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Found 88 Skills
Building applications with Large Language Models - prompt engineering, RAG patterns, and LLM integration. Use for AI-powered features, chatbots, or LLM-based automation.
Build with Claude Messages API using structured outputs for guaranteed JSON schema validation. Covers prompt caching (90% savings), streaming SSE, tool use, and model deprecations. Prevents 16 documented errors. Use when: building chatbots/agents, troubleshooting rate_limit_error, prompt caching issues, streaming SSE parsing errors, MCP timeout issues, or structured output hallucinations.
Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.
Build interactive chat agents for exploring and discussing academic research papers from ArXiv. Covers paper retrieval, content processing, question-answering, and research synthesis. Use when building research assistants, paper summarization tools, academic knowledge bases, or scientific literature chatbots.
DeepEval evaluation workflow for AI agents and LLM applications. TRIGGER when the user wants to evaluate or improve an AI agent, tool-using workflow, multi-turn chatbot, RAG pipeline, or LLM app; add evals; generate datasets or goldens; use deepeval generate; use deepeval test run; add tracing or @observe; send results to Confident AI; monitor production; run online evals; inspect traces; or iterate on prompts, tools, retrieval, or agent behavior from eval failures. AI agents are the primary use case. Covers Python SDK, pytest eval suites, CLI generation, tracing, Confident AI reporting, and agent-driven improvement loops. DO NOT TRIGGER for unrelated generic pytest, non-AI test setup, or non-DeepEval observability work unless the user asks to compare or migrate to DeepEval.
Guide for implementing Syncfusion Windows Forms AI AssistView (SfAIAssistView) for building conversational AI interfaces in desktop applications. Use this when creating chat interfaces, AI assistants, or chatbots with Windows Forms. Supports OpenAI and Azure OpenAI integration, typing indicators, chat suggestions, message bubbles, and custom views for interactive messaging experiences.
MUST activate when the project contains a uiBundles/*/src/ directory and the task involves adding or modifying a chat widget, chatbot, or conversational AI. Use this skill when the user asks to add, embed, integrate, configure, style, or remove an agent, chatbot, chat widget, conversation client, or AI assistant. Covers styling (colors, fonts, spacing, borders), layout (inline vs floating, width, height, dimensions), and props (agentId, agentLabel, headerEnabled, showHeaderIcon, showAvatar, styleTokens). Activate when files under uiBundles/*/src/ import AgentforceConversationClient or when adding any chat or agent functionality to a page. Never create a custom agent, chatbot, or chat widget component.
Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.
Use this skill when the user wants to build AI applications with Weaviate. It contains a high-level index of architectural patterns, 'one-shot' blueprints, and best practices for common use cases. Currently, it includes references for building a Query Agent Chatbot, Data Explorer, Multimodal PDF RAG (Document Search), Basic RAG, Advanced RAG, Basic Agent, Agentic RAG, and optional guidance on how to build a frontend for each of them.
AI integration with Vercel AI SDK - Build AI-powered applications with streaming, function calling, and tool use. Trigger: When implementing AI features, when using useChat or useCompletion, when building chatbots, when integrating LLMs, when implementing function calling.
Telegram bot development - chatbots, notifications, AI assistants, and group automation
Implement the Syncfusion Angular Chat UI component. Use this skill whenever users need to implement messaging, real-time conversations, file attachments, typing indicators, user mentions, or bot integrations (Dialogflow, Microsoft Bot Framework) in Angular applications. Essential for customer support chatbots, team messaging apps, AI-powered assistants, and collaborative communication interfaces.