Worker Integration

作者 ruvnet60de638630ab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

协调后台工作进程与专用代理，将触发类型映射到代理并跟踪性能。

功能: 该技能说明后台工作进程如何根据触发类型（如 ultralearn、optimize、audit、benchmark、testgaps、document、deepdive 和 refactor）将任务分派给专用代理。它记录了基于质量分数、成功率、延迟和执行次数的性能化代理选择，以及用于存储结果的内存键模式。它还涵盖基准阈值、用于记录执行结果的反馈循环、集成统计信息和配置选项。
适用场景: 在 agentic-flow 环境中设置或分析工作进程到代理的任务分派与代理选择时使用此技能。它也适用于监控代理性能、基准合规性或集成统计信息。
运行要求: 需要 agentic-flow CLI 及其 worker-agent 集成模块，通过 npx 调用。配置放在 .claude/settings.json 中。该技能不附带脚本，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.claude/skills/worker-integration提交60de638

许可证：无许可证

内容归原作者所有。SourceWeft 从公开仓库中收录这些内容。

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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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