Babysitter smart-routing

Complexity-based task routing with Q-Learning optimization, Agent Booster WASM fast-path, and Mixture-of-Experts model selection.

install

source · Clone the upstream repo

git clone https://github.com/a5c-ai/babysitter

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/methodologies/ruflo/skills/smart-routing" ~/.claude/skills/a5c-ai-babysitter-smart-routing && rm -rf "$T"

manifest: 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 modernization
```
add-types
```
- TypeScript type annotation insertion
```
add-error-handling
```
- Try/catch wrapper insertion
```
async-await
```
- Promise chain to async/await conversion
```
extract-function
```
- Code block extraction to named functions
```
add-jsdoc
```
- Documentation generation

Agents Used

```
agents/optimizer/
```
- Performance and cost optimization
```
agents/architect/
```
- Complex task decomposition

Tool Use

Invoke via babysitter process:

methodologies/ruflo/ruflo-task-routing