plugins/tui-master/skills/framework-recipes/SKILL.md
This skill should be used when the user asks for implementation recipes, examples, migration guidance, troubleshooting, or best practices for specific TUI frameworks and libraries: Rust Ratatui/Crossterm/Termion/Cursive, Python Textual/Rich/prompt_toolkit/curses/Urwid/asciimatics, Go Bubble Tea/Lip Gloss/Bubbles/tview/tcell/termui/gocui, Node Ink/Blessed/Inquirer/Prompts, .NET Terminal.Gui/Spectre.Console, C/C++ ncurses/notcurses/FTXUI, Zig vaxis, and SwiftTUI. PROACTIVELY activate for: framework-specific app structure, cleanup, testing, widgets, migration, library comparison, framework troubleshooting, API gotchas, and porting between TUI frameworks. Provides: framework defaults, gotchas, testing hooks, and selection recipes.
npx skillsauth add JosiahSiegel/claude-plugin-marketplace framework-recipesInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill when the user names a specific terminal UI framework or needs practical ecosystem guidance. Fetch current official docs when exact API syntax or version-specific behavior matters.
Ratatui + Crossterm is the modern default for Rust full-screen TUIs. Structure apps around explicit state, event handling, and rendering functions. Use Crossterm for cross-platform terminal setup and events. Snapshot widgets at fixed sizes and integration-test the event loop through a PTY.
Guidelines:
Textual fits substantial apps with widgets, CSS-like styling, workers, and test automation. Rich fits rich output, progress, markdown, tables, panels, and logging. prompt_toolkit fits shells, REPLs, completions, multiline editing, and custom key bindings. curses/ncurses fits classic dependency-light TUIs with careful cleanup.
Guidelines:
Bubble Tea uses Elm-style Model, Update, and View. Pair with Bubbles for common controls and Lip Gloss for styles/layout. tview gives widget-rich apps over tcell. Use tcell directly for lower-level cell control.
Guidelines:
Update and View separately before PTY tests.Ink is best for React-style terminal components, hooks, and component testing. Blessed fits DOM-like widget trees and manual screen lifecycle. Prompt libraries are often better than full-screen apps for short questionnaires.
Guidelines:
Terminal.Gui fits full-screen applications with views, keyboard navigation, mouse, themes, and layout. Spectre.Console fits rich CLI output, prompts, progress, status, trees, tables, and command app composition.
Guidelines:
ncurses remains the classic portable C TUI layer. notcurses is useful for modern visuals, rich cells, images where supported, and high-performance terminal graphics with fallbacks. FTXUI offers composable C++ components and layout. zig-vaxis is a modern Zig TUI toolkit with Unicode-aware terminal handling. SwiftTUI and related Swift terminal libraries fit Swift-native CLIs, but verify platform and terminal coverage carefully.
Guidelines:
references/rust-python-go.md - Practical patterns for Ratatui, Textual/Rich/prompt_toolkit/curses, Bubble Tea/tview/tcell.references/node-dotnet-and-selection.md - Ink, Blessed, Terminal.Gui, Spectre.Console, migration, and selection gotchas.references/low-level-and-emerging.md - C/C++ ncurses/notcurses/FTXUI, Zig vaxis, SwiftTUI, and emerging toolkit cautions.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.