plugins/tui-master/skills/unicode-color-accessibility/SKILL.md
This skill should be used when the user asks about Unicode rendering, wcwidth, grapheme clusters, emoji, combining marks, East Asian width, BiDi text, box drawing, Nerd Fonts, color themes, truecolor, 256-color fallback, NO_COLOR, contrast, accessibility, screen-reader behavior, keyboard access, reduced motion, or non-TUI fallbacks. PROACTIVELY activate for: Unicode width bugs, misaligned tables, emoji layout, ANSI-aware string measurement, color accessibility, screen-reader friendly CLI design, plain output mode, ASCII fallback, and terminal theme support. Provides: text measurement rules, color/fallback policy, accessibility checklist, and failure-mode fixes.
npx skillsauth add JosiahSiegel/claude-plugin-marketplace unicode-color-accessibilityInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill when terminal output must be visually correct, readable, inclusive, and robust across fonts, locales, themes, and assistive technologies.
Respect user and environment preferences:
NO_COLOR disables non-essential color.CLICOLOR, CLICOLOR_FORCE, and FORCE_COLOR may force color depending on ecosystem conventions.TERM=dumb, non-TTY output, and CI usually imply plain output unless overridden.--color=auto|always|never, --plain, and --ascii.--json, --plain, or documented stdin/stdout workflows.| Symptom | Likely cause | Fix | |--|--|--| | Misaligned tables | byte length or ANSI counted as visible width | use ANSI-aware wcwidth/grapheme measurement | | Cursor splits emoji | movement by code point | move by grapheme cluster | | Borders look broken | font or width mismatch | provide ASCII mode and test common fonts | | Error only shown in red | color-only meaning | add text/icon/label and contrast check | | Screen reader unusable | full-screen repaint grid | provide non-TUI mode and line-oriented flows |
references/text-measurement.md - Unicode width, graphemes, emoji, BiDi, and ANSI measurement.references/accessibility-fallbacks.md - Color policy, screen-reader alternatives, and inclusive UX checklist.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.