List available large language models and send chat completion requests from code.
npx skills add https://github.com/dtyq/magic --skill using-llm
Please help me install this Agent Skill. Skill: using-llm Source: https://github.com/dtyq/magic/tree/main/backend/super-magic/agents/skills/using-llm Command: npx skills add https://github.com/dtyq/magic --skill using-llm
List available large language models and send chat completions programmatically, enabling quick model comparison and integration into code snippets.
Run a short code snippet to list available models and perform a basic chat request to validate an LLM integration.
High-intent search queries and answers about installing and using this skill.
To call and compare large language models in Python, use the `create_openai_sync_client` function from the SDK to programmatically send OpenAI-like chat requests, execute snippets, and compare responses.
You can list available LLMs for model benchmarking by executing a short Python code snippet through the SDK. This allows you to discover supported models and validate LLM integrations before running automated batch inference scripts.
Yes, you can send vision-enabled prompts to LLMs using the Python SDK. By creating an OpenAI sync client, you can execute code snippets that process visual understanding workflows and test multimodal model behavior programmatically.
Yes, this approach supports sending chat completions in an OpenAI-like format from code. You create a sync client to execute model calls, enabling you to test prompts, compare responses, and validate model behavior within your development workflow.
The best way to run batch inference across multiple models in a script is to use the Python SDK to create a sync client and execute model calls via code snippets. This enables programmatic LLM discovery and comparison within automated workflows.
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