Skill UI

A comprehensive skill for managing the entire lifecycle of AI experiments. Use it to create, run, track, and analyze model performance against various datasets. Compare multiple large language models (like GPT-4 or Claude), assess key metrics, and perform detailed evaluation workflows using the Arize CLI.

A comprehensive skill for creating, managing, and querying datasets within the Arize platform. Use it for dataset CRUD operations, appending examples, exporting data, and generating test or evaluation examples for machine learning models.

This skill facilitates comprehensive LLM-as-judge evaluation within the Arize platform. Users can create, update, and manage evaluators (defining prompt templates, classification criteria, and models). It allows running evaluations against live spans, historical experiments, or continuous data streams. It supports advanced features like column mapping and fine-grained data granularity (span, trace, session) for rigorous model performance monitoring.

This skill guides the process of optimizing, improving, or debugging Large Language Model (LLM) prompts. It teaches users how to extract structured prompts and performance signals from production trace data, leveraging attributes like `llm.input_messages` and evaluation annotations. Users can perform data-driven tuning and quality improvement using the `ax` CLI.

Guides an AI pair to document application needs, access patterns, and scaling concerns while producing Azure Cosmos DB NoSQL requirements and data model artifacts using established best practices in a structured workflow.

Structured pipeline for credit risk pre-loan modeling that loads and formats raw data, filters abnormal periods, trims high-missing/low-IV/high-PSI/high-correlation features, denoises via Null Importance, and exports an Excel cleaning report for review.

Guides architects through Dataverse SDK use cases by analyzing requirements, designing data models, selecting integration patterns, and producing production-ready Python code with best practices.

This skill guides the end-to-end process of building automated evaluation pipelines for Python applications powered by Large Language Models (LLMs). It teaches how to define evaluation criteria, instrument the application, build golden datasets, and run real-world evaluations using tools like pixie, ensuring the app's logic (not just the LLM) is rigorously tested. Ideal for QA, benchmarking, and improving LLM-based services.

Expert skill for generating production-quality Python scripts specifically for the FreeCAD CAD application. This includes automating complex 3D modeling tasks, creating parametric FeaturePython objects, performing advanced geometric operations (Boolean, Fillet, Loft), building custom GUI tools using PySide, and handling mesh/FEM analyses via the FreeCAD Python API. Ideal for CAD automation and engineering visualization.

Creates a production-ready Model Context Protocol server in Go by scaffolding modules, tools, resources, configuration, README guidance, and basic tests built around the official GitHub SDK for rapid setup.

Guides founders through board meeting prep, investor updates, and executive comms using narrative-first decks, the Three Things model, metric hierarchy, and pre-brief rituals to keep boards focused and informed.

A comprehensive guide for developers to build advanced, declarative agents for Microsoft 365 Copilot. This skill details the integration process using a Model Context Protocol (MCP) server, allowing the agent to connect with external systems, select tools, and handle complex authentication (OAuth 2.0/SSO). You will learn to generate key JSON manifests required for deployment.