Worker Integration

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

作者 ruvnet60de638630ab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

協調背景工作程序與專用代理，將觸發類型對應到代理並追蹤效能。

功能: 此技能說明背景工作程序如何依觸發類型（例如 ultralearn、optimize、audit、benchmark、testgaps、document、deepdive 與 refactor）將任務分派給專用代理。它記錄了以品質分數、成功率、延遲與執行次數為基礎的效能化代理選擇，以及用於儲存結果的記憶體鍵模式。內容也涵蓋基準閾值、用於記錄執行結果的回饋迴圈、整合統計資料與設定選項。
適用情境: 在 agentic-flow 環境中設定或分析工作程序到代理的任務分派與代理選擇時使用此技能。它也適合用來監控代理效能、基準合規性或整合統計資料。
執行需求: 需要 agentic-flow CLI 及其 worker-agent 整合模組，透過 npx 呼叫。設定放在 .claude/settings.json 中。此技能不附帶指令碼，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.claude/skills/worker-integration提交60de638

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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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