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Found 56 Skills
This skill should be used when working with DSPy.rb, a Ruby framework for building type-safe, composable LLM applications. Use this when implementing predictable AI features, creating LLM signatures and modules, configuring language model providers (OpenAI, Anthropic, Gemini, Ollama), building agent systems with tools, optimizing prompts, or testing LLM-powered functionality in Ruby applications.
Подробная русскоязычная справка по Open WebUI: архитектура, авторизация, функции, пайплайны, API, RAG, масштабирование, отладка и скрытые возможности. Используй этот скилл при любых вопросах об Open WebUI — как он устроен, как развернуть, настроить авторизацию (OAuth, LDAP, JWT), написать функцию или пайплайн, подключить модель (Ollama, OpenAI), настроить RAG/knowledge base, масштабировать на production, отладить проблему. Также используй при написании кода для Open WebUI: функции (filter, pipe, action), пайплайны, конфигурации, docker-compose.
Route AI coding queries to local LLMs in air-gapped networks. Integrates Serena MCP for semantic code understanding. Use when working offline, with local models (Ollama, LM Studio, Jan, OpenWebUI), or in secure/closed environments. Triggers on local LLM, Ollama, LM Studio, Jan, air-gapped, offline AI, Serena, local inference, closed network, model routing, defense network, secure coding.
Implement the Syncfusion ASP.NET Core Smart TextArea for AI-powered inline or popup text autocompletion. Covers OpenAI, Azure OpenAI, Ollama, and custom IChatInferenceService backends, plus UserRole, UserPhrases, and suggestion display mode customization.
Use PAL MCP to orchestrate multiple AI models (Gemini, OpenAI, Grok, Ollama) for code reviews, debugging, planning, and CLI bridging
Use when the user wants to build, initialize, validate, optimize, or refactor a model-powered assistant, internal tool, automation, evaluator, or workflow from a business scenario or common problem statement, including project-structure refactors or starter skeletons that may separate model setup, prompt config, and orchestration, even if the request also mentions a UI, app shell, or local model service such as Ollama, and it is still unclear whether the solution should stay a single request, add supporting capabilities, or become orchestration. The user does not need to mention Agently explicitly.
Eino component selection, configuration, and usage. Use when a user needs to choose or configure a ChatModel, Embedding, Retriever, Indexer, Tool, Document loader/parser/transformer, Prompt template, or Callback handler. Covers all component interfaces and their implementations in eino-ext including OpenAI, Claude, Gemini, Ollama, Milvus, Elasticsearch, Redis, MCP tools, and more.
Implements Syncfusion SfSmartRichTextEditor, an AI-enhanced WYSIWYG editor extending SfRichTextEditor in Blazor. Use this when configuring AI backends (OpenAI, Azure OpenAI, Ollama, custom IChatClient), Smart Action toolbar, AI query dialog, AssistViewSettings, AI popup events and methods, or any inherited Rich Text Editor features in Blazor Server and Web App.
Implements Syncfusion WPF Smart Text Editor (SfSmartTextEditor), an AI-powered multiline input control with intelligent text completion. Use this when implementing smart text editors, AI-powered text input, or predictive text suggestions in WPF applications. The control provides context-aware typing assistance through integration with AI services including Azure OpenAI, OpenAI, Ollama, Claude, Gemini, Groq, and DeepSeek. Features include dual suggestion display modes (inline/popup), custom phrase libraries for offline suggestions, full text customization, and comprehensive command handling for workflow integration.
LangGraph-based agent framework for consistent tool calling with automatic tool loops. Use when you need reliable multi-step task execution with OpenAI-compatible providers (Z.AI/GLM-5, OpenRouter, Groq, DeepSeek, Ollama).
TanStack AI (alpha) provider-agnostic type-safe chat with streaming for OpenAI, Anthropic, Gemini, Ollama. Use for chat APIs, React/Solid frontends with useChat/ChatClient, isomorphic tools, tool approval flows, agent loops, multimodal inputs, or troubleshooting streaming and tool definitions.
Delegate a coding task to Aider (`aider`) as a background implementer, then review its diff and land it yourself. Use this whenever the user wants to hand implementation work to Aider - phrasings like "have Aider do X", "delegate this to aider", "run it through Aider", or "use Aider to implement/fix/refactor" - or wants to run a queue of coding tasks through Aider while staying the reviewer. This includes asking Aider to drive a local or self-hosted OpenAI-compatible endpoint ("have Aider use my local model", "run Aider against llama.cpp / Ollama / vLLM / LM Studio"), which Aider reaches via `--api-base`. DO NOT USE for local-model or coding requests that do not name Aider, for tasks small enough to do inline, or when the user wants the code written directly without delegating.