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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