Worker-Agent integration for intelligent task dispatch and performance tracking
npxskills add ruvnet/claude-flow--skill worker-integrationLoading…
Worker-Agent integration for intelligent task dispatch and performance tracking
npxskills add ruvnet/claude-flow--skill worker-integrationLoading…
Intelligent coordination between background workers and specialized agents.
# View agent recommendations for a trigger
npx agentic-flow workers agents ultralearn
npx agentic-flow workers agents optimize
# View performance metrics
npx agentic-flow workers metrics
# View integration stats
npx agentic-flow workers stats --integration
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 |
The system learns from execution history to improve agent selection:
// 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"
Workers store results using consistent patterns:
{trigger}/{topic}/{phase}
Examples:
- ultralearn$auth-module$analysis
- optimize$database$performance
- audit$payment$vulnerabilities
- benchmark$api$metrics
Agents are monitored against performance thresholds:
{
"researcher": {
"p95_latency": "<500ms",
"memory_mb": "<256MB"
},
"coder": {
"p95_latency": "<300ms",
"quality_score": ">0.85"
},
"security-analyst": {
"scan_coverage": ">95%",
"p95_latency": "<1000ms"
}
}
Workers provide feedback for continuous improvement:
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');
$ 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%
Enable integration features in .claude$settings.json:
{
"workers": {
"enabled": true,
"parallel": true,
"memoryDepositEnabled": true,
"agentMappings": {
"ultralearn": ["researcher", "coder"],
"optimize": ["performance-analyzer", "coder"]
}
}
}
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Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
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Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).