Machine learning

Analyze finance text sentiment using FinBERT or LLM.

Build retrieval and RAG with Anvia — chunking, embeddings, vector stores, knowledge graphs, filters, and search tools.

Scaffold, structure, and manage AI/ML projects that run on AzureML.

Generates code that transforms datasets between ML schemas for model training or evaluation.

Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration

Build type-safe LLM applications with DSPy.rb — Ruby's programmatic prompt framework with signatures, modules, agents, and optimization.

Build RAG / unstructured-document evaluation datasets and demo documents (e.g.

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…

Connects to and performs inference with Google Cloud Agent Platform GenAI models, including First-Party Gemini models and Third-Party OpenMaaS models (Llama…

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

Deterministic mathematical computation using SymPy.

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.

Apply source-aware mechanical-engineering judgment to research, analysis, coding, writing, teaching, research identity, and release work.

Build DeepStream GStreamer pipelines interactively.

Investigate outliers, rare events, spikes, and suspicious records in datasets.

Evaluate trained machine learning models with the right metrics and comparison logic.

Explain trained machine learning models through feature attribution, local explanations, and behavior summaries.

Design and implement repeatable preprocessing pipelines for cleaning, encoding, transforming, and validating ML input data.

Deep learning framework (PyTorch Lightning).

Split datasets into training, validation, and test partitions with the right stratification and temporal rules.