local-llm-router

Route AI coding queries to local LLMs in air-gapped environments via Serena MCP.

262|59|Updated Jan 7, 2026

Search Tags:#security #routing #offline #coding #Serena #local-llm #air-gapped

Ready to run

npx skills add https://github.com/hoodini/ai-agents-skills --skill local-llm-router

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: local-llm-router
Source: https://github.com/hoodini/ai-agents-skills/tree/main/skills/local-llm-router
Command: npx skills add https://github.com/hoodini/ai-agents-skills --skill local-llm-router

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill routes AI coding queries to local LLM services in air-gapped environments, enabling offline development with secure, private model routing based on Serena MCP.

Core Features & Use Cases

Local LLM routing: Detects available local models (Ollama, LM Studio, Jan, OpenWebUI) and forwards queries to the most suitable one.
Serena MCP integration: Uses Serena for semantic code understanding to improve symbol-level awareness and precise changes.
Use Case: In a secure IDE workflow, route a coding task to the best local model and receive code suggestions without leaving the air gap.

Quick Start

Install Serena MCP, ensure at least one local LLM service is running (Ollama, LM Studio, Jan), verify health endpoints, and start routing queries via the provided handler.

Frequently Asked Questions about local-llm-router

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I route coding queries to local LLMs in an air-gapped environment?▼

Route coding queries to local LLMs in an air-gapped environment by detecting available local model services and forwarding requests to the most suitable endpoint. This ensures secure, private model routing without leaving the offline network boundary.

Does Serena MCP work with offline local LLMs for semantic code understanding?▼

Yes, Serena MCP integrates with offline local LLMs to provide semantic code understanding. It improves symbol-level awareness and enables precise code changes by applying context from local models like Ollama, LM Studio, and Jan.

What local LLM services can I use for offline AI coding assistance?▼

You can use Ollama, LM Studio, Jan, or OpenWebUI as local LLM services for offline AI coding assistance. The routing mechanism detects available models across these platforms and forwards queries to the best matching endpoint.

What do I need to set up before routing queries to local models?▼

Before routing queries to local models, you must install Serena MCP and ensure at least one local LLM service is running. You also need to verify endpoint health, model, and chat APIs to establish a deterministic quick-start workflow.

Why use local LLM routing instead of cloud-based models for coding?▼

Use local LLM routing instead of cloud-based models to maintain strict air-gapped security and data privacy. It enables secure, offline development workflows by leveraging local agents and semantic code understanding without transmitting data externally.