worker-integration

# Worker-Agent Integration Skill

Intelligent coordination between background workers and specialized agents.

## Quick Start

```bash
# View agent recommendations for a trigger
npx agentic-flow workers agents ultralearn
npx agentic-flow workers agents optimize

# View performance metrics
npx agentic-flow workers metrics

# View integration stats
npx agentic-flow workers stats --integration
```

## Agent Mappings

Workers automatically dispatch to optimal agents based on trigger type:

| Trigger | Primary Agents | Fallback | Pipeline Phases |
|---------|---------------|----------|-----------------|
| `ultralearn` | researcher, coder | planner | discovery → patterns → vectorization → summary |
| `optimize` | performance-analyzer, coder | researcher | static-analysis → performance → patterns |
| `audit` | security-analyst, tester | reviewer | security → secrets → vulnerability-scan |
| `benchmark` | performance-analyzer | coder, tester | performance → metrics → report |
| `testgaps` | tester | coder | discovery → coverage → gaps |
| `document` | documenter, researcher | coder | api-discovery → patterns → indexing |
| `deepdive` | researcher, security-analyst | coder | call-graph → deps → trace |
| `refactor` | coder, reviewer | researcher | complexity → smells → patterns |

## Performance-Based Selection

The system learns from execution history to improve agent selection:

```typescript
// Agent selection considers:
// 1. Quality score (0-1)
// 2. Success rate
// 3. Average latency
// 4. Execution count

const { agent, confidence, reasoning } = selectBestAgent('optimize');
// agent: "performance-analyzer"
// confidence: 0.87
// reasoning: "Selected based on 45 executions with 94.2% success"
```

## Memory Key Patterns

Workers store results using consistent patterns:

```
{trigger}/{topic}/{phase}

Examples:
- ultralearn/auth-module/analysis
- optimize/database/performance
- audit/payment/vulnerabilities
- benchmark/api/metrics
```

## Benchmark Thresholds

Agents ar