Worker-Agent Integration Skill

Intelligent coordination between background workers and specialized agents.

Quick Start

# 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:

// 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 are monitored against performance thresholds:

{
  "researcher": {
    "p95_latency": "<500ms",
    "memory_mb": "<256MB"
  },
  "coder": {
    "p95_latency": "<300ms",
    "quality_score": ">0.85"
  },
  "security-analyst": {
    "scan_coverage": ">95%",
    "p95_latency": "<1000ms"
  }
}

Feedback Loop

Workers provide feedback for continuous improvement:

import { workerAgentIntegration } from 'agentic-flow/workers/worker-agent-integration';

// Record execution feedback
workerAgentIntegration.recordFeedback(
  'optimize',           // trigger
  'coder',              // agent
  true,                 // success
  245,                  // latency ms
  0.92                  // quality score
);

// Check compliance
const { compliant, violations } = workerAgentIntegration.checkBenchmarkCompliance('coder');

Integration Statistics

$ npx agentic-flow workers stats --integration

Worker-Agent Integration Stats
══════════════════════════════
Total Agents:       6
Tracked Agents:     4
Total Feedback:     156
Avg Quality Score:  0.89

Model Cache Stats
─────────────────
Hits:     1,234
Misses:   45
Hit Rate: 96.5%

Configuration

Enable integration features in .claude/settings.json:

{
  "workers": {
    "enabled": true,
    "parallel": true,
    "memoryDepositEnabled": true,
    "agentMappings": {
      "ultralearn": ["researcher", "coder"],
      "optimize": ["performance-analyzer", "coder"]
    }
  }
}

🔗 Связи

• [[MOC - System]] — System
• [[000 - Map of Maps]] — Map of Maps