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概要と使いどころ
Optimize Flutter widget rebuilds, memory usage, rendering, and scrolling performance. Use when diagnosing jank, reducing rebuilds, or improving list performance—not performance tests, monitoring setup, or API optimization.
日本語の概要は準備中です。原文の説明を表示しています。
Use when you need to verify Java performance optimizations by comparing profiling results before and after refactoring — including baseline validation, post-refactoring report generation, quantitative before/after metrics comparison, side-by-side flamegraph analysis, regression detection, or creating profiling-comparison-analysis and profiling-final-results documentation. This should trigger for requests such as Verify performance fix; Verify the performance; Verify the memory; Verify the threading; Compare before and after profiling results; Check for performance regressions after refactoring. Part of Plinth Toolkit
日本語の概要は準備中です。原文の説明を表示しています。
Comprehensive performance specialist covering analysis, optimization, load testing, and framework-specific performance. Use when identifying bottlenecks, optimizing code, conducting load tests, analyzing Core Web Vitals, fixing memory leaks, or improving application performance across all layers (application, database, frontend). Includes React-specific optimization patterns.
日本語の概要は準備中です。原文の説明を表示しています。
Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning. Use when coordinating performance optimization across the stack.
日本語の概要は準備中です。原文の説明を表示しています。
Profile programs at the function/method level to identify performance hotspots, bottlenecks, and optimization opportunities. Records execution time, memory usage, and call frequency for each interval. Generates actionable recommendations and visualizations. Use when users need to (1) analyze program performance, (2) identify slow functions or bottlenecks, (3) optimize execution time or memory usage, (4) profile Python, Java, or C/C++ programs with test cases or workload scenarios, or (5) generate performance reports with flame graphs and recommendations.
日本語の概要は準備中です。原文の説明を表示しています。
WordPress performance code review and optimization analysis. Use when reviewing WordPress PHP code for performance issues, auditing themes/plugins for scalability, optimizing WP_Query, analyzing caching strategies, checking code before launch, or detecting anti-patterns, or when user mentions "performance review", "optimization audit", "slow WordPress", "slow queries", "high-traffic", "scale WordPress", "code review", "timeout", "500 error", "out of memory", or "site won't load". Detects anti-patterns in database queries, hooks, object caching, AJAX, and template loading.
日本語の概要は準備中です。原文の説明を表示しています。
Provider-independent performance direction for generated video, avatar/spokesperson clips, animation, ads, film scenes, social content, and voice-led media. Use when an agent must cast or direct synthetic performers, avatars, animated characters, talking heads, AI video characters, or voice performances; plan acting beats, objective/obstacle/action, blocking, gesture, eye-line, facial expression, body language, lip-sync, voice alignment, rehearsal prompts, iteration, continuity, consent, and performance QA.
日本語の概要は準備中です。原文の説明を表示しています。
Performance optimization mindset - knowing when to optimize, how to measure, where bottlenecks hide, and when "fast enough" is the right answerUse when "slow, performance, optimize, profiling, benchmark, latency, throughput, cache, n+1, bottleneck, memory leak, too slow, speed up, response time, performance, optimization, profiling, caching, latency, throughput, big-o, benchmarking" mentioned.
日本語の概要は準備中です。原文の説明を表示しています。
Performance optimization specialist for profiling, caching, and latency optimizationUse when "performance, latency, slow query, profiling, caching, optimization, N+1, connection pool, p99, performance, profiling, caching, latency, optimization, async, database, load-testing, ml-memory" mentioned.
日本語の概要は準備中です。原文の説明を表示しています。
The intersection of AI generation and performance marketing. This skill covers creating ad creatives at scale using AI tools—from static images to video ads to dynamic creative optimization—while maintaining the conversion focus that performance marketing demands. Traditional creative testing is slow: create 5 variants, wait weeks for data, iterate. AI-powered ad creative is fast: generate 50 variants in an hour, test simultaneously, learn in days, iterate continuously. The constraint is no longer production capacity—it's testing velocity and creative strategy. This skill bridges AI generation capability with advertising effectiveness, ensuring that AI-created ads don't just look good—they convert. Use when "AI ads, ad creative, performance creative, ad generation, creative testing, ad variants, DCO, dynamic creative, Meta ads, Google ads, ad fatigue, conversion creative, advertising, performance-marketing, creative-testing, ai-ads, conversion, paid-media, scale" mentioned.
日本語の概要は準備中です。原文の説明を表示しています。
World-class expertise in measuring, attributing, and optimizing AI-generated content performance. Combining data science rigor with content strategy intelligence to answer the questions traditional content analytics can't: Is AI content performing? Which AI variations convert? What's the true ROI of AI vs traditional content creation? This isn't vanity metrics for robots. This is the discipline of proving AI content drives business outcomes - and using that data to make AI content systems better, faster, and more profitable. Built on the principles of companies like Jasper, Copy.ai, and Notion who've scaled AI content operations with measurement as the foundation. Use when "ai content performance, ai content analytics, measure ai content, ai content roi, ai vs human content, ai content attribution, ai content testing, ai variation testing, ai content dashboard, ai content metrics, prompt performance, ai content conversion, ai content quality score, content velocity, ai content efficiency, ai-content, analytics, measurement, attribution, roi, ab-testing, performance, optimization, data-driven, content-analytics" mentioned.
日本語の概要は準備中です。原文の説明を表示しています。
Performance profiling and optimization for web apps — Core Web Vitals (LCP, INP, CLS), Lighthouse audits, bundle analysis, backend profiling (CPU, memory, DB queries), N+1 detection, caching strategies (Redis, CDN, HTTP), and performance budgets. Use when user asks to improve performance, run Lighthouse audit, profile a Node.js app, optimize Core Web Vitals, reduce bundle size, or investigate slow response times. Do NOT use for database schema optimization (use db-sculptor), Docker image optimization (use docker), or CDN configuration.
日本語の概要は準備中です。原文の説明を表示しています。
Optimizes application performance across frontend, backend, queries, and databases. Use when performance requirements exist, when you suspect performance regressions, when Core Web Vitals or load times need improvement, when N+1 query patterns need fixing, or when profiling reveals bottlenecks.
日本語の概要は準備中です。原文の説明を表示しています。
Optimize web performance for faster loading and better user experience. Use when the task involves `speed up my site`, `optimize performance`, `reduce load time`, `fix slow loading`, `improve page speed`, or `performance audit`.
日本語の概要は準備中です。原文の説明を表示しています。
Optimize web performance for faster loading and better user experience. Use when asked to "speed up my site", "optimize performance", "reduce load time", "fix slow loading", "improve page speed", or "performance audit".
日本語の概要は準備中です。原文の説明を表示しています。
Optimize web performance for faster loading and better user experience. Use when the task involves `speed up my site`, `optimize performance`, `reduce load time`, `fix slow loading`, `improve page speed`, or `performance audit`.
日本語の概要は準備中です。原文の説明を表示しています。
Use when diagnosing or improving frontend performance, Core Web Vitals, bundle size, runtime rendering cost, network waterfalls, memory leaks, long tasks, Lighthouse findings, or performance budgets; Chinese triggers include performance optimization, page freeze, slow first screen, bundle volume, Web Vitals.
日本語の概要は準備中です。原文の説明を表示しています。
Generates performance-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Performance Optimization pillar of the Google Cloud Well-Architected Framework (WAF). Use this skill to evaluate a workload, identify performance requirements, and provide actionable recommendations for resource allocation, modular design, and elasticity.
日本語の概要は準備中です。原文の説明を表示しています。
Generates performance-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Performance Optimization pillar of the Google Cloud Well-Architected Framework (WAF). Use this skill to evaluate a workload, identify performance requirements, and provide actionable recommendations for resource allocation, modular design, and elasticity.
日本語の概要は準備中です。原文の説明を表示しています。
Optimizes application performance. Use when performance requirements exist, when you suspect performance regressions, or when Core Web Vitals or load times need improvement. Use when profiling reveals bottlenecks that need fixing.
日本語の概要は準備中です。原文の説明を表示しています。
Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning. Use when coordinating performance optimization across the stack.
日本語の概要は準備中です。原文の説明を表示しています。
Profiles DAG execution performance including latency, token usage, cost, and resource consumption. Identifies bottlenecks and optimization opportunities. Activate on 'performance profile', 'execution metrics', 'latency analysis', 'token usage', 'cost analysis'. NOT for execution tracing (use dag-execution-tracer) or failure analysis (use dag-failure-analyzer).
日本語の概要は準備中です。原文の説明を表示しています。
Optimizes application performance. Use when performance requirements exist, when you suspect performance regressions, or when Core Web Vitals or load times need improvement. Use when profiling reveals bottlenecks that need fixing.
日本語の概要は準備中です。原文の説明を表示しています。
Systematically find and fix performance problems in applications, frontend and backend, by measuring first. Use this skill whenever the user reports slow endpoints, high latency, poor throughput, memory leaks, large bundles, or slow page loads; wants to profile with cProfile, py-spy, Chrome DevTools, Node --cpu-prof, or perf; run load tests with locust or k6; set performance budgets or SLOs; optimize database queries (EXPLAIN, indexes, N+1); add caching (Redis); improve frontend performance (code splitting, lazy loading, image optimization, memoization, virtualization); or tune the network (CDNs, gzip/br, HTTP/2/3). Also trigger for p50/p95/p99 latency, throughput, error rate, connection pooling, async vs threads, GIL, and memory leak investigations.
日本語の概要は準備中です。原文の説明を表示しています。