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Found 163 Skills
Эксперт AutoML. Используй для automated machine learning, hyperparameter tuning и model selection.
Use when "scikit-learn", "sklearn", "machine learning", "classification", "regression", "clustering", or asking about "train test split", "cross validation", "hyperparameter tuning", "ML pipeline", "random forest", "SVM", "preprocessing"
Guidelines for building RoboCorp RPA automation with Python, emphasizing functional programming, Pydantic validation, and async operations.
YES24 / 인터파크 공연의 공개 일정 + 등급별 잔여석을 단일 HTTP 호출로 조회 (조회 전용, 예매·결제 없음).
Design experiment plans with progressive stages — initial implementation, baseline tuning, creative research, and ablation studies. Plan baselines, datasets, hyperparameter sweeps, and evaluation metrics. Use when planning experiments for a research paper.
Dynamic linking skill for Linux/ELF shared libraries. Use when debugging library loading failures, configuring RPATH vs RUNPATH, understanding soname versioning, using dlopen/dlsym for plugin systems, LD_PRELOAD interposition, or controlling symbol visibility. Activates on queries about shared libraries, dlopen, LD_LIBRARY_PATH, RPATH, soname, LD_PRELOAD, symbol visibility, or "cannot open shared object file" errors.
Use when "experiment tracking", "MLflow", "Weights & Biases", "wandb", "model registry", "hyperparameter logging", "ML experiments", "training metrics"
Implements the Syncfusion WPF ColorPickerPalette control for color selection from themed and standard color palettes. Use this when adding color pickers with predefined palettes, customizing color options, or handling color selection events in WPF applications. Covers setup, color management, appearance customization, and interaction patterns.
Implements the Syncfusion WPF Color Palette (SfColorPalette) control for color selection interfaces with swatches. Use this when implementing color picker functionality, color swatches, or color binding in WPF applications. Covers setup, color selection, data binding, swatch navigation, appearance customization, and theming.
Train ML models with scikit-learn, PyTorch, TensorFlow. Use for classification/regression, neural networks, hyperparameter tuning, or encountering overfitting, underfitting, convergence issues.
Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.
Automates the Karpathy LLM Wiki workflow: turns web, GitHub, and YouTube URLs into well-structured, citable, wikilinked pages with automatic linting and sourcing — invoke with /pin-llm-wiki