Skill UI

This comprehensive guide details the architecture of memory for intelligent agents. It covers the distinction between short-term context, long-term vector storage, and advanced cognitive models (CoALA framework: semantic, episodic, procedural). Learn how to implement robust memory retrieval mechanisms, select appropriate vector databases (Pinecone, Qdrant, etc.), and utilize embedding models to build truly context-aware, persistent AI applications.

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.

This skill set provides advanced, structured strategies for optimizing Large Language Model (LLM) interactions. It covers techniques such as intelligent context summarization, dynamic trimming, context routing, and precise token budget allocation to prevent 'context rot'. It is essential for building robust, long-running, multi-turn conversational AI systems.

Persistent LLM conversation memory that manages short-term, long-term, and entity-focused layers to store, retrieve, and consolidate memorable context for smarter conversational AI agents.

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.