worker-integration

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Original

English

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Translation

Chinese

Worker-Agent Integration Skill

Worker-Agent集成技能

Intelligent coordination between background workers and specialized agents.

实现后台工作进程与专用Agent之间的智能协调。

Quick Start

快速开始

bash

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bash

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View agent recommendations for a trigger

查看触发器对应的Agent推荐

npx agentic-flow workers agents ultralearn npx agentic-flow workers agents optimize

View performance metrics

查看性能指标

npx agentic-flow workers metrics

View integration stats

查看集成统计信息

npx agentic-flow workers stats --integration

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npx agentic-flow workers stats --integration

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Agent Mappings

Agent映射关系

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

工作进程会根据触发器类型自动调度至最优Agent：

触发器	首选Agent	备选Agent	流水线阶段
`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

基于性能的Agent选择

The system learns from execution history to improve agent selection:

typescript

// 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"

系统会从执行历史中学习，优化Agent选择机制：

typescript

// Agent选择会考虑以下因素：
// 1. 质量评分（0-1）
// 2. 成功率
// 3. 平均延迟
// 4. 执行次数

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

内存存储关键模式

Workers store results using consistent patterns:

{trigger}/{topic}/{phase}

Examples:
- ultralearn$auth-module$analysis
- optimize$database$performance
- audit$payment$vulnerabilities
- benchmark$api$metrics

工作进程采用统一模式存储结果：

{trigger}/{topic}/{phase}

示例:
- ultralearn$auth-module$analysis
- optimize$database$performance
- audit$payment$vulnerabilities
- benchmark$api$metrics

Benchmark Thresholds

性能基准阈值

Agents are monitored against performance thresholds:

json

{
  "researcher": {
    "p95_latency": "<500ms",
    "memory_mb": "<256MB"
  },
  "coder": {
    "p95_latency": "<300ms",
    "quality_score": ">0.85"
  },
  "security-analyst": {
    "scan_coverage": ">95%",
    "p95_latency": "<1000ms"
  }
}

系统会根据性能基准阈值监控Agent：

json

{
  "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

反馈循环机制

Workers provide feedback for continuous improvement:

typescript

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');

工作进程会提供反馈以实现持续优化：

typescript

import { workerAgentIntegration } from 'agentic-flow$workers$worker-agent-integration';

// 记录执行反馈
workerAgentIntegration.recordFeedback(
  'optimize',           // 触发器
  'coder',              // Agent
  true,                 // 是否成功
  245,                  // 延迟（毫秒）
  0.92                  // 质量评分
);

// 检查合规性
const { compliant, violations } = workerAgentIntegration.checkBenchmarkCompliance('coder');

Integration Statistics

集成统计信息

bash

$ 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%

bash

$ 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

配置说明

Enable integration features in

.claude$settings.json

json

{
  "workers": {
    "enabled": true,
    "parallel": true,
    "memoryDepositEnabled": true,
    "agentMappings": {
      "ultralearn": ["researcher", "coder"],
      "optimize": ["performance-analyzer", "coder"]
    }
  }
}

在

.claude$settings.json

中启用集成功能：

json

{
  "workers": {
    "enabled": true,
    "parallel": true,
    "memoryDepositEnabled": true,
    "agentMappings": {
      "ultralearn": ["researcher", "coder"],
      "optimize": ["performance-analyzer", "coder"]
    }
  }
}