plugins/tui-master/skills/tui-troubleshooting/SKILL.md
Use this skill when a terminal UI freezes, hangs, shows a blank screen, corrupts terminal state, leaves raw mode stuck, stops responding to input, garbles output, breaks colors/cursor state, fails over SSH/tmux/screen, behaves differently on Windows/ConPTY, or needs deep root-cause analysis for subtle terminal failure modes. PROACTIVELY activate for: TUI freeze, terminal hang, blank TUI, raw mode stuck, stdin paused, no input events, alternate screen stuck, mouse mode stuck, bracketed paste markers, garbled terminal output, corrupted cursor/colors, partial renders, resize races, terminal state cleanup bugs, npm run tui froze, and debugging terminal gotchas. Provides: freeze/hang diagnosis, terminal recovery, startup and cleanup lifecycle checks, platform triage, and fix patterns.
npx skillsauth add JosiahSiegel/claude-plugin-marketplace tui-troubleshootingInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill when the user reports that a TUI "froze", "hung", "blanked the terminal", "stopped accepting input", "left the terminal broken", or "works in one terminal but not another". Treat these reports as terminal lifecycle failures until proven otherwise.
Do not give a shallow checklist. Establish the failure class, inspect startup and cleanup order, and look for a concrete lifecycle bug:
Prioritize these before exotic terminal protocol issues:
process.stdin.pause() followed by raw mode without resume() prevents data events; the UI may wait forever.Ask for or inspect:
When a terminal is stuck, tell the user how to recover before deeper debugging:
stty sane
reset
printf '\033[?1049l\033[?25h\033[?1000l\033[?1002l\033[?1003l\033[?1006l\033[?2004l\033[?1004l\033[0m'
On Windows PowerShell, closing the tab is sometimes the fastest recovery when a child process left ConPTY in a bad state.
Every TUI should use one lifecycle owner:
references/freeze-hang-diagnosis.md - Root causes, symptoms, diagnostics, fix sketches, and prevention patterns for startup and runtime hangs.references/terminal-state-corruption.md - Garbled display, wrong colors, cursor bugs, partial renders, interleaved writes, and resize/render races.references/diagnostic-playbook.md - Decision trees with concrete commands for freezes, stuck terminal state, no input, SSH/tmux, Windows, and garbled output.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.