performance▌

Optimize application performance end-to-end using specialized performance and optimization agents:

Frameworks for measuring, reporting, and optimizing marketing performance across channels and campaigns.

Help developers optimize website and web application performance to improve user experience, SEO rankings, and conversion rates. This skill provides systematic approaches to measure, analyze, and improve loading speed, runtime performance, and Core Web Vitals metrics.

You are a performance engineer specializing in modern application optimization, observability, and scalable system performance.

Performance-specific guidelines apply only to the hot path. Don't prematurely optimize—focus these patterns where they matter most.

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Identify render hotspots, isolate expensive updates, and apply targeted optimizations without changing UI behavior.

Systematic approach to measuring, analyzing, and optimizing application performance. \n \n Covers Core Web Vitals (LCP, INP, CLS) with target thresholds and measurement stages from development through production \n Provides a 4-step profiling workflow: establish baseline, identify bottleneck, apply fix, validate improvement \n Includes tool selection guidance for specific problems (Lighthouse for page load, DevTools for runtime and memory, bundle analyzers for code size) \n Documents common runt

Performance optimization patterns for Mapbox GL JS applications, prioritized by user impact. \n \n Eliminate initialization waterfalls by loading map data in parallel with map initialization, and set precise viewport to avoid redundant tile fetches \n Use symbol layers for 100+ markers (GPU-accelerated) and clustering for 10,000+ features; avoid HTML markers at scale \n Choose GeoJSON for datasets under 5 MB, vector tiles for 20+ MB; implement viewport-based loading to reduce bandwidth \n Always

Systematic approach to identifying and eliminating performance bottlenecks through measurement and targeted optimization. \n \n Emphasizes profiling first (flamegraph, perf, criterion) before optimizing; includes decision table mapping goals (reduce allocations, improve cache, parallelize) to specific implementation patterns \n Prioritizes optimization by impact: algorithm choice (10x–1000x), data structure (2x–10x), allocation reduction (2x–5x), cache optimization (1.5x–3x) \n Covers common tec