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Worker Integration

  • 96 installs
  • 67k repo stars
  • Updated August 4, 2026
  • ruvnet/claude-flow

Helps with ai & agent building tasks during AI-assisted development.

About

worker-integration is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted coding.

  • worker-integration
  • AI & Agent Building
  • AI-coding skill

Worker Integration by the numbers

  • 96 all-time installs (skills.sh)
  • Ranked #4,536 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/ruvnet/claude-flow --skill worker-integration

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Listed on Skillselion
Installs96
repo stars67k
Last updatedAugust 4, 2026
Repositoryruvnet/claude-flow

What it does

Helps with ai & agent building tasks during AI-assisted development.

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"]
    }
  }
}

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