ReddRadar
Agent skill
smart-routing
Complexity-based task routing with Q-Learning optimization, Agent Booster WASM fast-path, and Mixture-of-Experts model selection.
Install this agent skill to your Project
npx add-skill https://github.com/a5c-ai/babysitter/tree/main/library/methodologies/ruflo/skills/smart-routing
SKILL.md
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
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