Send chat completions to LLM providers using model prefix routing.
npx skills add https://github.com/xberg-io/plugins --skill calling-llms
Please help me install this Agent Skill. Skill: calling-llms Source: https://github.com/xberg-io/plugins/tree/main/plugins/liter-llm/.cursor-plugin/skills/calling-llms Command: npx skills add https://github.com/xberg-io/plugins --skill calling-llms
The Skill unit addresses the complexity of sending chat completions to multiple language learning model providers and routing them appropriately.
ChatCompletionRequest objects to interact with different models.Create a client for OpenAI GPT-4o with a model hint, then send a message request using client.chat(request).
High-intent search queries and answers about installing and using this skill.
Route chat completions to different LLM providers by specifying a model string prefix. The Skill uses this prefix to direct the ChatCompletionRequest to the correct provider using the liter_llm library for client creation.
Manage multi-provider LLM integrations by using a routing layer that handles ChatCompletionRequests across 143 providers. This approach centralizes client creation and message dispatching without writing provider-specific logic.
Yes, you can send chat completion requests without a provider prefix by using an optional model hint. This avoids the need to manually prepend provider routing strings to your model requests.
Yes, the Skill requires the liter_llm library for LLM integration. It depends on liter_llm internally to handle client creation and manage the ChatCompletionRequest objects sent to various language models.
Model prefix routing works by parsing the model string in your chat request to identify the target provider. The Skill then uses this prefix to route the interaction to the correct language learning model API.
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