| name | smart-routing |
| description | Complexity-based task routing with Q-Learning optimization, Agent Booster WASM fast-path, and Mixture-of-Experts model selection. |
| allowed-tools | Read, Write, Edit, Bash, Grep, Glob, WebFetch, WebSearch, Agent, AskUserQuestion |
Smart Routing
Overview
Intelligent task routing using Q-Learning to select optimal execution paths. Simple tasks route to Agent Booster (WASM, <1ms, $0), medium tasks to efficient models, and complex tasks to Opus + multi-agent swarms.
When to Use
- Optimizing cost vs. quality tradeoffs for diverse task types
- When tasks range from simple transforms to complex multi-file changes
- Reducing latency for common code transformations
- Learning from routing history to improve future decisions
Routing Tiers
| Tier | Target | Latency | Cost |
|---|
| Agent Booster | Simple transforms (var-to-const, add-types) | <1ms | $0 |
| Medium | Standard coding tasks | ~500ms | Low |
| Complex | Multi-agent swarm coordination | 2-5s | Higher |
Agent Booster Transforms
var-to-const - Variable declaration modernizationadd-types - TypeScript type annotation insertionadd-error-handling - Try/catch wrapper insertionasync-await - Promise chain to async/await conversionextract-function - Code block extraction to named functionsadd-jsdoc - Documentation generation
Agents Used
agents/optimizer/ - Performance and cost optimizationagents/architect/ - Complex task decomposition
Tool Use
Invoke via babysitter process: methodologies/ruflo/ruflo-task-routing
