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 are monitored against performance thresholds: ```json { "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: ```typescript 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 ```bash $ 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`: ```json { "workers": { "enabled": true, "parallel": true, "memoryDepositEnabled": true, "agentMappings": { "ultralearn": ["researcher", "coder"], "optimize": ["performance-analyzer", "coder"] } } } ```