agentdb-advanced
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems…
Run comprehensive worker system benchmarks and performance analysis
$ npx -y skills add ruvnet/claude-flow --skill worker-benchmarks --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/worker-benchmarksContext preview
The summary Claude sees to decide when to auto-load this skill.
Run comprehensive worker system benchmarks and performance analysis
name: worker-benchmarks description: Run comprehensive worker system benchmarks and performance analysis user-invocable: true
Run comprehensive performance benchmarks for the agentic-flow worker system.
# Run full benchmark suite npx agentic-flow workers benchmark # Run specific benchmark npx agentic-flow workers benchmark --type trigger-detection npx agentic-flow workers benchmark --type registry npx agentic-flow workers benchmark --type agent-selection npx agentic-flow workers benchmark --type concurrent
Tests keyword detection speed across 12 worker triggers.
Tests CRUD operations on worker entries.
Tests performance-based agent selection.
Tests model caching performance.
Tests parallel worker creation and updates.
Tests memory pattern key generation.
═══════════════════════════════════════════════════════════ 📈 BENCHMARK RESULTS ═══════════════════════════════════════════════════════════ ✅ Trigger Detection Operation: detect Count: 1,000 Avg: 0.045ms | p95: 0.120ms (target: 5ms) Throughput: 22,222 ops/s Memory Δ: 0.12MB ✅ Worker Registry Operation: crud Count: 1,500 Avg: 1.234ms | p95: 3.456ms (target: 10ms) Throughput: 810 ops/s Memory Δ: 2.34MB ─────────────────────────────────────────────────────────── 📊 SUMMARY ─────────────────────────────────────────────────────────── Total Tests: 6 Passed: 6 | Failed: 0 Avg Latency: 0.567ms Total Duration: 2345ms Peak Memory: 8.90MB ═══════════════════════════════════════════════════════════
Benchmark thresholds are configured in `.claude/settings.json`:
{
"performance": {
"benchmarkThresholds": {
"triggerDetection": { "p95Ms": 5 },
"workerRegistry": { "p95Ms": 10 },
"agentSelection": { "p95Ms": 1 },
"memoryKeyGeneration": { "p95Ms": 0.1 },
"concurrentWorkers": { "totalMs": 1000 }
}
}
}import { workerBenchmarks, runBenchmarks } from 'agentic-flow/workers/worker-benchmarks';
// Run full suite
const suite = await runBenchmarks();
console.log(suite.summary);
// Run individual benchmarks
const triggerResult = await workerBenchmarks.benchmarkTriggerDetection(1000);
const registryResult = await workerBenchmarks.benchmarkRegistryOperations(500);1. **Model Cache**: Enable with `CLAUDE_FLOW_MODEL_CACHE_MB=512` 2. **Parallel Workers**: Enable with `CLAUDE_FLOW_WORKER_PARALLEL=true` 3. **Warning Suppression**: Enable with `CLAUDE_FLOW_SUPPRESS_WARNINGS=true` 4. **SQLite WAL Mode**: Automatic for better concurrent performance
An agent meta-harness for Claude Code and Codex. 📖 RuFlo Explained — Build an AI Team That Plans, Remembers, Tests, and Improves A 14-chapter guide: from the basic idea to a first useful task, then memory, agent teams, plugins, cost and verification.
Repo: ruvnet/claude-flow
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems…
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and…
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use…
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing…
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG…
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination