Skill UI

Azure AI Voice Live SDK for JavaScript/TypeScript lets you build real-time, bidirectional voice assistants powered by Azure AI; it handles authentication, WebSocket sessions, audio streaming, VAD, tool invocation, and event callbacks for both Node.js and browser contexts.

Generates stakeholder-friendly C4 context-level documentation, covering personas, user journeys, features, external dependencies, and Mermaid diagrams to clarify system boundaries and interactions.

Guides engineering teams on compressing long agent conversations by focusing on tokens-per-task rather than tokens-per-request, using structured summaries, triggered strategies, and probe-based evaluation to keep artifact trails intact.

Identifies predictable context degradation patterns in large language models to diagnose failures and design resilient agents; covers lost-in-middle, poisoning, distraction, and confusion with mitigation tactics for long or complex conversations.

Defines a structured context-driven development approach that treats context artifacts as living documentation, ensuring consistent AI behavior, clear onboarding, and aligned planning across Conductor projects.

Explains what makes up a language model’s context state and how engineers should structure prompts, tools, retrieval, history, and outputs to guide agent behavior and manage token budgets.

Provides elite guidance on dynamic context management, vector databases, knowledge graphs, intelligent memory, RAG, and multi-agent workflows so AI systems maintain relevant, actionable information across enterprise scenarios.

A specialized skill for optimizing LLM applications handling long conversations and large datasets. This guide teaches advanced context engineering techniques, addressing issues like context rot, token limits, and the lost-in-the-middle problem. Capabilities include intelligent summarization, context trimming, information prioritization, and strategic context routing to ensure critical information is retained and utilized effectively throughout the dialogue.

A comprehensive framework for building persistent memory systems within Large Language Models (LLMs). It supports multi-faceted memory management, including short-term context storage, long-term knowledge retrieval, and entity-based fact updates. This skill is crucial for developing sophisticated AI assistants that maintain coherence, recall user history over extended periods, and provide context-aware interactions by managing memory consolidation and retrieval relevance.

Details how to research context, structure homepage or landing copy, and follow clarity/benefit-driven rules so every headline, CTA, and section pushes visitors toward a single conversion.

Master the Cursor AI chat interface to get concise code explanations, snippets, debugging help, refactoring advice, and review feedback by selecting context, using @-mentions, and following the documented multi-turn prompting flow in Cursor IDE.

Sets up and optimizes Cursor codebase indexing so your repo becomes searchable via semantic queries, improves AI context awareness, and powers @codebase features for faster navigation and chat-assisted coding.