plugins/tui-master/skills/platform-compatibility/SKILL.md
This skill should be used when the user asks about terminal emulator compatibility, operating-system differences, tmux/screen/Zellij, SSH/remote terminals, Windows ConPTY/classic console/Windows Terminal, Unix PTYs, macOS Terminal/iTerm2, Kitty/Alacritty/WezTerm/foot/GNOME Terminal/Konsole, terminal feature matrices, escape-sequence passthrough, latency, resize propagation, or graceful degradation. PROACTIVELY activate for: compatibility audits, emulator bugs, remote TUI performance, multiplexer passthrough. Provides: support matrices, platform caveats, and fallback rules.
npx skillsauth add JosiahSiegel/claude-plugin-marketplace platform-compatibilityInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill when a TUI must behave across operating systems, terminal emulators, multiplexers, SSH sessions, containers, CI, or embedded pseudo-terminal hosts.
Terminal capability is a chain: application library, OS console or PTY, shell, multiplexer, SSH transport, terminal emulator, font, theme, and user settings. A feature is reliable only if each link preserves it. Treat every advanced protocol as optional unless the app owns the whole environment.
For production TUIs, document and test:
TERM and terminfo availability.SIGWINCH, termios raw/cbreak restoration, process signals, and locale-dependent encoding.TERM, filter OSC/DCS, transform mouse events, mediate alternate screen, and require passthrough wrappers for clipboard, images, truecolor, or keyboard protocols.references/emulator-platform-matrix.md - OS, emulator, PTY, and feature compatibility guidance.references/multiplexer-remote.md - tmux/screen/Zellij, SSH, latency, passthrough, and resize practices.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.