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.

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.

Builds production-ready rollout plans covering checks, procedures, verification signals, rollback, and communications for infrastructure or application changes across environments.

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.

Transforms unstructured and messy bug reports—including pasted error logs, voice dictations, and screenshots—into clean, actionable, and highly structured GitHub Issues. It automatically populates key sections like reproduction steps, expected/actual behavior, impact severity, and environment details, ensuring developers receive comprehensive and ready-to-use information.

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.

Master the art of scroll-driven motion with production-grade code recipes for both GSAP (ScrollTrigger) and Framer Motion. This skill covers all complex scroll effects, including parallax, pinned sections, horizontal scrolling, scrub animations, and view-triggered reveals, applicable in Vanilla JS, React, and Next.js environments. Ideal for implementing advanced, immersive user experiences that feel premium and highly polished.

A structured methodology for coordinating multiple, simultaneous investigation threads to determine the root cause of complex system failures, performance regressions, or integration bugs. This skill decomposes a large problem into independent tracks (e.g., database, application code, infrastructure), assigns ownership, and manages structured sync points (Continue/Pivot/Converge) to converge on a solution faster than serial debugging. Essential for high-stakes production incident response.

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.

This tool generates a complete, production-ready PHP Model Context Protocol (MCP) server project. It automatically scaffolds the entire structure, including tools, resources, prompts, and comprehensive tests, utilizing the official PHP SDK. It provides a standardized, robust starting point for developers building AI-integrated backend services in PHP.

This tool acts as an expert prompt engineer, guiding users through a systematic discovery process to create highly structured, production-ready prompts for AI assistants like GitHub Copilot. It covers defining persona, specifying complex tasks, managing context variables, integrating tools, and enforcing strict output standards, ensuring the resulting prompt is comprehensive and actionable for advanced AI agents.