plugins/tui-master/skills/terminal-standards/SKILL.md
This skill should be used when the user asks about ANSI, VT100, VT220, ECMA-48, ISO 6429, xterm control sequences, terminal capability detection, terminfo, environment variables, alternate screen protocols, SGR colors, OSC, CSI, DCS, terminal graphics, Windows virtual terminal processing, ConPTY, tmux/screen, SSH, or escape-sequence compatibility. PROACTIVELY activate for: escape sequences, terminal standards, capability negotiation, raw ANSI output, xterm protocols, alternate screen, color support, OSC 8 links, OSC 52 clipboard, Kitty/iTerm2 protocols, sixel, and cross-terminal fallback design. Provides: safe protocol rules, negotiation checklists, compatibility warnings, and reference tables.
npx skillsauth add JosiahSiegel/claude-plugin-marketplace terminal-standardsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill when code emits or parses terminal control sequences, depends on terminal features, or must behave across emulators, multiplexers, and Windows console hosts.
Think in layers:
The further down this stack you go, the more you need detection, fallbacks, and cleanup.
| Feature | Typical sequence | Caveat |
|--|--|--|
| Alternate screen | CSI ? 1049 h / CSI ? 1049 l | May be disabled or mediated by multiplexers. Cleanup is mandatory. |
| SGR reset | CSI 0 m | Reset before exit and after errors. |
| Truecolor | CSI 38;2;R;G;B m / CSI 48;2;R;G;B m | Downsample to 256/16/no-color when needed. |
| Bracketed paste | CSI ? 2004 h / CSI ? 2004 l | Parse paste boundaries; disable on exit. |
| SGR mouse | CSI ? 1006 h plus tracking modes | Requires parser and coordinate validation. |
| Focus events | CSI ? 1004 h | Treat as advisory; may not arrive. |
| OSC 8 links | OSC 8 ; ; URI ST | Provide visible URL fallback. |
| OSC 52 clipboard | OSC 52 | Often blocked by terminals, multiplexers, SSH policies, or security settings. |
| DCS passthrough | DCS ... ST | Used by multiplexers and some protocols; highly environment-dependent. |
| Terminal title | OSC 0/2 | Restore or avoid changing titles unexpectedly. |
| Palette queries/changes | OSC 4/10/11 variants | Useful but fragmented; restore changes and time out queries. |
See references/escape-sequence-field-guide.md for detailed tables.
TERM meaningful? Handle dumb, empty, and unknown values.--color, --no-color, --plain, --ascii, --no-mouse?If enabling raw mode, alternate screen, mouse tracking, bracketed paste, focus events, cursor hiding, or keyboard protocols, verify cleanup on normal quit, Ctrl-C, panic, exception, fatal error, failed partial initialization, and test assertion failures.
references/escape-sequence-field-guide.md - ANSI/VT/xterm controls and cautions.references/capability-negotiation.md - Detection and fallback patterns across terminals.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.