WO

worker-integration

Automates task dispatch by selecting specialized agents based on performance and trigger types.

Install

mkdir -p .claude/skills/worker-integration && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/6812" && unzip -o skill.zip -d .claude/skills/worker-integration && rm skill.zip

Installs to .claude/skills/worker-integration

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Worker-Agent integration for intelligent task dispatch and performance tracking
79 charsno explicit “when” trigger
Intermediate

Key capabilities

  • Dispatch tasks to specialized agents
  • Track agent performance metrics
  • Automate workflow pipeline phases
  • Coordinate agent memory
  • Perform self-learning

How it works

Uses execution history and performance metrics to dispatch tasks to the most suitable agent.

Inputs & outputs

You give it
Task trigger
You get back
Optimal agent assignment

When to use worker-integration

  • Dispatch tasks to specialized agents
  • Track agent performance metrics
  • Automate workflow pipeline phases

About this skill

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"]
    }
  }
}

When not to use it

  • Workflows without background workers

Prerequisites

npxagentic-flow package

Limitations

  • Requires execution history for optimal selection

How it compares

Automates agent selection based on historical success rates rather than manual assignment.

Compared to similar skills

worker-integration side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
worker-integration (this skill)12moReviewIntermediate
using-superpowers953moNo flagsBeginner
ultrawork112moNo flagsAdvanced
clawhub252moReviewIntermediate

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Example prompts that trigger this skill in your AI assistant.

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