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Add or update Handsontable performance tests for rendering and user interactions. The skill guides trace recording, measurement boundaries, baseline comparisons, and reports that help reviewers identify performance changes in pull requests.
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Review Google Cloud workloads using the Performance Optimization pillar of the Well-Architected Framework. Assess performance requirements and architecture, then get actionable recommendations for resource allocation, caching, independent components, automatic scaling, and ongoing monitoring.
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Analyze application and system performance to find bottlenecks, the points that slow processing. Use resource usage and performance data to plan improvements to resource allocation, load balancing, caching, and algorithms, then compare results after changes.
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application-performance-performance-optimization workflow skill. Use this skill when the user needs Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning. Use when coordinating performance optimization across the stack and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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application-performance-performance-optimization workflow skill. Use this skill when the user needs Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning. Use when coordinating performance optimization across the stack and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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Measure web page speed, API response times, and build or test duration. Save baseline measurements as JSON tracked in Git, then compare results before and after changes to identify performance regressions and assess launch targets.
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Monitor agent and system performance through live metrics, resource tracking, bottleneck analysis, and anomaly detection. Track response times and availability against service targets, and prepare dashboard data and performance reports.
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Measure code performance and run benchmarks to investigate slow operations, regressions, or memory pressure. Establish a baseline, compare results under matching conditions, report variability, and rerun correctness tests after optimization.
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Coordinate specialist agents to investigate and improve application performance across databases, backend services, frontend code, and delivery infrastructure. Establish baseline measurements, address observed bottlenecks, validate changes with load tests, and set up monitoring to detect future slowdowns.
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Review code for performance bottlenecks across database queries, memory use, frontend rendering, network requests, and algorithms. Use a structured checklist to identify optimization opportunities and classify their severity according to expected load and scale.
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Review game code for likely performance bottlenecks in processing, memory, rendering, and data loading. Compare available estimates with configured limits and produce ranked optimization recommendations, including expected impact, effort, risks, and areas needing runtime measurement.
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Create, update, run, and debug shell-based performance scenarios for the but CLI. It separates repository setup from the operation being measured, uses Hyperfine for timing, and guides focused validation and inspection of test output.
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Audit Google Ads conversion measurement, search terms, keywords, creative assets, bidding, budgets, and settings. Use account evidence to identify wasted spend and prioritize improvements across Search, Shopping, Performance Max, and Demand Gen without changing campaigns.
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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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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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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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Find and fix slow page loads, API responses, and database queries using measurements. Compare results before and after each change, discard changes without clear gains, and add checks or monitoring to catch future slowdowns.
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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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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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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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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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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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