Worker Integration
Worker-Agent integration for intelligent task dispatch and performance tracking
- Skill ID
- ruvnet/ruflo/worker-integration
- Publisher
- ruvnet
- Repository
- ruflo
- Installs
- 0
- Files
- 1
- Synced
- Sep 23, 2026
Open any RiverX project, open the Skills panel in the chat, and search for this identifier. The files are fetched from the source repository at install time.
ruvnet/ruflo/worker-integrationInstalls these files- SKILL.md
What this skill tells the agent
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 --integrationAgent 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$api$metricsBenchmark 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"]
}
}
}More from ruvnet
Use this skill in a project
Open any RiverX project, search the Skills panel for ruvnet/ruflo/worker-integration, and install it in one click.
