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"] } } }