plugins/react-master/skills/react-performance/SKILL.md
Complete React performance optimization system. PROACTIVELY activate for: (1) React.memo and memoization, (2) useMemo and useCallback usage, (3) Code splitting with React.lazy, (4) List virtualization (react-window, react-virtuoso), (5) Avoiding unnecessary re-renders, (6) useTransition and useDeferredValue, (7) Bundle optimization, (8) Web Vitals and profiling. Provides: Profiler setup, memoization patterns, lazy loading, virtualization config, state colocation. Ensures optimal React performance with measurable improvements.
npx skillsauth add JosiahSiegel/claude-plugin-marketplace react-performanceInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill to diagnose and improve React rendering, bundle size, Web Vitals, list performance, image/media loading, state placement, and responsiveness with measurable before/after evidence.
Use when the user asks for tasks covered by the frontmatter triggers, especially implementation guidance, debugging, architecture choices, production hardening, or performance-sensitive decisions in this domain. Start from this orchestrator, then load the focused reference file that matches the requested detail level.
React.memo, useMemo, and useCallback for measured hotspots or stable prop boundaries, not everywhere.React.memo comparisons can be slower than rendering if they perform deep equality on large structures.development
This skill should be used when the user asks to train, debug, scale, or improve ML models. PROACTIVELY activate for: (1) PyTorch, TensorFlow/Keras, JAX, Flax, Hugging Face Trainer/Accelerate training loops, (2) distributed training, DDP/FSDP/DeepSpeed, TPU/GPU setup, (3) mixed precision AMP/bf16, gradient accumulation, checkpointing, seeding, (4) overfitting, imbalance, loss functions, regularization, LR schedules, warmup, (5) memory optimization, gradient checkpointing, offloading, quantization-aware training. Provides: reproducible training best practices across deep learning and classical ML.
development
This skill should be used when the user asks to productionize, track, version, govern, monitor, or automate ML systems. PROACTIVELY activate for: (1) MLflow, Weights & Biases, Neptune, Comet, ClearML experiment tracking, (2) model registry, model versioning, artifact lineage, reproducibility, (3) Kubeflow, SageMaker Pipelines, Vertex AI Pipelines, Azure ML pipelines, Databricks workflows, (4) CI/CD, continuous training/evaluation, A/B tests, canary/shadow deployments, (5) drift detection, model monitoring, data validation, responsible AI governance. Provides: end-to-end MLOps architecture and operational safeguards.
development
This skill should be used when the user asks to optimize, export, serve, compress, or accelerate ML inference. PROACTIVELY activate for: (1) latency, throughput, p95/p99, batching, concurrency, KV cache, memory, or cost issues, (2) quantization INT8/INT4, GPTQ, AWQ, bitsandbytes, pruning, sparsity, distillation, (3) ONNX export, ONNX Runtime, TensorRT, TorchScript, torch.compile, XLA, OpenVINO, Core ML, TFLite, (4) Triton, TorchServe, TF Serving, BentoML, Seldon, KServe configuration, (5) edge deployment, CPU/GPU/TPU/Inferentia serving. Provides: hardware-aware inference optimization and safe benchmarking.
testing
This skill should be used when the user asks to tune hyperparameters, run sweeps, optimize search spaces, or use AutoML. PROACTIVELY activate for: (1) Optuna, Ray Tune, FLAML, AutoGluon, Hyperopt, Nevergrad, KerasTuner, W&B sweeps, (2) grid search, random search, Bayesian optimization, TPE, Gaussian processes, evolutionary search, (3) ASHA, Hyperband, successive halving, multi-fidelity optimization, population-based training, (4) learning-rate finder, batch-size search, early stopping, pruning, (5) reproducible sweep design and experiment analysis. Provides: budget-aware hyperparameter search strategy.