plugins/tui-master/skills/rendering-layout-performance/SKILL.md
This skill should be used when the user asks to implement, optimize, debug, or review TUI rendering, layout, repainting, flicker, frame buffers, diff rendering, terminal resize behavior, alternate screen lifecycle, scrollback, viewports, tables, dashboards, progress displays, or performance over SSH/tmux. PROACTIVELY activate for: rendering performance, layout bugs, flicker, alternate screen cleanup, resize handling, terminal dashboards, frame pacing, virtual DOM/cell buffers, double buffering, dirty regions, scroll regions, long-running tasks, and high-frequency updates. Provides: render-loop rules, layout patterns, performance budgets, lifecycle cleanup checks, and failure triage.
npx skillsauth add JosiahSiegel/claude-plugin-marketplace rendering-layout-performanceInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill when a TUI draws incorrectly, flickers, wastes CPU, mishandles resize, corrupts scrollback, or needs a robust rendering architecture.
width and height every frame or on every resize event.Use alternate screen for full-screen apps when users should return to the original scrollback after exit. Do not use it for simple output where command results should remain visible.
Cleanup must restore:
| Symptom | Likely cause | Fix | |--|--|--| | Flicker | clearing entire screen or flushing repeatedly | diff/batch writes, avoid full clears | | High CPU while idle | unconditional redraw loop | render on events or capped tick | | Corrupted shell after crash | missing cleanup on panic/exception | central terminal guard and finally/defer/drop cleanup | | Layout overlap | absolute coordinates or stale dimensions | recompute constraints after resize | | Logs appear inside UI | stdout/stderr logging while alternate screen active | file sink, in-app log panel, or buffered logs |
references/render-loop-patterns.md - Frame buffers, dirty regions, throttling, and event loops.references/alternate-screen-lifecycle.md - Setup/teardown checklist and crash recovery.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.