performance▌

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

If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.

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

Systematic approach to measuring, analyzing, and improving website loading speed and Core Web Vitals. \n \n Covers five-step optimization workflow: establish baseline metrics with Lighthouse, identify bottlenecks (bundle size, images, render-blocking resources), prioritize high-impact fixes, implement improvements, and verify results \n Addresses Core Web Vitals (LCP, FID, CLS) with specific code examples for image optimization, code splitting, lazy loading, and layout shift prevention \n Includ

Diagnose and fix SwiftUI rendering bottlenecks through code review, Instruments profiling, and targeted remediation. \n \n Covers view invalidation storms, unstable list identity, expensive body computations, layout thrash, and image decoding issues with concrete code examples and fixes \n Includes step-by-step Instruments profiling workflow using the SwiftUI template to identify high body-evaluation counts and CPU hotspots \n Explains identity and lifetime mechanics, lazy loading patterns, and

Audit and fix animation performance issues including layout thrashing, compositor properties, and scroll-linked motion. \n \n Covers nine rule categories by priority: never patterns, mechanism selection, measurement batching, scroll-linked motion, paint optimization, layer promotion, blur/filter constraints, view transitions, and tool boundaries \n Provides file-level review mode that identifies violations with exact line quotes, impact explanation, and concrete code fixes \n Includes FLIP-style

Comprehensive SEO and GEO performance reports combining rankings, traffic, backlinks, and AI visibility metrics. \n \n Aggregates data from multiple sources (analytics, search console, SEO tools, AI monitors) into executive summaries, detailed analyses, and visual presentations \n Generates 11-section reports covering organic traffic, keyword rankings, domain authority, content quality (CORE-EEAT), backlinks, and GEO/AI citation performance \n Includes period-over-period trend analysis, benchmar