ai-model-router
Intelligent AI model router that automatically switches between two configured models (local for simple tasks, cloud for complex ones). Detects local models (Ollama, LM Studio) automatically, routes based on task complexity and privacy. Use when users ask to "switch model", "use local/cloud model", or mention API keys/passwords (triggers privacy mode). Trigger on: sensitive data detection (forces local), complex tasks like "design architecture" (uses cloud), or configuration requests.
Best use case
ai-model-router is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Intelligent AI model router that automatically switches between two configured models (local for simple tasks, cloud for complex ones). Detects local models (Ollama, LM Studio) automatically, routes based on task complexity and privacy. Use when users ask to "switch model", "use local/cloud model", or mention API keys/passwords (triggers privacy mode). Trigger on: sensitive data detection (forces local), complex tasks like "design architecture" (uses cloud), or configuration requests.
Teams using ai-model-router should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/ai-model-router/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How ai-model-router Compares
| Feature / Agent | ai-model-router | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Intelligent AI model router that automatically switches between two configured models (local for simple tasks, cloud for complex ones). Detects local models (Ollama, LM Studio) automatically, routes based on task complexity and privacy. Use when users ask to "switch model", "use local/cloud model", or mention API keys/passwords (triggers privacy mode). Trigger on: sensitive data detection (forces local), complex tasks like "design architecture" (uses cloud), or configuration requests.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# AI Model Router
Compact, intelligent model routing that just works.
## Quick Start
```bash
# Install
npx clawhub@latest install ai-model-router
# First run - auto-detects your models
python3 skill/core/router.py "What is Python?"
# List available models
python3 skill/core/router.py --list
```
## How It Works
```
Your Request → Analyze → Select Model
↓
Simple? → Primary (fast/cheap)
Complex? → Secondary (capable)
Private? → Primary (forced)
```
## Scoring (from model-router-premium)
| Pattern | Points |
|---------|--------|
| Microservices, architecture | +10 |
| Design, implement, optimize | +5 |
| Explain, analyze, compare | +3 |
| **Syntax, example, "what is"** | **-3** |
**Threshold: 5** (simple vs complex)
## Features
| Feature | Status |
|---------|--------|
| Auto-detect local models | ✓ (Ollama, LM Studio) |
| Cloud model registry | ✓ (7 built-in) |
| Privacy detection | ✓ (API keys, passwords) |
| Context tracking | ✓ (conversations) |
| JSON config | ✓ (optional) |
| CLI interface | ✓ |
| **Core code size** | **~200 lines** |
## CLI
```bash
# Route a task
python3 skill/core/router.py "Design a system"
python3 skill/core/router.py "What is a for loop?"
# Options
--json # JSON output
--force primary # Force primary model
--list # List all models
--status # Show status
```
## Python API
```python
from skill.core.router import RouterCore
router = RouterCore()
result = router.route("Design microservices")
print(result.model_name) # "Claude Opus 4"
print(result.reason) # "complex_task(score=15)"
print(result.confidence) # 0.75
```
## Configuration (Optional)
Create `~/.model-router/models.json`:
```json
{
"primary_model": {"id": "ollama:llama3:8b"},
"secondary_model": {"id": "anthropic:claude-opus-4"},
"models": [...]
}
```
**Without config**: Auto-detects local + uses cloud registry.
## Privacy Protection
Automatically forces primary (local) when sensitive data detected:
- API keys (`sk-...`, `api_key`)
- Passwords (`password`, `passwd`)
- Tokens (`bearer`, `secret`)
- Emails, SSN, credit cards
## Files
- `core/router.py` - Core routing engine (~200 lines)
- `modules/detector.py` - Auto-detection (optional)
- `modules/context.py` - Context tracking (optional)
## Inspired By
- **model-router-premium**: Simple scoring logic, cost-aware routing
- **Model Router v1**: Full feature set, documentation
This version combines:
- The **simplicity** of model-router-premium (~200 lines)
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