ruvnet 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"]
}
}
}
Source: ruvnet/ruflo → .agents/skills/worker-integration/SKILL.md