Worker Integration

ruvnet/ruflo/.agents/skills/worker-integration

作者 ruvnet6051f6702b61無授權條款74K 個星標收錄於 2026年10月8日更新於 2026年10月8日儲存庫今天更新

Worker-Agent integration for intelligent task dispatch and performance tracking

僅含說明AI & Agents

AI 產生的概覽

協調背景工作器與專用代理，將觸發條件對應到代理並追蹤效能。

功能: 此技能定義背景工作器如何依據 ultralearn、optimize、audit、benchmark、testgaps、document、deepdive 與 refactor 等觸發類型，將任務分派給專用代理。它規範了代理對應（含主要代理與備援代理）、管線階段、記憶體鍵模式、基準閾值，以及用來記錄執行結果的回饋迴圈。它也記錄了 agentic-flow 工作器系統的設定選項與整合統計資訊。
適用情境: 在設定或分析 agentic-flow 工作器如何針對不同任務觸發條件選擇與協調代理時使用此技能。它適用於為工作器與代理整合設定代理對應、效能追蹤或基準合規性。
執行需求: 需要 agentic-flow CLI 及其工作器子系統，以及用於設定的 .claude 設定檔。不包含指令碼，僅為說明文件。

Worker-Agent Integration Skill

Intelligent coordination between background workers and specialized agents.

Quick Start

bash

# View agent recommendations for a triggernpx agentic-flow workers agents ultralearnnpx agentic-flow workers agents optimize
# View performance metricsnpx agentic-flow workers metrics
# View integration statsnpx agentic-flow workers stats --integration

# View agent recommendations for a triggernpx agentic-flow workers agents ultralearnnpx agentic-flow workers agents optimize
# View performance metricsnpx agentic-flow workers metrics
# View integration statsnpx agentic-flow workers stats --integration

Agent 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:

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"

// 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

{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:

json

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

{  "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:

typescript

import { workerAgentIntegration } from 'agentic-flow$workers$worker-agent-integration';
// Record execution feedbackworkerAgentIntegration.recordFeedback(  'optimize',           // trigger  'coder',              // agent  true,                 // success  245,                  // latency ms  0.92                  // quality score);
// Check complianceconst { compliant, violations } = workerAgentIntegration.checkBenchmarkCompliance('coder');

import { workerAgentIntegration } from 'agentic-flow$workers$worker-agent-integration';
// Record execution feedbackworkerAgentIntegration.recordFeedback(  'optimize',           // trigger  'coder',              // agent  true,                 // success  245,                  // latency ms  0.92                  // quality score);
// Check complianceconst { compliant, violations } = workerAgentIntegration.checkBenchmarkCompliance('coder');

Integration Statistics

bash

$ npx agentic-flow workers stats --integration
Worker-Agent Integration Stats══════════════════════════════Total Agents:       6Tracked Agents:     4Total Feedback:     156Avg Quality Score:  0.89
Model Cache Stats─────────────────Hits:     1,234Misses:   45Hit Rate: 96.5%

$ npx agentic-flow workers stats --integration
Worker-Agent Integration Stats══════════════════════════════Total Agents:       6Tracked Agents:     4Total Feedback:     156Avg Quality Score:  0.89
Model Cache Stats─────────────────Hits:     1,234Misses:   45Hit Rate: 96.5%

Configuration

Enable integration features in .claude$settings.json:

json

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

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

來源與署名

來源：ruvnet/ruflo位於.agents/skills/worker-integration提交6051f67

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檢舉或申請下架

Worker Integration

Worker-Agent Integration Skill

Quick Start

Agent Mappings

Performance-Based Selection

Memory Key Patterns

Benchmark Thresholds

Feedback Loop

Integration Statistics

Configuration

來源與署名

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