Evaluate and compare ML model performance with rigorous testing methodologies

Design data models with Pydantic schemas, comprehensive validation rules,

Build revenue projection models with driver-based forecasting, scenario analysis, and pricing optimization

Monitor model performance, detect data drift, concept drift, and anomalies in production using Prometheus, Grafana, and MLflow

Build binary and multiclass classification models using logistic regression, decision trees, and ensemble methods for categorical prediction and classification

Automatic model selection based on task type. Routes planning to Opus, coding to Sonnet, simple tasks to Haiku. Optimizes cost and quality automatically.

Build predictive models using linear regression, polynomial regression, and regularized regression for continuous prediction, trend forecasting, and relationship quantification

Expert skill for AI model quantization and optimization. Covers 4-bit/8-bit quantization, GGUF conversion, memory optimization, and quality-performance tradeoffs for deploying LLMs in...

Reduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT. Use when compressing models without retraining, achieving 50% sparsity with minimal accuracy loss, or...

Reduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT. Use when compressing models without retraining, achieving 50% sparsity with minimal accuracy loss, or...

Use when reducing model size, improving inference speed, or deploying to edge devices - covers quantization, pruning, knowledge distillation, ONNX export, and TensorRT optimizationUse when ", " mentioned.

Use when building VaR models, stress testing portfolios, Monte Carlo simulations, or implementing enterprise risk management - covers market risk, credit risk, and operational risk frameworksUse...

Expert guidance for natural language processing development using transformers, spaCy, NLTK, and modern NLP techniques.

Vision-language pre-training framework bridging frozen image encoders and LLMs. Use when you need image captioning, visual question answering, image-text retrieval, or multimodal chat with...

This skill should be used when the user asks to "start an LLM project", "design batch pipeline", "evaluate task-model fit", "structure agent project", or mentions pipeline architecture,...

This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise...

Use when learning Rust concepts. Keywords: mental model, how to think about ownership, understanding borrow checker, visualizing memory layout, analogy, misconception, explaining ownership, why...

当用户需要学习某种风格、提取写作配方、建立风格库或模仿特定作者时调用。深度解构文本的15个维度，包括作者画像、思维内核、创作路径、互动设计等，建模为可精准复制的风格文件。触发词：风格建模、提取风格、学习风格、模仿写作、解构文章、写作配方、风格库。

Explain ML model predictions using SHAP values, feature importance, and decision paths with visualizations.

Use when a user asks a business question that requires querying data (e.g., "What were total sales last quarter?"). NOT for validating, testing, or building dbt models during development.