performance▌

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

Apply when optimizing GSAP animations for smooth 60fps, reducing layout/paint cost, or when the user asks about performance, jank, or best practices for fast animations.

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

Diagnose and fix performance bottlenecks across React frontends and database backends. \n \n Covers frontend optimization: React.memo, useMemo, useCallback, lazy loading, code splitting, image optimization, and bundle analysis \n Includes backend strategies: N+1 query fixes, database indexing, Redis caching, and API compression \n Provides measurement tools and workflows using Lighthouse, Web Vitals, and webpack-bundle-analyzer \n Emphasizes profiling first, incremental improvements, and continu

Make applications fast, scalable, and cost-efficient.

Systematic diagnosis for frame rate issues on variable refresh rate displays (ProMotion, iPad Pro, future devices). Covers render loop configuration, frame pacing, hitch mechanics, and production telemetry.

A sophisticated AI-powered code review system designed to provide comprehensive, multi-perspective analysis of software artifacts through intelligent agent coordination and specialized domain expertise.