Now liveThe Skillselion MCP - thousands of ranked skills, loaded into your agent mid-task. No install.Get it →
ruvnet avatar

Worker Integration

  • 996 installs
  • 67k repo stars
  • Updated August 4, 2026
  • ruvnet/ruflo

worker-integration is a version 1.0.0 agentic-flow skill that routes background worker tasks to specialized agents with performance tracking and self-learning for developers building coordinated agent dispatch pipelines.

About

worker-integration is a version 1.0.0 invocable ruvnet/ruflo skill for Worker-Agent integration in agentic-flow. It provides intelligent task dispatch from background workers to specialized agents, with performance_tracking, memory_coordination, agent_selection, and self_learning capabilities. Developers use npx agentic-flow workers agents for trigger-specific recommendations such as ultralearn or optimize, npx agentic-flow workers metrics for performance data, and npx agentic-flow workers stats --integration for coordination stats. Reach for worker-integration when worker triggers need automated agent routing instead of hard-coded handler maps.

  • Intelligent agent selection based on trigger type
  • Performance tracking and metrics dashboard
  • Memory coordination across worker-agent handoffs
  • Self-learning capabilities that improve dispatch over time
  • 8 predefined trigger-to-agent mappings with fallback logic

Worker Integration by the numbers

  • 996 all-time installs (skills.sh)
  • +3 installs in the week ending Aug 5, 2026 (Skillselion tracking)
  • Ranked #1,093 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
  • Security screen: LOW risk (skills.sh audit)
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/ruvnet/ruflo --skill worker-integration

Add your badge

Show developers this skill is listed on Skillselion. Paste this into your README.

Listed on Skillselion
Installs996
repo stars67k
Security audit3 / 3 scanners passed
Last updatedAugust 4, 2026
Repositoryruvnet/ruflo

How do you route worker tasks to specialized agents?

Automatically route tasks from background workers to the best specialized agents while tracking performance and enabling self-learning.

Who is it for?

Developers building agentic-flow pipelines who need intelligent worker-to-agent dispatch with tracked performance.

Skip if: Static cron scripts without agent selection or projects outside the agentic-flow worker system.

When should I use this skill?

A developer needs worker-agent routing, agent recommendations per trigger, or integration metrics for agentic-flow background workers.

What you get

Worker-agent routing maps, performance metrics, integration stats, and self-learning dispatch recommendations.

  • Worker-agent routing configuration
  • Performance and integration metrics output

By the numbers

  • Version 1.0.0 invocable skill with 4 capabilities: agent_selection, performance_tracking, memory_coordination, self_lear
  • Documents 3 CLI command families: workers agents, workers metrics, and workers stats --integration

Files

SKILL.mdMarkdownGitHub ↗

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:

TriggerPrimary AgentsFallbackPipeline Phases
ultralearnresearcher, coderplannerdiscovery → patterns → vectorization → summary
optimizeperformance-analyzer, coderresearcherstatic-analysis → performance → patterns
auditsecurity-analyst, testerreviewersecurity → secrets → vulnerability-scan
benchmarkperformance-analyzercoder, testerperformance → metrics → report
testgapstestercoderdiscovery → coverage → gaps
documentdocumenter, researchercoderapi-discovery → patterns → indexing
deepdiveresearcher, security-analystcodercall-graph → deps → trace
refactorcoder, reviewerresearchercomplexity → 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"]
    }
  }
}

Related skills

How it compares

Use worker-integration when agentic-flow workers need dynamic agent routing and tracked metrics rather than static handler assignment.

FAQ

How does worker-integration pick agents?

worker-integration uses agentic-flow agent_selection to map worker triggers to specialized agents; developers inspect recommendations with npx agentic-flow workers agents followed by a trigger name such as ultralearn or optimize.

What metrics does worker-integration expose?

worker-integration exposes performance_tracking through npx agentic-flow workers metrics and integration coordination stats via npx agentic-flow workers stats --integration, supporting memory_coordination and self_learning over time.

Is Worker Integration safe to install?

skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.

AI & Agent Buildingagentsautomation

This week in AI coding

Five minutes, every Monday - the tools, releases and tactics for developers.

unsubscribe anytime.