disable memory
export CLAUDE_FLOW_MEMORY_PERSIST=false
```
## Benefits
- 🧠 Contextual awareness
- 📈 Cumulative learning
- ⚡ Faster task completion
- 🎯 Personalized optimization

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# Smart Agent Auto-Spawning
## Purpose
Automatically spawn the right agents at the right time without manual intervention.
## Auto-Spawning Triggers
### 1. File Type Detection
When editing files, agents auto-spawn:
- **JavaScript/TypeScript**: Coder agent
- **Markdown**: Researcher agent
- **JSON/YAML**: Analyst agent
- **Multiple files**: Coordinator agent
### 2. Task Complexity
```
Simple task: "Fix typo"
→ Single coordinator agent
Complex task: "Implement OAuth with Google"
→ Architect + Coder + Tester + Researcher
```
### 3. Dynamic Scaling
The system monitors workload and spawns additional agents when:
- Task queue grows
- Complexity increases
- Parallel opportunities exist
**Status Monitoring:**
```javascript
// Check swarm health
mcp__claude-flow__swarm_status({
"swarmId": "current"
})
// Monitor agent performance
mcp__claude-flow__agent_metrics({
"agentId": "agent-123"
})
```
## Configuration
### MCP Tool Integration
Uses Claude Flow MCP tools for agent coordination:
```javascript
// Initialize swarm with appropriate topology
mcp__claude-flow__swarm_init({
"topology": "mesh",
"maxAgents": 8,
"strategy": "auto"
})
// Spawn agents based on file type
mcp__claude-flow__agent_spawn({
"type": "coder",
"name": "JavaScript Handler",
"capabilities": ["javascript", "typescript"]
})
```
### Fallback Configuration
If MCP tools are unavailable:
```bash
npx claude-flow hook pre-task --auto-spawn-agents
```
## Benefits
- 🤖 Zero manual agent management
- 🎯 Perfect agent selection
- 📈 Dynamic scaling
- 💾 Resource efficiency

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# smart-spawn
Intelligently spawn agents based on workload analysis.
## Usage
```bash
npx claude-flow automation smart-spawn [options]
```
## Options
- `--analyze` - Analyze before spawning
- `--threshold <n>` - Spawn threshold
- `--topology <type>` - Preferred topology
## Examples
```bash
# Smart spawn with analysis
npx claude-flow automation smart-spawn --analyze
# Set spawn threshold
npx claude-flow automation smart-spawn --threshold 5
# Force topology
npx claude-flow automation smart-spawn --topology hierarchical
```

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# workflow-select
Automatically select optimal workflow based on task type.
## Usage
```bash
npx claude-flow automation workflow-select [options]
```
## Options
- `--task <description>` - Task description
- `--constraints <list>` - Workflow constraints
- `--preview` - Preview without executing
## Examples
```bash
# Select workflow for task
npx claude-flow automation workflow-select --task "Deploy to production"
# With constraints
npx claude-flow automation workflow-select --constraints "no-downtime,rollback"
# Preview mode
npx claude-flow automation workflow-select --task "Database migration" --preview
```