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