plugins/tui-master/skills/input-handling/SKILL.md
This skill should be used when the user asks about TUI input handling, raw mode, cbreak mode, terminal keyboard parsing, Esc vs Alt ambiguity, Ctrl-C, shortcuts, focus management, mouse events, scroll wheel, bracketed paste, paste safety, command palettes, forms, text editing, resize events, or keybinding design. PROACTIVELY activate for: raw mode, keyboard input, keybindings, mouse support, paste handling, focus model, terminal forms, text input, vim/emacs-style shortcuts, input conflicts, and cross-platform Windows/macOS/Linux input behavior. Provides: input architecture, shortcut rules, focus patterns, paste/mouse safety, and failure-mode triage.
npx skillsauth add JosiahSiegel/claude-plugin-marketplace input-handlingInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill when a TUI reads keys, handles shortcuts, supports mouse, edits text, manages focus, or needs safe paste behavior.
Raw mode disables line discipline and changes signal behavior. Centralize it and restore it reliably. Verify behavior for Ctrl-C, Ctrl-Z/suspend where supported, EOF, resize, terminal close, and fatal exceptions. Passwords and tokens must not echo.
Good keybindings are discoverable, consistent, and conflict-aware.
q, Esc, or Ctrl-C for quit/cancel only when consistent with workflow risk.Every interactive element needs:
Enable bracketed paste for shells, editors, command palettes, and multiline inputs. Parse paste start/end markers and treat pasted text as data, not a sequence of commands. For mouse input, validate coordinates against the current layout and ignore stale events after resize.
| Symptom | Likely cause | Fix | |--|--|--| | Esc feels delayed | parser waiting to distinguish Alt prefix | tune timeout or use framework protocol support | | Ctrl-C does not exit | raw mode swallowed signal | map Ctrl-C explicitly and restore terminal | | Paste triggers actions | no bracketed paste or paste treated as keystrokes | enable paste protocol and route as text | | Mouse clicks wrong row | stale layout after resize or scroll | validate coordinates against current viewport | | Focus disappears | no focused fallback after removing widget | compute next eligible focus target |
references/input-protocols.md - Keyboard, mouse, paste, focus, and resize event details.references/keybinding-design.md - Shortcut design, conflict avoidance, and forms/editors.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.