ai-cli-switchboard

The ai-cli-switchboard is an innovative automation skill designed to serve as a simple framework for utilizing Claude Code or Codex CLI as the frontend to any cloud or local large language model (LLM) on Apple Silicon. This skill allows users to connect either locally through LiteLLM + MLX or LM Studio, or remotely via platforms like Z.AI, Gemini/Google AI Studio, DeepSeek, or OpenRouter. By providing a seamless interface, it facilitates easier interaction with various LLMs, enhancing productivity in AI-driven projects. One of the key benefits of the ai-cli-switchboard skill is its ability to streamline the process of accessing and managing LLMs. Although the specific time savings are currently unknown, users can expect to significantly reduce the complexity and time associated with switching between different LLMs. This skill is particularly beneficial for developers, product managers, and AI practitioners who are looking to optimize their workflow automation. By simplifying the connection to multiple LLMs, it allows teams to focus on developing and deploying AI solutions rather than getting bogged down by technical challenges. The ai-cli-switchboard is suited for intermediate users who have a foundational understanding of AI technologies and command-line interfaces. It requires about 30 minutes to implement, making it a practical choice for teams looking to enhance their AI capabilities without extensive setup time. This skill fits seamlessly into AI-first workflows, enabling users to quickly adapt to different AI models and leverage their functionalities effectively. Practical use cases for the ai-cli-switchboard include data analysis, natural language processing, and machine learning model testing. For instance, a developer might use this skill to quickly switch between LLMs for testing various algorithms, while a product manager could leverage it to evaluate different AI solutions for product features. By integrating this automation skill into their toolkit, teams can enhance their efficiency and responsiveness in an increasingly AI-driven landscape.