Skill UI

Generates and iterates high-performing ad creative—headlines, descriptions, primary text, and full variations—for any platform, using performance data to discover winning angles, stay within platform specs, and scale creative production.

This SDK provides comprehensive connectivity to Azure AI Search for .NET developers. It enables building sophisticated search applications, supporting full-text search, vector search, semantic understanding, and hybrid query capabilities. Developers can manage indexes, perform document CRUD operations, and implement advanced search patterns like faceted search and autocomplete.

The official SDK for interacting with Azure AI Search service using Python. It provides comprehensive functionality for advanced search retrieval, including full-text search, vector similarity search (HNSW), hybrid search, and semantic ranking. Use this tool to build sophisticated enterprise search applications that require deep content understanding and precise information retrieval.

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.

This comprehensive workflow manages the entire lifecycle of notes within Evernote. It provides structured methods for creating new notes (supporting plain text, checklists, and ENML formatting), retrieving existing content, updating notes, and organizing both notes and notebooks/stacks. Essential for building full-featured note-taking applications and content management systems.

A comprehensive workflow for executing advanced, semantic web searches using Exa. This tool moves beyond simple keyword matching, enabling deep context retrieval by understanding the intent behind the query. Key features include RAG context retrieval, content extraction (summaries, highlights, full text), and robust filtering by date range, specific domains, and content categories. Ideal for building knowledge base search features and complex research pipelines.

This skill manages and refines search results obtained from Exa's neural search API. It provides comprehensive control over content extraction scope, allowing users to selectively retrieve metadata, highlights, full text, or structured summaries. Key features include implementing result caching with TTL, deduplicating citations, and crucial token budget management, ensuring the retrieved context fits within LLM context window limits for robust RAG 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.

This bundle collects comprehensive diagnostic evidence for Langfuse support tickets and troubleshooting. It gathers system environment details (Node.js, Python, OS), installed SDK versions, API connectivity status, and redacted application logs. Use this when encountering persistent or complex Langfuse issues to provide full context to support teams.

Used for building, debugging, and extending MCP servers and clients. This skill implements robust connections between AI systems and external tools/data sources using structured protocols like JSON-RPC 2.0. Expertise includes defining tool schemas (Zod/Pydantic), configuring transport layers (HTTP/SSE/stdio), and scaffolding full-stack projects in TypeScript or Python.

nanoGPT is a simplified, minimalist implementation designed specifically for educational purposes. It reproduces the core architecture of GPT-2 (124M) using clean, hackable code, allowing users to understand the entire transformer pipeline from scratch. The framework supports full workflows, including data preparation on various datasets (e.g., Shakespeare, OpenWebText), model training, and text generation, making it ideal for students and researchers learning NLP and deep learning principles.

This skill provides a comprehensive walkthrough for setting up and testing Sentry error monitoring using both Node.js and Python SDKs. It demonstrates how to capture various event types, including simple messages and full exceptions, and crucially, how to enrich these events with user context, searchable tags, and detailed breadcrumbs. This is essential for verifying robust error logging and monitoring in a production environment.