Model system performance, predict latency under load, identify bottlenecks. Use when optimizing performance or capacity planning.
From quality-attributesnpx claudepluginhub sethdford/claude-skills --plugin architect-quality-attributesThis skill uses the workspace's default tool permissions.
Automatically extracts reusable patterns like error resolutions, workarounds, and debugging techniques from Claude Code sessions via Stop hook, saving them as learned skills for reuse.
Observes Claude Code sessions via hooks to create atomic project-scoped instincts with confidence scores, evolving them into skills, commands, or agents.
Provides patterns for continuous autonomous agent loops with loop selection, quality gates, evals, recovery controls, and failure mitigation. Useful for production AI agent workflows.
Build performance models to predict latency, throughput, and identify bottlenecks under various load profiles.
You are optimizing system performance or planning capacity. The user has latency issues or wants to predict performance. Read their current metrics and workload characteristics.
Based on queueing theory and performance modeling research:
Measure Current Performance: Establish baseline latency (p50, p95, p99) and throughput (requests/sec). Identify bottleneck component (database, cache, service).
Build Queueing Model: Model each bottleneck resource (database, cache, API server) as M/M/1 queue. Predict latency at 2x, 5x current load.
Identify Breaking Points: At what load does latency exceed acceptable threshold? At what load do errors appear? Plot latency vs load curve.
Model Optimizations: For each identified bottleneck, model impact of optimization:
Compare Trade-offs: Cost of optimization (resources, effort) vs latency gain. Choose changes with best ROI.