Orchestrate multi-provider chat completions with unified message structures and semantic aliases.
npx skills add https://github.com/juanre/llmring --skill chat-juanre
Please help me install this Agent Skill. Skill: chat Source: https://github.com/juanre/llmring/tree/main/skills/chat Command: npx skills add https://github.com/juanre/llmring --skill chat-juanre
Starting a new LLM project or building a basic chat application often involves boilerplate code for different providers and managing conversation history. This Skill provides a unified, simple interface for core chat completions, letting you focus on your application logic.
LLMRequest and LLMResponse structure.Message objects, enabling natural, ongoing dialogues with your AI.First, initialize your lockfile with 'llmring lock init' and bind 'summarizer' to a model. Then, use the chat skill to send "Hello!" to your 'summarizer' alias.
High-intent search queries and answers about installing and using this skill.
Use a unified chat interface with provider-agnostic `LLMRequest` and `LLMResponse` structures. Define semantic aliases in a lockfile to bind task names to specific models, then send messages through a single API that routes to OpenAI, Anthropic, Google Gemini, or Ollama without changing your application code.
Maintain conversation history by appending `Message` objects to your chat state, then send the full message list with each request. This enables natural, ongoing dialogues where the LLM retains context across multiple exchanges.
Yes. Use semantic aliases defined in your lockfile to map task names like 'summarizer' to any provider's model. Update the lockfile binding, and your application automatically routes requests to the new provider without code changes.
Initialize your lockfile with `llmring lock init` and bind a semantic alias to your chosen LLM provider and model. After setup, you can immediately use the chat skill to send requests through your configured alias.
Yes. The unified chat API supports common AI assistant tasks including question-answering and text summarization. Create a semantic alias for each task type and maintain conversation history as needed for multi-turn interactions.
The skill provides timeout control and resource management through context managers, allowing you to handle request failures gracefully and define acceptable response times for your application's requirements.
juanreGenerate a TOML-based llmring.lock with semantic aliases, environment profiles, and model fallbacks.
ovachieverConstrain LLM outputs to valid JSON, XML, and code using regex and grammar constraints.
flexionDesign AI agent tools with evidence-based principles for descriptions, parameters, and errors.
appleManage custom skills and chat completions via the Foundation Models framework.