Worker Integration

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

作者 ruvnet6051f6702b61无许可证74K 个星标收录于 2026年10月8日更新于 2026年10月8日仓库今天更新

Worker-Agent integration for intelligent task dispatch and performance tracking

仅含说明AI & Agents

AI 生成的概览

协调后台工作器与专用代理，将触发条件映射到代理并跟踪性能。

功能: 该技能定义了后台工作器如何根据 ultralearn、optimize、audit、benchmark、testgaps、document、deepdive 和 refactor 等触发类型将任务分派给专用代理。它规定了代理映射（含主代理与备用代理）、流水线阶段、内存键模式、基准阈值以及用于记录执行结果的反馈循环。它还记录了 agentic-flow 工作器系统的配置选项和集成统计信息。
适用场景: 在配置或分析 agentic-flow 工作器如何为不同任务触发条件选择与协调代理时使用此技能。它适用于为工作器-代理集成设置代理映射、性能跟踪或基准合规性。
运行要求: 需要 agentic-flow CLI 及其工作器子系统，以及用于配置的 .claude 设置文件。不包含脚本，仅为说明文档。

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"]    }  }}

来源与署名

来源：ruvnet/ruflo位于.agents/skills/worker-integration提交6051f67

许可证：无许可证

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举报或申请下架

Worker Integration

Worker-Agent Integration Skill

Quick Start

Agent Mappings

Performance-Based Selection

Memory Key Patterns

Benchmark Thresholds

Feedback Loop

Integration Statistics

Configuration

来源与署名

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