plugins/tui-master/skills/tui-testing-debugging/SKILL.md
This skill should be used when the user asks to test, debug, snapshot, validate, reproduce, or automate terminal UIs, curses apps, raw ANSI output, PTY-driven interactions, virtual terminal emulators, ConPTY behavior, resize tests, keyboard/mouse/paste tests, or flaky terminal snapshots. PROACTIVELY activate for: TUI testing, snapshot tests, golden files, pty integration tests, pseudo-terminal automation, ConPTY, Pexpect, node-pty, vt emulators, Textual Pilot, Ratatui snapshots, Bubble Tea tests, terminal debug logs, and CI failures. Provides: layered test strategy, deterministic snapshot rules, PTY/ConPTY patterns, debugging playbooks, and flake prevention.
npx skillsauth add JosiahSiegel/claude-plugin-marketplace tui-testing-debuggingInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill when a TUI needs reliable tests or when an interactive terminal bug must be reproduced outside a human terminal session.
Freeze or normalize:
Prefer semantic assertions for behavior and snapshots for stable visual contracts.
| Symptom | Likely cause | Fix | |--|--|--| | Snapshot flake | time, spinner, random data, terminal width | freeze inputs and normalize output | | Works manually, fails in CI | no TTY or different TERM | create PTY or use non-TUI mode | | Windows tests fail only | Unix PTY assumptions | test via ConPTY and account for line endings | | Raw bytes differ but screen same | testing sequences not final cells | assert virtual terminal state | | Debug logs corrupt UI | logs write to controlled screen | route logs to file or panel |
references/test-strategy.md - Test layers, tools, and assertions by ecosystem.references/debugging-playbook.md - Byte capture, terminal reset, CI, Windows, and flake triage.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.