'performance' 검색 결과

Use TensorBoard to visualize training loss and accuracy, compare experiments, and inspect model structure. Includes PyTorch and TensorFlow logging examples for examining weight distributions, predictions, and performance bottlenecks during machine learning training.

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• Comparing loss and accuracy across training runs
• Inspecting weight and gradient distributions
• Exploring embeddings in two or three dimensions

Write, review, or refactor React code using prioritized guidance for data fetching, bundle size, rendering, component structure, testing, and type safety. Start with high-impact issues such as sequential requests and unnecessary library loading.

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• Writing React components and data-fetching logic
• Reviewing code for loading and rendering performance
• Refactoring large components and overly broad interfaces

Analyze social media post data to calculate engagement, click rates, and estimated returns on advertising spend. Compare results with supplied platform benchmarks, identify strong and weak posts, and generate recommendations for improving campaign performance.

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• Review social campaign performance
• Identify posts with strong or weak engagement
• Estimate advertising returns and interaction costs

Refine a game feature or area through performance measurement, visual and audio polish, and stress and regression testing. Report before-and-after metrics, remaining issues, and release readiness, with work limited to the enabled team roles.

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• Identify performance bottlenecks in combat
• Refine particle effects and shaders
• Review sound feedback and audio mixing

Investigate slow TPU training on GKE by comparing diagnostic events with one-minute monitoring metrics. Separate memory or CPU bottlenecks from hardware and network issues, and identify the appropriate next steps.

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• Investigating slower TPU training without pod crashes
• Checking PERFORMANCE_DEGRADATION events
• Separating resource bottlenecks from infrastructure issues

Measure scripts, running processes, or whole systems to find performance bottlenecks. Select suitable tools, add measurement code when needed, and report CPU, GPU, memory, and communication findings with prioritized recommendations and a record of file changes.

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• Finding slow functions in a Python script
• Investigating CPU and GPU memory usage
• Measuring communication overhead across multiple GPUs

Improve GSAP animation performance by reviewing animated properties, browser layout work, and simultaneous updates. Use transforms, grouped updates, reusable animations, and targeted cleanup to reduce stuttering in lists, mouse followers, and scroll effects.

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• Reviewing stuttering GSAP animations
• Reducing animation load in long lists
• Optimizing mouse-following effects

Use TensorBoard to visualize training loss and accuracy, compare experiments, and inspect model structure. Guides logging for PyTorch and TensorFlow, including weight distributions and performance profiling to investigate training problems and processing bottlenecks.

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• Monitoring training loss and accuracy
• Comparing experiments with different training settings
• Inspecting weight and gradient distributions

Create unit, integration, and E2E tests that check complete user flows, then identify coverage gaps and investigate unreliable results. The skill also supports performance and security test plans, with findings organized by severity and suggested fixes.

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• Writing tests for normal behavior, errors, and boundary values
• Finding gaps in test coverage
• Investigating tests that fail intermittently

Build custom WordPress themes, plugins, and editor blocks, extend WooCommerce stores, and add REST API endpoints. The skill also guides input protection, permission checks, caching, and database query tuning to improve security and performance.

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• Build custom WordPress themes and plugins
• Create Gutenberg blocks and block patterns
• Extend WooCommerce stores and REST API endpoints

Build Go services and command-line tools with clear interfaces, explicit error handling, and concurrent tasks using goroutines and channels. Use pprof to investigate performance, then check behavior with benchmarks, structured test cases, fuzzing, and race detection.

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• Implementing concurrent tasks with cancellation and error handling
• Building gRPC or REST services and command-line tools
• Investigating Go performance with pprof and benchmarks

Build Angular 17+ applications with standalone components, routing, and shared application state. Covers reactive data handling with RxJS, performance improvements, and unit and integration tests, with code and explanations of architecture choices.

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• Building Angular screens with standalone components
• Managing shared application state with NgRx
• Configuring lazy-loaded routes and access guards

Turn social media and content metrics into a concise report comparing engagement, reach, and growth across periods or platforms. Explain performance patterns, flag data quality issues, and propose three to five actions backed by the findings.

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• Preparing social media performance reports
• Comparing results across platforms and periods
• Finding patterns in strong and weak content

Review Pinterest Ads tracking, product catalogs, visual ads, audiences, and budgets. Get prioritized recommendations supported by account evidence, with missing information and unavailable features clearly identified so you can assess what needs attention.

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• Checking Pinterest Tag and conversion tracking
• Reviewing catalog and shopping ad readiness
• Prioritizing improvements to visuals, audiences, and budgets

Investigate slow rendering, uneven scrolling, and excessive updates in SwiftUI apps by reviewing code first. When needed, use runtime measurements to assess likely causes, prioritize fixes, and compare performance before and after changes.

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• Investigate uneven scrolling and dropped frames
• Find causes of excessive view updates
• Review high CPU use or memory growth

Build iOS and Android apps with React Native and Expo. Get guidance and typed component code for navigation, lists, animations, storage, and platform differences, with attention to scrolling performance and testing on both platforms.

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• Building mobile screens with React Native and Expo
• Adding screen navigation with Expo Router
• Improving list scrolling and animation performance

Investigate slow Apple apps, memory growth, launch delays, and battery drain using Instruments and Xcode diagnostics. Choose measurements for each symptom, identify the responsible code, and compare results before and after targeted fixes.

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• Investigate UI freezes and stuttering
• Find memory leaks and growing memory use
• Measure and improve app launch time

Diagnose and improve slow web interfaces by measuring loading, rendering, interactions, and animations. The skill addresses images, JavaScript bundles, page layout, and network requests, then compares before-and-after results to verify improvements across devices and connection speeds.

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• Reducing slow initial page loads
• Improving sluggish scrolling and animations
• Reducing image and JavaScript download sizes

Review interface code for accessibility, performance, theme support, responsive behavior, and problematic patterns. Receive a scored report with code locations, user impact, severity levels, and prioritized recommendations to guide subsequent fixes.

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• Checking accessibility before release
• Reviewing performance issues in interface code
• Inspecting dark mode and mobile layouts

Analyze trends and group performance in Excel data, clean percentage values, and calculate projections using growth rates assigned by performance tier. Produce comparison charts and a summary of current totals, projected totals, and growth.

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• Track changes in business metrics over time
• Compare performance across departments or categories
• Estimate growth using rates assigned by performance tier

Review C#/.NET code for about 50 inefficient API usage patterns across asynchronous work, memory, strings, collections, and I/O. Get exact occurrence counts, prioritized findings, and concrete fixes, with attention to frequently executed code.

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• Reviewing frequently executed C#/.NET code
• Finding unnecessary memory allocations
• Checking known performance pitfalls before release

Compare Google and Meta campaign results across mobile, desktop, and tablet. Identify device performance gaps, investigate whether ads, landing pages, or audiences explain them, and receive recommendations for bids, campaign splits, and page changes.

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• Investigating device differences in acquisition costs
• Checking mobile performance after a landing page redesign
• Reviewing mobile traffic growth without more conversions

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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• Reallocating budgets across regions
• Reviewing results after entering new markets
• Checking quarterly shifts in regional performance

Golang performance optimization patterns and methodology - if X bottleneck, then apply Y. Covers allocation reduction, CPU efficiency, memory layout, GC tuning, pooling, caching, and hot-path optimization. Use when profiling or benchmarks have identified a bottleneck and you need the right optimization pattern to fix it. Also use when performing performance code review to suggest improvements or benchmarks that could help identify quick performance gains. Not for measurement methodology (→ See `samber/cc-skills-golang@golang-benchmark` skill) or debugging workflow (→ See `samber/cc-skills-golang@golang-troubleshooting` skill).

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