Machine learning

Investigate training failures, NaNs, missing gradients, misleading losses, and non-reproducible runs in PyTorch, Lightning, or TensorFlow/Keras.

Azure Machine Learning SDK v2 for Python.

Search and extract Cypress information from official documentation (docs.cypress.io, cypress.io); prefer LLM markdown under /llm/* and refuse unverified API…

Protocol-level facts for Roboflow REST and Inference APIs — URL patterns, auth, parameters, error codes, and SDK quick-start.

Deployment option comparison (serverless, dedicated, self-hosted, batch) and Workflow execution patterns.

Master context engineering for AI agent systems.

Use this skill when the user wants to build tool/scripts or achieve a task where using data from the Hugging Face API would help.

Build and run evals for LLM and agent systems: golden datasets, LLM-as-a-judge with bias control, trajectory/step/outcome scoring, adversarial refuters, and…

PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading.

Unified biological database evidence owner.

This skill should be used at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs…

High-performance toolkit for genomic interval analysis in Rust with Python bindings.

Structured hypothesis formulation from observations.

Deterministic mathematical computation using SymPy.

Detects and prevents data leakage in machine learning and mathematical modeling.

Venue formatting for an ML paper: official style files, starting from a conference template, converting a submission between venues, and the page and margin…

Computational pathology toolkit for analyzing whole-slide images (WSI) and multiparametric imaging data.

Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data.

World-class computer vision skill for image/video processing, object detection, segmentation, and visual AI systems.

World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems.

World-class prompt engineering skill for LLM optimization, prompt patterns, structured outputs, and AI product development.

Enforces structured, highly documented storage for code and data projects.

Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM.

Designs complex generic types, refactors `any` types to strict alternatives, creates type guards and utility types, and resolves TypeScript compiler errors.