plugins/tui-master/skills/widgets-state-security-distribution/SKILL.md
This skill should be used when the user asks about TUI widgets, forms, dialogs, tables, trees, charts, command palettes, split panes, scrollbars, validation, state management, Elm/MVU architecture, async message passing, configuration, packaging, distribution, terminal security, escape-sequence injection, paste sanitization, or production hardening. PROACTIVELY activate for: widget design, validation, virtual scrolling, config/keybinding files, XDG paths, packaging, secure terminal output, and release readiness. Provides: widget contracts, state patterns, config, security, and distribution checklists.
npx skillsauth add JosiahSiegel/claude-plugin-marketplace widgets-state-security-distributionInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill when a TUI needs product-level interaction patterns, robust application state, secure terminal handling, user customization, or shipping guidance.
Prefer an explicit model-update-view or reducer architecture:
This keeps rendering deterministic, makes tests cheap, and prevents background tasks from writing directly to the terminal.
Terminal output is an interpreter. Sanitize untrusted text before writing it to a terminal or logs that may later be viewed in a terminal. Escape-sequence injection can change titles, write clipboard data, spoof prompts, hide text, or corrupt display state.
--no-tui path for automation, accessibility, and incident recovery.references/widget-patterns.md - Common terminal widgets, behavior contracts, and anti-patterns.references/state-config-distribution-security.md - MVU state, async effects, config, packaging, and terminal security.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.