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).

Automates Chmeetings tasks by orchestrating Composio’s toolkit through Rube MCP, guiding users to discover current tool schemas, verify toolkit connections, and execute workflows with schema-compliant arguments and managed sessions.

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.

CLIP enables general-purpose vision-language tasks through zero-shot image classification, similarity scoring, semantic search, and content moderation using pretrained contrastive embeddings without fine-tuning.

This tool optimizes the codebase indexing within Cursor. It processes your entire codebase, converting code chunks into vector embeddings stored in a vector database. This enables advanced @Codebase semantic search, allowing users to ask natural language questions (e.g., "Where is authentication handled?") and retrieve the most relevant code snippets, significantly improving AI context awareness across chat and coding tasks.

Leverages the Exa API for semantic/embeddings search, discovering similar content, or filtering by categories like companies, people, and research papers, making research and knowledge retrieval more targeted.

Automate Gong conversation intelligence by using Composio MCP natural-language commands to fetch call recordings, transcripts, analytics, speaker stats, and workspace metadata across date ranges.

High-performance Rust-backed tokenization toolkit with Python bindings, offering BPE/WordPiece/Unigram, custom vocabulary training, alignment tracking, and production-ready integrations for NLP pipelines.

This comprehensive guide details the end-to-end process of building Retrieval-Augmented Generation (RAG) pipelines using LangChain. It covers critical steps: loading diverse document types (PDF, CSV, TXT), splitting text into manageable chunks, generating high-quality embeddings (OpenAI), storing them in various vector databases (FAISS, Pinecone), and finally constructing the full runnable chain for accurate and context-aware querying.

A comprehensive guide to setting up production-grade observability for LangChain applications. It integrates LangSmith tracing for zero-code debugging, custom callbacks for detailed metric collection (latency, token usage, error rate), and Prometheus exporters for robust alerting. This allows developers to monitor LLM pipelines, track performance metrics, and manage operational health in real-time.

A comprehensive checklist for ensuring LangChain applications are robust, secure, and scalable for production environments. It covers critical areas including configuration management, advanced error handling, observability setup (e.g., LangSmith), performance optimization, security best practices (like prompt injection prevention and PII handling), rigorous testing, and zero-downtime deployment strategies. Use this guide when preparing for a major launch or auditing existing LLM systems.