Skill UI

The official client library for interacting with Azure OpenAI services in .NET. It provides comprehensive access to various OpenAI models, including GPT-4o, embeddings, image generation (DALL-E), and audio transcription (Whisper). Use cases include building advanced chat applications, generating structured JSON outputs, and implementing Retrieval-Augmented Generation (RAG).

This skill automatically collects raw activity data (merged PRs, issues, commits, and optional Slack updates) from repositories. It formats this data using predefined templates, runs quality checks, and prepares a structured changelog draft. It can automatically commit the draft to a new branch and open a Pull Request (PR) for review, making it ideal for generating weekly or monthly release notes.

This skill generates commit messages strictly following Sentry's conventions and the Conventional Commit format. It mandates a structured commit message including a type, scope, subject, body (explaining what and why), and a footer for issue references. Use this whenever committing code changes to ensure a highly readable, traceable, and standardized version history.

Provides a structured approach for creating, editing, and analyzing .docx files with tooling and best practices for tracked changes, comments, formatting, and text extraction, covering JavaScript document generation and Python OOXML editing plus markdown-based redlining.

This skill provides the production-ready reference architecture for integrating Exa into complex applications. It covers advanced search pipelines, RAG implementation for context generation, and structured content discovery. Use this to design new Exa integrations, review project structures, or establish industry-standard best practices for neural search systems.

A comprehensive pipeline for processing, validating, and optimizing scraped web content from Firecrawl. This tool handles markdown cleaning, structured data extraction using Zod validation, content deduplication, and chunking tailored for Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems. It ensures crawled data is standardized, clean, and ready for downstream consumption or knowledge base indexing.

This guide details integrating FoundationModels for running Large Language Models (LLMs) directly on the device (iOS 26). It covers advanced patterns including text generation, extracting structured data using @Generable, implementing custom tool calling for domain logic, and streaming responses for real-time UI updates. Key features emphasize strong privacy and offline capability, ensuring no data leaves the device.

This skill provides a comprehensive set of reusable, production-grade TypeScript patterns for interacting with the Gamma REST API, which lacks an official SDK. It includes implementation for typed clients, advanced polling mechanisms, structured error handling, and complex generation workflows (e.g., template-based generation). Use this when building robust, reliable applications that integrate deeply with Gamma's content generation services.

This skill provides structured patterns for combining vector similarity search with traditional keyword-based search. It is essential for building advanced Retrieval-Augmented Generation (RAG) systems, sophisticated search engines, or any retrieval task requiring high recall. Use this when pure semantic understanding is insufficient, and precise matching of specific terms (like names or codes) is necessary.

A comprehensive reference architecture for implementing large-scale AI image generation using Ideogram. This system guides developers through building robust pipelines, including structured prompt templating (for brand consistency), utilizing multiple API endpoints (Generate, Edit, Remix), and implementing full asset workflow management from generation to final CDN delivery. Ideal for establishing brand asset systems and scalable AI integrations.

A comprehensive utility for structured logging, request tracing, and diagnostic purposes when integrating with the Kling AI API. It automatically captures request/response pairs, task lifecycle events, and detailed timing metrics for every API call, making troubleshooting video generation pipelines efficient and systematic. Ideal for developers building robust, observable AI applications.

A production-ready Python SDK providing comprehensive patterns for integrating with the Kling AI API. This wrapper handles crucial operational complexities such as auto-refreshing JWT tokens, structured request/response models, exponential backoff polling for long-running tasks, and detailed error handling. Use this library when building stable, high-reliability applications that require advanced text-to-video or image-to-video synthesis capabilities.