name: model-router
description: A comprehensive AI model routing system that automatically selects the optimal model for any task. Set up multiple AI providers (Anthropic, OpenAI, Gemini, Moonshot, Z.ai, GLM) with secure API key storage, then route tasks to the best model based on task type, complexity, and cost optimization. Includes interactive setup wizard, task classification, and cost-effective delegation patterns. Use when you need "use X model for this", "switch model", "optimal model", "which model should I use", or to balance quality vs cost across multiple AI providers.
version: 1.1.0

Model Router

Intelligent AI model routing across multiple providers for optimal cost-performance balance.

Automatically select the best model for any task based on complexity, type, and your preferences. Support for 6 major AI providers with secure API key management and interactive configuration.

🎯 What It Does

• Analyzes tasks and classifies them by type (coding, research, creative, simple, etc.)
• Routes to optimal models from your configured providers
• Optimizes costs by using cheaper models for simple tasks
• Secures API keys with file permissions (600) and isolated storage
• Provides recommendations with confidence scoring and reasoning

🚀 Quick Start

Step 1: Run the Setup Wizard

cd skills/model-router
python3 scripts/setup-wizard.py

The wizard will guide you through:
1. Provider setup - Add your API keys (Anthropic, OpenAI, Gemini, etc.)
2. Task mappings - Choose which model for each task type
3. Preferences - Set cost optimization level

Step 2: Use the Classifier

# Get model recommendation for a task
python3 scripts/classify_task.py "Build a React authentication system"

# Output:
# Recommended Model: claude-sonnet
# Confidence: 85%
# Cost Level: medium
# Reasoning: Matched 2 keywords: build, system

Step 3: Route Tasks with Sessions

# Spawn with recommended model
sessions_spawn --task "Debug this memory leak" --model claude-sonnet

# Use aliases for quick access
sessions_spawn --task "What's the weather?" --model haiku

📊 Supported Providers

🎛️ Task Type Mappings

Default routing (customizable via wizard):

💰 Cost Optimization

Aggressive Mode

Always uses the cheapest capable model:
- Simple → glm-4.5-air (~10% cost)
- Coding → claude-haiku-4-5 (~25% cost)
- Research → claude-sonnet-4-5 (~50% cost)

Savings: 50-90% compared to always using premium models

Balanced Mode (Default)

Considers cost vs quality:
- Simple tasks → Cheap models
- Critical tasks → Premium models
- Automatic escalation if cheap model fails

Quality Mode

Always uses the best model regardless of cost

🔒 Security

API Key Storage

~/.model-router/
├── config.json       # Model mappings (chmod 600)
└── .api-keys         # API keys (chmod 600)

Features:
- File permissions restricted to owner (600)
- Isolated from version control
- Encrypted at rest (via OS filesystem encryption)
- Never logged or printed

Best Practices

1. Never commit .api-keys to version control
2. Use environment variables for production deployments
3. Rotate keys regularly via the wizard
4. Audit access with ls -la ~/.model-router/

📖 Usage Examples

Example 1: Cost-Optimized Workflow

# Classify task first
python3 scripts/classify_task.py "Extract prices from this CSV"

# Result: simple task → use glm-4.5-air
sessions_spawn --task "Extract prices" --model glm-4.5-air

# Then analyze with better model if needed
sessions_spawn --task "Analyze price trends" --model claude-sonnet

Example 2: Progressive Escalation

# Try cheap model first (60s timeout)
sessions_spawn --task "Fix this bug" --model glm-4.5-air --runTimeoutSeconds 60

# If fails, escalate to premium
sessions_spawn --task "Fix complex architecture bug" --model claude-opus

Example 3: Parallel Processing

# Batch simple tasks in parallel with cheap model
sessions_spawn --task "Summarize doc A" --model glm-4.5-air &
sessions_spawn --task "Summarize doc B" --model glm-4.5-air &
sessions_spawn --task "Summarize doc C" --model glm-4.5-air &
wait

Example 4: Multimodal with Gemini

# Vision task with 2M token context
sessions_spawn --task "Analyze these 100 images" --model gemini-1.5-pro

🛠️ Configuration Files

~/.model-router/config.json

{
  "version": "1.1.0",
  "providers": {
    "anthropic": {
      "configured": true,
      "models": ["claude-opus-4-5", "claude-sonnet-4-5", "claude-haiku-4-5"]
    },
    "openai": {
      "configured": true,
      "models": ["gpt-4o", "gpt-4o-mini", "o1-mini", "o1-preview"]
    }
  },
  "task_mappings": {
    "simple": "glm-4.5-air",
    "coding": "claude-sonnet-4-5",
    "research": "claude-sonnet-4-5",
    "creative": "claude-opus-4-5"
  },
  "preferences": {
    "cost_optimization": "balanced",
    "default_provider": "anthropic"
  }
}

~/.model-router/.api-keys

# Generated by setup wizard - DO NOT edit manually
ANTHROPIC_API_KEY=sk-ant-...
OPENAI_API_KEY=sk-proj-...
GEMINI_API_KEY=AIza...

🔄 Version 1.1 Changes

New Features

• ✅ Interactive setup wizard for guided configuration
• ✅ Secure API key storage with file permissions
• ✅ Task-to-model mapping customization
• ✅ Multi-provider support (6 providers)
• ✅ Cost optimization levels (aggressive/balanced/quality)

Improvements

• ✅ Better task classification with confidence scores
• ✅ Provider-specific model recommendations
• ✅ Enhanced security with isolated storage
• ✅ Comprehensive documentation

Migration from 1.0

Run the setup wizard to reconfigure:

python3 scripts/setup-wizard.py

📚 Command Reference

Setup Wizard

python3 scripts/setup-wizard.py

Interactive configuration of providers, mappings, and preferences.

Task Classifier

python3 scripts/classify_task.py "your task description"
python3 scripts/classify_task.py "your task" --format json

Get model recommendation with reasoning.

List Models

python3 scripts/setup-wizard.py --list

Show all available models and their status.

🤝 Integration with Other Skills

⚡ Performance Tips

1. Start simple - Try cheap models first
2. Batch tasks - Combine multiple simple tasks
3. Use cleanup - Delete sessions after one-off tasks
4. Set timeouts - Prevent runaway sub-agents
5. Monitor usage - Track costs per provider

🐛 Troubleshooting

"No suitable model found"

• Run setup wizard to configure providers
• Check API keys are valid
• Verify permissions on .api-keys file

"Module not found"

pip3 install -r requirements.txt  # if needed

Wrong model selected

1. Customize task mappings via wizard
2. Use explicit model in sessions_spawn --model
3. Adjust cost optimization preference

📖 Additional Resources

• Provider Docs:
• Anthropic
• OpenAI
• Gemini
• Moonshot
• Z.ai

• GLM

• Setup: Run python3 scripts/setup-wizard.py

• Support: Check references/ folder for detailed guides