Skill UI

Master the art of developing robust and scalable Python applications that interface with Microsoft Dataverse. This skill covers implementing enterprise-grade best practices, including comprehensive error handling using DataverseError hierarchy, advanced retry logic with exponential backoff, and optimizing API calls using OData standards (filtering, selecting, and expanding). It ensures code is clean, maintainable, and production-ready.

Provides Dataverse Python SDK snippets for installation, interactive authentication, CRUD, bulk operations, paging, and optional file uploads aligned with official guidance.

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.

This script provides a lightweight, standalone Python integration for Google Sheets, offering full read and write access without needing a dedicated server. Users can retrieve spreadsheet data in multiple formats (plain text, CSV, JSON), extract specific ranges, find sheets via search queries, and perform advanced write operations like updating, appending, or clearing cell values. It is highly suitable for automating data reporting, extraction, and workflow integrations involving Google Workspace.

A comprehensive guide for creating, updating, refactoring, and reviewing solutions built on the Microsoft Agent Framework. This skill provides unified best practices for both .NET and Python environments, serving as a modern successor to Semantic Kernel and AutoGen. It emphasizes adherence to the latest official documentation, async patterns, robust error handling, and structured agent/workflow orchestration for complex AI applications.

A comprehensive guide for writing and managing Python code specifically within n8n Code nodes. Learn how to perform complex data transformations, handle different execution modes (batch vs. item-by-item), and master various data access patterns (_input.all(), _input.item, _node). Note the critical limitations, such as relying only on the standard Python library and the general recommendation to use JavaScript for most n8n use cases.

Phoenix Evals lets teams build evaluators for AI/LLM apps by wiring deterministic code with LLM nuance, running Python or TypeScript workloads, validating against humans, and iterating through error analysis, RAG, and production workflows.

Guides hooking Phoenix AI services with OpenInference tracing, covering setup, spans, instrumentation, and production deployment for LLM workflows using Python or TypeScript.

Scaffold a production-ready Python MCP server with uv-based tooling, FastMCP configuration, async tools, structured schemas, and comprehensive error handling plus deployment/testing guidance.

Guides Python authors from commit to release with end-to-end steps for structuring src layouts, choosing setuptools/hatchling/flit/poetry backends, enforcing SOLID+type-hinting patterns, orchestrating lint/test/CI, managing PEP 440 semantics, and publishing trusted releases to PyPI.