Configure a modular RAG MCP project from a clean clone.
npx skills add https://github.com/ljk486/Rag-agent --skill setup-ljk486
Please help me install this Agent Skill. Skill: setup Source: https://github.com/ljk486/Rag-agent/tree/main/MODULAR-RAG-MCP-SERVER-clean-start/.github/skills/setup Command: npx skills add https://github.com/ljk486/Rag-agent --skill setup-ljk486
This Skill removes the friction of first-time project setup for a modular RAG MCP server, helping users move from a fresh clone to a working, configured environment without manual trial and error.
Use the setup skill to configure the project from a clean clone, generate the settings file, install dependencies, and launch the dashboard.
High-intent search queries and answers about installing and using this skill.
To configure a modular RAG MCP project, use an interactive setup flow to select LLM, embedding, vision, and rerank providers. The process generates a validated settings file, installs dependencies, validates startup, and launches the dashboard.
Yes, you can auto-scaffold unimplemented OpenAI-compatible providers like Qwen or Gemini. The setup process creates starter implementations for these providers across LLM, embedding, and vision workflows.
The best way to switch LLM and embedding providers in an MCP server is through interactive provider selection. This rebuilds your validated settings file with new credentials, endpoints, model names, and embedding dimensions.
If your RAG dashboard launch fails, the setup process performs startup validation and retry-based diagnosis. It checks the generated configuration, validates dependencies, and attempts recovery to identify the failure.
No, you do not need to manually install dependencies before generating RAG configuration files. The setup process handles dependency installation automatically alongside config generation and dashboard launch.
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