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.

Deliver production-ready LLM applications, RAG systems, and multi-agent orchestration with vector databases, embedding strategies, safety controls, and observability for enterprise deployments.

Deliver production-ready LLM applications, RAG systems, and multi-agent orchestration with vector databases, embedding strategies, safety controls, and observability for enterprise deployments.

REST-based Azure Speech to Text helper for short audio clips (≤60s) that avoids the Speech SDK; suitable for Python scripts that use requests with subscription keys or tokens for quick transcription and chunked streaming.

This skill defines structured behavioral frameworks (e.g., Brainstorm, Implement, Debug, Review, Teach, Ship) to guide the AI's output and approach based on the specific task phase. It optimizes performance by ensuring the AI adopts the correct mindset and output format—from initial ideation to final deployment—guaranteeing systematic, high-quality results across the entire development lifecycle.

Clarity Gate ensures documents entering RAG knowledge bases retain epistemic quality by generating Clarity-Gated Documents, validating Source of Truth tables, and running deterministic claim/document hash scripts before ingestion.

A sophisticated, two-stage system designed to capture and internalize user corrections and learnings during ongoing conversations. It automatically detects patterns (e.g., "use X instead of Y") and queues them. Users can manually review and apply these learnings to a persistent knowledge base (CLAUDE.md), ensuring the AI continuously improves its knowledge and adherence to specific user context for future sessions.

Chroma is an open-source, AI-native embedding database designed for building sophisticated LLM applications. It allows users to store high-dimensional embeddings and associated metadata, facilitating powerful vector search and full-text filtering. Ideal for implementing Retrieval-Augmented Generation (RAG) systems, semantic search, and document retrieval in local or self-hosted environments, making it perfect for open-source AI projects and prototyping.

Empowers building AI agents that perceive screens, plan via vision-language reasoning, and control mouse/keyboard for desktop automation, with attention to sandboxing and safety across Anthropic, OpenAI, and open-source paradigms.

Connect Claude to Gmail, Slack, GitHub, Notion and 1000+ services so it can send emails, create issues, post messages, and update databases instead of just drafting instructions.

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.

An expert system for designing and implementing advanced AI context architectures. It specializes in integrating intelligent memory systems, vector databases, and knowledge graphs to ensure AI applications maintain coherent, relevant, and scalable state across complex, multi-agent workflows. Essential for enterprise RAG implementation and stateful AI development.