plugins/tui-master/skills/tui-visual-design/SKILL.md
This skill should be used when the user asks to make a terminal UI beautiful, polished, readable, premium, modern, branded, or visually distinctive; improve visual hierarchy, spacing, typography, borders, color palettes, charts, progress displays, icons, prompts, forms, dashboards, or framework styling. PROACTIVELY activate for: TUI visual design, aesthetic polish, layout composition, terminal typography, color systems, box drawing, chart styling, animation, glyph/icon usage, prompt styling, design critique, visual anti-patterns, and framework-specific styling guidance. Provides: terminal design principles, polish checklists, visual recipes, and accessibility-aware aesthetics.
npx skillsauth add JosiahSiegel/claude-plugin-marketplace tui-visual-designInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill when a terminal UI needs to look deliberate, elegant, readable, and production-grade, not merely functional. Good TUI design is the craft of using cells, text, whitespace, color, borders, glyphs, rhythm, and motion under harsh terminal constraints.
Create hierarchy with, in order: placement, spacing, grouping, text labels, alignment, weight, then color. Do not make color carry the entire hierarchy.
Terminals do not give you real fonts inside the app, but they still have typographic systems.
Borders are punctuation. Too many boxes create visual noise.
Use semantic roles before raw colors: primary, accent, success, warning, error, info, muted, selected, focused, disabled, border, background, surface.
Error, icons, placement, or borders.Terminal charts should answer a question faster than reading raw numbers.
Motion should communicate waiting, progress, transition, or attention; otherwise it is noise.
Glyphs improve scanability when they are predictable and optional.
--ascii or a plain icon set.A beautiful prompt makes the next action obvious.
Layout constraints, Block borders, Paragraph wrapping, Table widths, and semantic Styles. Snapshot at fixed sizes and color modes.Width/Height calculations with wide Unicode.Theme roles, use tables/panels/progress consistently, and avoid nesting panels until output becomes visually heavy.NO_COLOR, TERM=dumb, and non-TTY output.references/layout-typography.md - Visual hierarchy, spacing, rhythm, typography, alignment, wrapping, and truncation.references/color-design.md - Semantic palettes, contrast, theme adaptation, gradients, and constrained color modes.references/borders-icons-prompts.md - Box drawing, containers, glyphs, icons, prompts, forms, dialogs, and notifications.references/charts-visualization.md - Bars, sparklines, gauges, progress, tables, trees, heat maps, and dashboard density.references/animation-motion.md - Spinners, motion design, transitions, scrolling, decorative effects, and performance limits.references/framework-styling.md - Visual styling recipes for Ratatui, Textual, Bubble Tea/Lip Gloss, Rich, Ink, Terminal.Gui, and Spectre.Console.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.