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.

[{"step":"Install and configure the ai-cli-switchboard framework.","action":"Run `pip install ai-cli-switchboard` and follow the setup guide for your local LLM provider (LiteLLM/MLX/LM Studio) or remote endpoint (Z.AI, etc.). Use the `--model` flag to specify the target LLM.","tip":"For Apple Silicon, use `litellm --model mlx-community/[MODEL_NAME]` for optimal performance. Test connectivity with `claude --model http://localhost:4000 --test`."},{"step":"Define the task and select the integration approach.","action":"Use the prompt template to craft a task-specific query. Decide whether to use a local LLM (for speed/privacy) or remote (for broader context).","tip":"For Sortd-related tasks, prioritize remote LLMs (e.g., Z.AI) if the documentation is complex or requires up-to-date external context."},{"step":"Execute the task and capture the output.","action":"Run the command in your terminal (e.g., `claude --model zai--mistralai/mistral-7b-instruct-v0.2 \"[TASK]\"`). Save the output to a file for review.","tip":"Use `--max-tokens 4096` for detailed responses. For large documents (e.g., Sortd’s API docs), split the task into chunks and process sequentially."},{"step":"Validate and refine the output.","action":"Cross-check the LLM’s recommendations against Sortd’s official documentation or your team’s workflows. Adjust parameters (e.g., `--temperature 0.7`) for more creative or precise outputs.","tip":"For sales workflows, use Sortd’s shared inbox features as a reference. Example: `claude --model local-llm \"How would you set up a shared inbox for a sales team using Sortd?\"`"},{"step":"Automate repetitive tasks.","action":"Use the ai-cli-switchboard to create scripts for recurring tasks (e.g., weekly lead follow-up reports). Save the script as `sortd-sales-report.sh` and run it via cron.","tip":"Example script:\n```bash\n#!/bin/bash\nclaude --model zai--mistralai/mistral-7b-instruct-v0.2 \\\n \"Generate a weekly report for Sortd sales board [BOARD_ID]. Include metrics: total leads, leads closed, average response time.\"\n```"}]