plugins/tui-master/skills/tui-fundamentals/SKILL.md
This skill should be used when the user asks to design, create, choose, modernize, or review a terminal UI, text user interface, curses app, dashboard, wizard, REPL shell, prompt flow, or CLI with interactive screens. PROACTIVELY activate for: TUI architecture, terminal-ui design, framework selection, curses vs modern framework, full-screen vs inline UI, app state modeling, widget composition, command palette, keyboard-first workflow, terminal dashboard, interactive CLI UX, and cross-platform terminal product planning. Provides: architecture patterns, framework decision rules, UX guardrails, scope boundaries, and production readiness checklists.
npx skillsauth add JosiahSiegel/claude-plugin-marketplace tui-fundamentalsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Use this skill when the task is about the shape of the terminal product: what kind of TUI to build, which abstraction to use, how to organize state, or whether a full-screen terminal UI is the right answer.
Prefer a rich TUI when users need continuous visual feedback, multi-step navigation, keyboard-driven exploration, selection among many items, or a dashboard-like control surface. Prefer a line-oriented CLI, prompts, or plain output when the flow is short, scriptable, primarily automation-focused, or likely to be used by screen readers and CI systems.
A production terminal product often ships both:
TERM=dumb, non-TTY output, missing color, Windows console differences, tmux/screen, SSH, and CI should have clear behavior.| Need | Strong default | |--|--| | Rust full-screen app with precise rendering | Ratatui + Crossterm | | Python product-grade app with widgets, CSS, async workers | Textual | | Python rich output, progress, tables, markdown, logs | Rich | | Python REPL, shell, completions, complex prompt input | prompt_toolkit | | Classic dependency-light Unix TUI | curses/ncurses | | Go Elm-style app with composable commands | Bubble Tea + Bubbles + Lip Gloss | | Go batteries-included forms/tables/tree views | tview over tcell | | Go portable low-level cell engine | tcell | | React-style terminal components in Node | Ink | | Node DOM-like full-screen widgets | Blessed | | .NET full-screen views | Terminal.Gui | | .NET rich CLI output and prompts | Spectre.Console |
Read references/framework-selection.md before committing to a framework in a new project.
?, command palette, --help, docs.TERM=dumb until late.references/framework-selection.md - Detailed ecosystem comparison and decision tree.references/tui-architecture.md - State, event, lifecycle, logging, and distribution patterns.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.