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Compare Google and Meta campaign results across countries, states, cities, or postal codes. Identify weak and strong regions, then get recommendations for bid changes, separate campaigns, and budget redistribution based on meaningful performance differences.
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Analyze teams of agents in Claude Flow to find delays in communication, processing, memory, and network access. Generate performance reports, compare runs, and review recommendations or apply available automatic optimizations.
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Use this skill when the user is measuring the host or DPU-CPU control-plane rate of a DOCA Flow pipeline with doca_flow_perf — picking a JSON policy from configs/, choosing the DPDK or DOCA backend, running the single-iteration smoke then the iterative eval loop, interpreting per-iteration CPU cycles and num_pushed / num_failed, or capturing the four-tuple (DOCA version, BlueField/firmware, JSON policy, worker/queue/burst config) that makes a Kops/sec number defensible. Trigger even when the user does not explicitly mention "doca-flow-perf" — typical implicit phrasings include "how many rules per second can my BlueField insert", "5-tuple hairpin rule rate", "Kops/sec for steering", "flow-perf number does not match release notes", "DPDK vs DOCA benchmark", or "rule-install variance too high". Refuse and route elsewhere for optimizing a live Flow app (doca-flow-tune), the DPA-offloaded path (doca-flow-dpa-perf), dataplane throughput or latency, or library-internal pipe semantics — those belong to other skills.
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Intelligent loading performance analysis with automated workflows for TTFB investigation (DNS/connection/server breakdown), render-blocking detection, script performance deep dive (first vs third-party attribution), font optimization, and resource hints validation. Includes decision trees that automatically analyze TTFB sub-parts when slow, detect script loading anti-patterns (async/defer/preload conflicts), identify render-blocking resources, and validate resource hints usage. Features workflows for complete loading audit (6 phases), backend performance investigation, and priority optimization. Cross-skill integration with Core Web Vitals (LCP resource loading), Interaction (script execution blocking), and Media (lazy loading strategy). Use when the user asks about TTFB, FCP, render-blocking, slow loading, font performance, script optimization, or resource hints. Compatible with Chrome DevTools MCP.
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Django performance code review. Use when asked to "review Django performance", "find N+1 queries", "optimize Django", "check queryset performance", "database performance", "Django ORM issues", or audit Django code for performance problems.
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Diagnose and fix web performance issues including Core Web Vitals (LCP, INP, CLS), bundle size, asset optimization, render performance, and runtime efficiency. Use this skill whenever the user wants to improve page speed, fix Core Web Vitals, optimize assets, reduce bundle size, debug slow renders, or systematically improve a site's performance. Triggers on performance, page speed, Core Web Vitals, LCP, INP, CLS, FID, TTFB, bundle size, code splitting, image optimization, lazy loading, render blocking, slow page, performance audit, Lighthouse score. Also triggers when traffic or conversion is dropping due to perceived slowness.
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Performance scorecard design, post-matter feedback collection, QBR preparation, and firm comparison for in-house legal ops teams evaluating outside counsel. Design a scorecard framework calibrated to your team's maturity level. Produce post-matter feedback forms for supervising attorneys to complete at matter close. Prepare a QBR pack with agenda, data summary, and talking points for structured business reviews. Produce a side-by-side firm comparison for panel decision-making. Trigger on: 'design a performance scorecard', 'how do we evaluate our firms', 'build us a scorecard', 'post-matter feedback form', 'feedback at matter close', 'collect feedback on the firm', 'QBR', 'quarterly business review', 'prep for the firm review', 'what should I raise with the partner', 'compare the firms', 'which firm is performing better', 'rank our panel', 'firm performance data', 'scorecard framework', 'evaluate outside counsel', 'annual firm review', 'how are our firms performing'.
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Use when you need to set up JMeter performance testing for a Java project — including creating the run-jmeter.sh script from the exact template, configuring load tests with loops, threads, and ramp-up, or running performance tests from the project root with custom or default settings. This should trigger for requests such as Improve the code with JMeter performance testing; Apply JMeter performance testing; Refactor the code with JMeter performance testing; Add JMeter support; Create a JMeter test plan for a Java service. Part of Plinth Toolkit
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Expert at diagnosing and fixing performance bottlenecks across the stack. Covers Core Web Vitals, database optimization, caching strategies, bundle optimization, and performance monitoring. Knows when to measure vs optimize. Use when "slow page load, performance optimization, core web vitals, bundle size, lighthouse score, database slow, memory leak, optimize performance, speed up, reduce load time, performance, optimization, core-web-vitals, caching, profiling, bundle-size, database" mentioned.
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Use when a Lightning record page, home page, or app page is slow to load or render — covers Experienced Page Time (EPT) analysis, component count reduction, progressive disclosure via tabs and conditional rendering, Lightning Experience Insights diagnostics, and DOM/XHR minimization strategies. Triggers: 'Lightning page is slow', 'EPT is high', 'record page takes too long to load', 'too many components on page', 'Lightning Experience Insights shows slow page', 'how to optimize Lightning page performance'. NOT for Visualforce page performance (separate concern). NOT for Apex or SOQL query optimization (use apex/apex-cpu-and-heap-optimization or data/soql-query-optimization). NOT for report or dashboard performance (use admin/report-performance-tuning).
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Diagnose MSBuild build performance bottlenecks using binary log analysis. Only activate in MSBuild/.NET build context. USE FOR: identifying why builds are slow by analyzing binlog performance summaries, detecting ResolveAssemblyReference (RAR) taking >5s, Roslyn analyzers consuming >30% of Csc time, single targets dominating >50% of build time, node utilization below 80%, excessive Copy tasks, NuGet restore running every build. Covers timeline analysis, Target/Task Performance Summary interpretation, and 7 common bottleneck categories. Use after build-perf-baseline has established measurements. DO NOT USE FOR: establishing initial baselines (use build-perf-baseline first), fixing incremental build issues (use incremental-build), parallelism tuning (use build-parallelism), non-MSBuild build systems. INVOKES: binlog MCP server tools (overview, errors, search, items, properties); falls back to dotnet msbuild binlog replay + grep/cat when the MCP is unavailable.
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Django performance code review. Use when asked to "review Django performance", "find N+1 queries", "optimize Django", "check queryset performance", "database performance", "Django ORM issues", or audit Django code for performance problems.
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Systematic performance profiling and optimization methodology. Covers CPU profiling, memory profiling, heap analysis, goroutine/thread analysis, FlameGraph generation, latency profiling, database query profiling, network I/O profiling, and continuous performance regression detection. USE WHEN: investigating performance issues, optimizing slow code, reducing memory usage, diagnosing GC/CPU bottlenecks, finding N+1 queries, analyzing pprof/flamegraph output, or establishing a performance baseline. Triggers on "profiling", "performance analysis", "slow code", "flame graph", "pprof", "heap dump", "memory leak", "CPU spike", "bottleneck".
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提供检查和改善React与Next.js加载速度及交互响应的指引，按优先级梳理数据获取等待、首次加载的代码量和重复渲染等问题。
读取项目内容，确定介绍角度和表达风格，制作简短发布视频，并同时生成海报图片与分享文案。可指定横屏、竖屏或方形比例、时长及音频选项。
通过测量定位页面显示、交互、API和数据库的性能瓶颈，对比修改前后的数据验证效果。撤销没有明确收益的修改，并添加性能检查与监控，防止再次变慢。
基于HTML制作视频，组合标题、同步字幕、朗读音频、随音乐变化的视觉效果和场景转场，并检查内容溢出、文字可读性及动画时序。
测量claude-flow v3的启动时间、搜索速度和内存用量，验证是否达到改进目标，并通过持续监测发现性能退化，为优化方案提供依据。
Benchmark distributed consensus protocols, methods computers use to agree on shared results. Compare processing rates, response times, scaling behavior, and resource usage across Byzantine, Raft, and Gossip, then identify bottlenecks and potential tuning changes.
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Analyze performance in Claude Flow swarms, groups of agents working together. Identify delays in communication, processing, memory, and networking, then generate reports and prioritized recommendations. Compare previous runs or apply available automatic optimizations.
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Measure and optimize claude-flow v3 startup, search, attention processing, and memory use. Compare benchmarks against performance targets, track changes over time, and identify regressions to guide further optimization.
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Set and review limits for bundled file size and page performance so CI, automated checks on code changes, can catch regressions. Check enforcement on pull requests and review production monitoring for slowdowns after release.
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Audit page speed in a real browser to find delays in loading, script execution, and layout. Use measured results to identify bottlenecks, compare device and connection conditions, and guide performance improvements.
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Turn a music track into a HyperFrames video with text, images, and clips timed to its rhythm and energy. Additional visual assets are optional, and the workflow includes storyboard approval, visual checks, and final MP4 rendering.
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