Local Llm Router

Routes your coding queries to locally running LLMs like Ollama, LM Studio, or Jan when you're offline or in air-gapped networks. The big idea here is Serena MCP integration, which you call first to get actual semantic code understanding (symbols, references, type info) before hitting the LLM. This cuts down on token waste and makes edits more precise since you're working with symbol-level operations instead of grep-style text matching. The implementation includes service discovery, task classification, and automatic model selection based on query type. Honestly most useful in secure or defense environments where internet-connected AI is off the table.

skills.sh installs: 160
GitHub stars: 296
last updated: Jun 10, 2026

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Install

$ npx -y skills add hoodini/ai-agents-skills --skill local-llm-router --agent claude-code

Installs into .claude/skills of the current project.

Files

Local LLM Router for Air-Gapped Networks

Intelligent routing of AI coding queries to local LLMs with Serena LSP integration for secure, offline-capable development environments.

Prerequisites (CRITICAL)

Before using this skill, ensure:

Serena MCP Server installed and running (PRIMARY TOOL)
At least one local LLM service running (Ollama, LM Studio, Jan, etc.)

# Install Serena (required)
pip install serena
# Or via uvx
uvx --from git+https://github.com/oraios/serena serena start-mcp-server

# Verify local LLM service
curl http://localhost:11434/api/version  # Ollama
curl http://localhost:1234/v1/models     # LM Studio
curl http://localhost:1337/v1/models     # Jan

Quick Start

import httpx
import asyncio
from dataclasses import dataclass
from enum import Enum
from typing import Optional

class TaskCategory(Enum):
    CODING = "coding"
    REASONING = "reasoning"
    ANALYSIS = "analysis"
    DOCUMENTATION = "documentation"

SKILL.md on GitHub